<p><a href="https://bitcoinmagazine.com/technical/compute-maximalism-the-symbiosis-between-bitcoin-mining-and-ai" rel="noopener noreferrer" target="_blank">Initially published at Bitcoin Magazine</a></p><p><br></p><p>The story of human progress can be simplified into the story of increasing energy utilization. We harness energy to create order, both in terms of biology and society. Energy surpluses allow for every form of wealth creation, which in turn produce new technologies to effectively harness yet more energy. This truth has inspired famous concepts such as the Kardashev scale, which measures civilizations by their ability to harness energy resources towards useful ends.</p><p><br></p><p>Compute is a natural continuation of this endeavor. Modern digital technology transforms ever greater quantities of electricity into advanced value-creating processes. The most recent surge in demand for compute has come from two technologies in particular: bitcoin mining and, more recently, high performance compute (“HPC,”) in particular the use of Graphics Processing Units (“GPUs”) for artificial intelligence. The meteoric rise in energy consumption by these technologies have raised many questions: What impact will these power-hungry technologies have on our energy systems? Given their mutually voracious use of energy, what interplay will they have with each other? What do these developments mean for humanity?</p><p><br></p><p>We explore the essential characteristics of these respective technologies, and how they offer alternative markets for excess power that can in fact improve the efficiency of energy systems. Based on this exploration, we also argue that bitcoin mining and HPC are complementary rather than competitive. As we’ll see, their respective trade offs offer a symbiotic ability to maximize the value created from energy resources, which in turn benefits society as a whole.</p><p><br></p><p>In short, we argue for compute maximalism.</p><p><br></p><h2>Energy</h2><p><br></p><p>Modern technology depends on energy converted from a wide array of sources into electricity, and this comes with certain challenges and tradeoffs. The primary of which is limited portability.</p><p><br></p><p>This is due to several simple realities. Electricity requires a grid, essentially a massive series of circuits which transports energy in real time. The grid must remain in balance, meaning the generation must roughly equal demand at any point in time.</p><p><br></p><p>This is difficult for two reasons:</p><p><br></p><p>First, energy resources are not always conveniently distributed, have long lead times to develop, and have varying degrees of dispatchability.</p><p><br></p><p>Second, transmission as well as storage are both expensive, have similarly long lead times, and experience inherent inefficiencies. An estimated <a href="https://demo.openeclass.org/modules/document/file.php/DEMO-A1153/%CE%A3%CE%B7%CE%BC%CE%B5%CE%B9%CF%8E%CF%83%CE%B5%CE%B9%CF%82%20%CF%80%CF%81%CE%BF%CF%82%20%CE%BC%CE%B5%CE%BB%CE%AD%CF%84%CE%B7/EFFICIENT%20ELECTRICAL%20ENERGY%20TRANSMISSION%20AND%20DISTRIBUTION%20.pdf" rel="noopener noreferrer" target="_blank">8-15%</a> of electricity is lost to transmission and distribution losses by the time it reaches local consumers, and this figure is even higher for long term battery storage.</p><p><br></p><p>The result is that it will always be cheaper and more efficient to consume generated electricity immediately at the source, than to transport it over time or space. As such, the most efficient solution isn’t to more widely and inefficiently transport electricity to where it can be used, but to move use cases to the electricity. Compute is an ideal use case for such excess electricity because it is power dense, largely portable, and scalable; we are yet to find a limit on our demand for compute.</p><p>Meanwhile “meatspace” constraints are strong limiting factors for legacy forms of energy sinks like aluminum smelting and manufacturing.</p><p><br></p><p>Bitcoin mining in particular has emerged as such an ideal use case for local surplus power, providing a dispatchable and revenue-generating load to balance the grid. More recently the demand for High Performance Compute, in particular GPUs, is also having unignorable impacts on the utilization of energy as well. Many are expecting these two technologies to compete over the same energy resources, but as we explore the characteristics of each, the potential symbiosis will become self-evident.</p><p><br></p><h2>Bitcoin Mining</h2><p><br></p><p>Bitcoin mining can be thought of as a permissionless energy sink. Bitcoin’s proof-of-work consensus mechanism amounts to proof of energy-intensive computation. Miners must perform this energy-intensive computation to create new blocks of transactions, thereby earning bitcoin as a reward. It is this proof-of-work that provides global settlement assurances in a decentralized and permissionless way.</p><p><br></p><p>In practice, this looks like millions of computers (these days, application specific integrated circuits or “ASICs”) running in bare bones data centers around the world. One of the beautiful things about bitcoin mining is its permissionless nature; anyone anywhere in the world can plug in an ASIC. In effect, Bitcoin allows miners around the world to participate in a global energy market; whoever has the lowest cost of power has the highest margin.</p><p><br></p><p>This global decentralized network is part of the reason why Bitcoin’s adoption has steadily continued, as people seek a new monetary and financial system that is active 24/7, lacks a single point of failure, and sidesteps the perverse incentives of politically captured central bank monopolies.</p><p><br></p><p>Bitcoin mining is distinguished by the following characteristics relative to GPU/HPC infrastructure:</p><p><br></p><ul><li>No customers</li><li>No customer acquisition</li><li>No support</li><li>High interruptibility</li><li>Low operational complexity</li><li>Low connectivity requirements (Less than 100MB/s)</li><li>Low margin (generally)</li></ul><p><br></p><h2>HPC</h2><p><br></p><p>Data center GPUs are the latest form of HPC, the demand for which has exploded in the previous 2 years due to quickly escalating interest in AI/ML breakthroughs which rely on it. These technologies have unlocked whole new categories of digital operations and functions which were not previously possible, with the resulting use cases only just beginning to be explored. The sudden explosion in interest in these technologies has quickly made NVIDIA, the leading manufacturer of the underlying GPUs, the most valuable company in the world.</p><p><br></p><p>Initially this sudden spike in demand created an intense bottleneck in the production of sufficient units of GPU itself. This however was temporary and over time continues to be alleviated by increased production, with focus quickly switching to a new bottleneck: Data center rack space with cheap power. The result has been an explosion in new data center build outs, wherever a large amount of steady power can be sourced. This has brought GPU infrastructure into competition with Bitcoin mining in many areas with excess local power.</p><p><br></p><p>Relative to Bitcoin mining, GPU/HPC has these is distinguished by these characteristics:</p><p><br></p><ul><li>Customers</li><li>Customer acquisition</li><li>Customer support</li><li>Low interruptibility</li><li>High operational complexity</li><li>High connectivity requirements (10 - 100GBs)</li><li>High margin (generally)</li></ul><p><br></p><h2>Complimentary Competition</h2><p><br></p><p>The demand for both Bitcoin and AI/ML technologies has taken off in the last decade, a testament to their utility to society. This demand has led to the proliferation of their respective compute resources.</p><p><br></p><p>To reduce operating costs, both markets seek excess power to utilize as it tends to be cheaper. This naturally resolves some of the grid inefficiencies discussed above, but it does mean that data center builders and operators will find themselves asking which form of compute to support and invest in for the same amount of available power.</p><p><br></p><p>Both forms of compute are energy intensive and relatively location-agnostic (barring legal or jurisdictional considerations beyond the scope of this paper) bringing them into seeming competition, but they can in fact be highly complementary tools for maximizing utilization and profit from such excess or stranded electricity.</p><p><br></p><p>GPU workloads have higher operational complexity, and low interruptibility, as well as higher upfront capital investment. That makes it a poor choice for taking advantage of transient surpluses of power, such as the peak window of energy production by solar panels for instance. Unlike bitcoin mining, GPUs have <em>customers</em>, who are typically sensitive to issues such as uptime and availability. There are exceptions, such as spot instances and frameworks which can failover from such instances, but generally speaking due to the existence of a customer the interruptibility tolerance of GPU infrastructure will never match that of bitcoin mining. Coupled with the higher capital costs and complexity, in these situations we can expect bitcoin mining to continue to grow and dominate as a highly flexible, dispatchable load to the grid.</p><p><br></p><p>Consistent excesses in power on the other hand, such as a largely fixed delta between the base generation capacity of hydropower or nuclear sites and their surrounding consumption, are ideal opportunities for GPU infrastructure to close the gap and establish new baseline consumption and equilibrium. These situations favor the low interruptibility of GPU infrastructure, and justify the added expenditure and operational complexity in order to secure substantially higher revenues. So long as the supporting bandwidth is available to facilitate GPU workloads (at least 10GB/s, ideally 100GB/s), these sites will always provide more profit opportunity than if allocated exclusively for bitcoin mining.</p><p><br></p><h2>Hybrid data center Strategies</h2><p><br></p><p>There are also strategies which can utilize both technologies in tandem to maximize revenue and return on investment.</p><p><br></p><p>First, bitcoin mining could be used as an initial load for energy resources before the site is suitable for high performance compute. Examples include: (1) using semi-portable modular bitcoin mining data centers to monetize power while the remaining infrastructure for an HPC data center (redundant power/internet lines, buildings, backup energy systems, etc.) is built; or (2) pioneering stranded energy resources with bitcoin mining, some of which may eventually be used for HPC. In fact, <a href="https://bitcoinmagazine.com/tags/core-scientific" rel="noopener noreferrer" target="_blank">Core Scientific</a>’s recently announced deal with CoreWeave could be viewed as an example of this occurring in the wild, as bitcoin mining led to the development of a large substation and data center shell that would eventually be used for HPC.</p><p><br></p><p>A second, more advanced strategy is to co-mingle HPC and Bitcoin mining workloads in tandem, using Bitcoin mining as a counter weight to balance fluctuations in HPC workload power draws. While HPC loads require reliable power, “inferencing workloads” which host production AI/ML models can fluctuate based on levels of real time use by users, leading to typical cycles of high activity and power consumption and low activity and lower power consumption. To date, the value for such HPC has substantially outweighed any inefficiencies from fluctuating power use, but the highly flexible and interruptible nature of Bitcoin mining can be used to provide stable power draw and in turn lower effective power rates, in addition to providing additional revenue for the data center overall. Some are describing this strategy as a “mullet data center,” with AI in the front and bitcoin in the back. While it is still early, this approach promises to utilize the best of both HPC and bitcoin mining to offer the most value maximized data center deployments possible with current technology.</p><p><br></p><h2>Industry Implications</h2><p><br></p><p>Until recently, the data center industry has been dominated by colocation providers. These providers build the facilities used to host industrial servers, and lease out space, power, connectivity, and sometimes the servers themselves to tenants. Traditionally, the majority of these tenants have been large enterprise and hyperscale cloud providers. In many cases these hyperscale and enterprise tenants have also built their own data centers to support their own growth.</p><p><br></p><p>Since roughly 2017 bitcoin mining has truly entered the picture at an industrial level, with entire data center complexes being built solely to support Bitcoin mining in areas with extreme deltas in produced and consumed electricity. Now in 2023 and 2024 we’ve seen shifts in the market even more notable and disruptive. With the explosion in demand for GPU infrastructure, many former colocation focused data centers have ventured into buying and hosting this GPU infrastructure themselves. Meanwhile hyperscalers are moving behind-the-meter to co-locate with large baseload power plants, seeking cheap reliable power for the new surge in HPC demand. This is particularly notable as intermittent renewables have been the most popular form of generation in recent years, primarily due to government subsidies.</p><p><br></p><p>We anticipate the following:</p><p><br></p><p>1. Continued increase in energy demand for both forms of compute.</p><p><br></p><p>2. New data center construction as the next bottleneck of expanding HPC footprints, with large swaths of bitcoin mining facilities being repurposed for higher margin use cases.</p><p><br></p><p>3. Mining hardware will relocate to the fringes, seeking remote locations and variable inefficiencies that HPC workloads are ill-suited to monetize.</p><p><br></p><p>4. Co-mingling of both bitcoin mining and HPC in “mullet data centers” will leverage the high revenue potential of HPC and the flexible nature of bitcoin mining to effectively balance power draw and local grids, while outcompeting traditional data center strategies.</p><p><br></p><h2>Conclusion</h2><p><br></p><p>When new power-hungry technologies emerge, there is often concern about their energy utilization and its externalities. Bitcoin mining and HPC are no exceptions with politicians and arm-chair technologists alike crying for their mitigation or control. But such voracious technologies represent the natural trend of human progress. In addition to the self-evident utility provided by the Bitcoin settlement network and AI/ML workloads, we can demonstrate that they can be deployed in ways that efficiently maximize new and existing energy resources to useful economic ends.</p>

Drew Armstrong

Discover how Bitcoin mining and GPUs drive energy efficiency and progress. Learn about hybrid data centers and strategies for compute maximalism.

Compute Maximalism: The Synergy of Bitcoin Mining and AI | Hydra Host

James Walsh

<p>Running a data center is a relentless, round-the-clock commitment, as James Walsh, founder of Sytronix and CTO of Daylight Compute, knows all too well. <strong>For two grueling years, James worked seven days a week, overseeing every detail of Daylight Compute's data center operations.</strong> Whether it was provisioning GPUs or troubleshooting technical hiccups, his dedication kept customers happy but left no time for himself.&nbsp;</p><p>Then came Brokkr by Hydra Host a game-changer that transformed not just his workflow but his entire approach to managing server rentals.&nbsp;</p><p>&nbsp;</p><p><strong>The Struggle: Complexity at the Cost of Balance</strong>&nbsp;</p><p>Managing a bare metal data center is no small feat. While platforms like Vast, Fluidstack, and Cudo Compute offered partial solutions, none fully addressed the intricate needs of Daylight Compute. Every server required hands-on management, keeping James perpetually tethered to data center operations.&nbsp;</p><p>This constant demand on his time not only drained his energy but also stole his focus from what he truly needed to send time on, the research and development of server technology.&nbsp;</p><p>&nbsp;</p><p><strong>The Turning Point: Discovering Brokkr</strong>&nbsp;</p><p>Determined to regain control of his time, James began searching for a smarter, more streamlined solution. Enter Brokkr: a comprehensive platform designed to optimize GPU resources, automate server management, and connect data center operators with a global customer base.&nbsp;</p><p>Brokkr wasn’t just another marketplace it was the system James had been actively searching for.&nbsp;</p><p>&nbsp;</p><p><strong>The Solution: Simplicity Meets Sophistication</strong>&nbsp;</p><p>With Brokkr, James turned Daylight Compute into a finely tuned operation. The platform delivered:&nbsp;</p><ul><li><strong>Automated Efficiency</strong>: Features like auto-reboot and reprovisioning minimized downtime and manual interventions.&nbsp;</li><li><strong>Seamless Monetization</strong>: Brokkr linked James’s GPUs to a global network, maximizing utilization and revenue.&nbsp;</li><li><strong>Centralized Control</strong>: A single dashboard gave James the power to manage everything from anywhere.&nbsp;</li><li><strong>Scalable Stability</strong>: Brokkr’s infrastructure adapted to customer needs, enabling growth without overextension.&nbsp;</li></ul><p>&nbsp;</p><p><strong>The Results: Freedom to Innovate and Thrive</strong>&nbsp;</p><p>For the first time in over 2 years, James could step away from the grind and even take a vacation. <strong>"With Brokkr, you don’t work yourself to death,"</strong> he shared.&nbsp;</p><p>Freed from the constant demands of data center management, James redirected his energy to his passion for engineering high-performance servers. Sytronix, now specializing in CPU and GPU servers for AI, CGI, and HPC, has become a leader in the industry.&nbsp;</p><p>James’s expertise in tuning AMD EPYC CPUs and NVIDIA GPUs like the RTX 4090 has shattered CGI rendering benchmark records. His precision engineering delivers unprecedented speed and efficiency, making <a href="https://www.sytronix.co.uk/" rel="noopener noreferrer" target="_blank">Sytronix</a> the go-to for record-breaking server performance.&nbsp;</p><p>&nbsp;</p><p><strong>The Brokkr Advantage: A Platform Built for Success</strong>&nbsp;</p><p><a href="https://docs.hydrahost.com/docs/brokkr-overview" rel="noopener noreferrer" target="_blank">Brokkr</a> isn’t just a tool it’s a transformative ecosystem for data center operators. Key features include:&nbsp;</p><ul><li><strong>Reliability Through Automation</strong>: Auto-reboots and reprovisioning minimize human error and keep systems running smoothly.&nbsp;</li><li><strong>Scalability Without Boundaries</strong>: A global network ensures compliance, low-latency solutions, and seamless expansion.&nbsp;</li><li><strong>Effortless Monetization</strong>: GPU resources are easily listed and connected to customers worldwide, maximizing earnings.&nbsp;</li><li><strong>Enhanced Security</strong>: Robust encryption and access controls safeguard operations.&nbsp;</li><li><strong>Peak Efficiency</strong>: Dynamic dashboards optimize resource allocation and energy usage, aligning with sustainability goals.&nbsp;</li></ul><p>&nbsp;</p><p><strong>A New Era for Data Center Management</strong>&nbsp;</p><p>James Walsh’s journey with Brokkr is proof of what’s possible when innovation meets execution. By removing the burdens of day-to-day management, Brokkr empowered James to focus on growth, creativity, and setting new performance standards in the data center industry.&nbsp;</p><p>Are you ready to optimize your operations and reclaim your time? Learn more about Brokkr and how it can transform your data center. Visit <a href="https://hydrahost.com/datacenters/" rel="noopener noreferrer" target="_blank">Hydra Host</a> or reach out to <a href="mailto:sales@hydrahost.com" rel="noopener noreferrer" target="_blank">sales@hydrahost.com</a> and start your journey today.&nbsp;</p><p>&nbsp;</p><p><strong>With Brokkr, James Walsh didn’t just optimize his business he revolutionized it. Now, it’s your turn.</strong>&nbsp;</p><p>&nbsp;</p><p><br></p><p>&nbsp;</p>

Running a data center is a relentless, round-the-clock commitment, as James Walsh, founder of Sytronix and CTO of Daylight Compute, knows all too well. For two grueling years, James worked seven days a week, overseeing every detail of Daylight Compute's data center operations. Whether it was provisioning GPUs or troubleshooting technical hiccups, his dedication kept customers happy but left no time for himself. 

From Overworked to Optimized: How James Walsh Revolutionized Server Rentals with Brokkr

<h3><strong>Interconnect Technologies: An Overview</strong></h3><p><br></p><p>I'll start by laying out the technology used in AI, HPC and other server workloads to give you an understanding of how these technologies are used and later in this blog I will breakdown how this tech affects your bottom line. Explaining why old tech should not be used and why some new tech can be risky if you haven't enough knowledge to operate these technologies.</p><p><br></p><h4><strong>1. PCIe (Peripheral Component Interconnect Express)</strong></h4><p>PCIe is the most widely used interconnect for CPUs, GPUs, and peripherals in servers. Generational improvements in PCIe versions 3.0, 4.0, and 5.0 have brought higher bandwidth and lower latency:</p><ul><li><strong>PCIe 3.0</strong>: Suitable for none demanding workloads which do not require high performance. Release date 2010 very dated.</li><li><strong>PCIe 4.0</strong>: Addressing mid-level performance requirements. Release date 2017 still in wide use but now become dated technology.</li><li><strong>PCIe 5.0</strong>: Enables better communication for high-performance systems like AI/ML training or HPC. Release date 2019, now in wide spread use with modern up to date servers.</li></ul><p><br></p><p>PCIe 6 standards have already been set in 2022, but are not in widespread use yet and supported by very few devices - no CPU's currently sold by Intel or AMD to the open market. The same is also true of PCIe 7, which won't be released until 2025 and so far to date I have never seen or heard of a PCIe 7 device in the field. Most next gen GPU's and network adapters will support PCIe 5.</p><p><br></p><h4><strong>2. NVidia\Mellanox Interconnects Networks and Fabrics.</strong></h4><p>InfiniBand is a high-speed networking interconnect designed for low-latency, high-throughput communication across clustered systems. With speeds up to <strong>800 Gbps (HDR)</strong>&nbsp;and <strong>direct RDMA support</strong>, it is widely used in HPC and enterprise environments. Features include:</p><ul><li><strong>Scalability</strong>: Connects thousands of nodes in a supercomputing environment.</li><li><strong>GPUDirect RDMA</strong>: Facilitates GPU-to-GPU communication across nodes without CPU intervention.</li><li><strong>Seamless CPU-GPU Communication</strong>: Enables large-scale hybrid compute clusters.</li><li><strong>NVLink and NVSwitch </strong></li></ul><p><br></p><p>Developed by NVIDIA, <strong>NVLink</strong>&nbsp;is a GPU-to-GPU interconnect that provides ultra-fast communication with bandwidths of up to <strong>1800 GB/s</strong>&nbsp;in its latest version. It creates unified memory pools for AI/ML workloads and is essential in systems like NVIDIA DGX for:</p><ul><li><strong>Single-Node Performance</strong>: Enhances GPU communication within one server.</li><li><strong>Large GPU Clusters</strong>: Uses <strong>NVSwitch</strong>&nbsp;to connect multiple GPUs in a full-mesh topology for large-scale AI/ML or HPC tasks.</li><li><a href="https://www.nvidia.com/en-gb/data-center/nvlink/" rel="noopener noreferrer" target="_blank"><u>https://www.nvidia.com/en-gb/data-center/nvlink/</u></a></li></ul><p><br></p><h4><strong>4. XGMI (AMD Infinity Fabric)</strong></h4><p>XGMI is AMD’s proprietary interconnect for efficient CPU-to-GPU and GPU-to-GPU communication. Features include:</p><ul><li><strong>Tight CPU-GPU Integration</strong>: Leverages AMD EPYC CPUs with Instinct GPUs for AI and HPC workloads.</li><li><strong>GPU to GPU communication:</strong> 896GB\s Bidirectional</li><li><strong>Scalable Clusters</strong>: Facilitates distributed workloads using Infinity Fabric.</li><li><a href="https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/data-sheets/amd-instinct-mi300x-platform-data-sheet.pdf" rel="noopener noreferrer" target="_blank"><u>https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/data-sheets/amd-instinct-mi300x-platform-data-sheet.pdf</u></a></li></ul><p><br></p><h4><strong>5. RDMA and RoCE</strong></h4><p><strong>Remote Direct Memory Access (RDMA)</strong>&nbsp;allows memory access across nodes without CPU intervention, significantly reducing latency. RDMA can be implemented over InfiniBand or Ethernet-based technologies like <strong>RoCE (RDMA over Converged Ethernet)</strong>:</p><p><br></p><ul><li><strong>RoCE</strong>: Brings RDMA's low-latency benefits to Ethernet networks, enabling compatibility with existing data center infrastructure.</li><li><strong>InfiniBand RDMA</strong>: Offers the lowest latency and highest throughput, ideal for HPC.</li><li><a href="https://www.juniper.net/content/dam/www/assets/white-papers/us/en/2024/juniper-artificial-intelligence-data-center-comparison-of-infiniband-and-rdma-over-converged-ethernet.pdf" rel="noopener noreferrer" target="_blank"><u>https://www.juniper.net/content/dam/www/assets/white-papers/us/en/2024/juniper-artificial-intelligence-data-center-comparison-of-infiniband-and-rdma-over-converged-ethernet.pdf</u></a></li></ul><p><br></p><p><br></p><h3><strong>Performance Benefits of Advanced Interconnects</strong></h3><p><br></p><h4><strong>1. Clustered GPU and CPU Systems For AI and HPC</strong></h4><p>In clustered systems, interconnect technologies like InfiniBand, NVLink and RDMA are critical for ensuring efficient communication between compute elements:</p><ul><li><strong>GPU Clusters</strong>: NVlink and xGMI create unified memory pools, while InfiniBand or RoCE connects GPUs across nodes with minimal overhead, enabling fast AI/ML training or large-scale simulations.</li><li><strong>Hybrid Clusters</strong>: InfiniBand and xGMI\Infinity Fabric - provide low-latency connections between CPUs and GPUs, optimising workloads that rely on both compute types.</li><li><br></li></ul><h4><strong>2. AI ML Workloads</strong></h4><p>AI and ML workloads benefit significantly from advanced interconnects:</p><ul><li><strong>NVLink</strong>&nbsp;accelerates GPU to GPU communication, reducing training times for AI models.</li><li><strong>InfiniBand RDMA</strong>&nbsp;allows GPUs across nodes to exchange data efficiently enabling distributed AI training.</li><li><strong>RoCE (RDMA Over Converged Ethernet)</strong> used as a Ethernet networking alternative to NVidia\Mellanox's Infiniband.</li><li><br></li></ul><p><strong>3. HPC Workloads</strong></p><p>High-performance computing (HPC) demands high-speed, low-latency networking interconnects:</p><ul><li><strong>InfiniBand</strong>: Essential for large-scale simulations, weather modeling and complex molecular dynamics.</li><li><strong>RDMA</strong>: Eliminates CPU bottlenecks in distributed systems, ensuring smooth data flow across nodes.</li></ul><p><br></p><p><br></p><h3><strong>Profitability of AI Server Rentals: How Interconnects Make a Difference</strong></h3><p><br></p><h4><strong>1. Increased Performance and Utilisation</strong></h4><p>Faster interconnects reduce workload execution times, allowing servers to handle more tasks within a given period. Features like InfiniBand scalability and NVLink’s unified memory pools enable:</p><ul><li><strong>Higher Throughput</strong>: More tasks completed per server.</li><li><strong>Improved Resource Utilisation</strong>: Minimising idle hardware increases revenue efficiency.</li></ul><p><br></p><h4><strong>2. Differentiated Offerings in Data Centre Tiers</strong></h4><p>Advanced interconnects, storage solutions and memory technologies allow service providers to create premium pricing tiers, ensuring they can cater to a wide range of customer needs while maximising revenue opportunities. Here’s a breakdown of the key tiers and their potential for profitability:</p><p><br></p><h4><strong>Lowest Tier: PCIe 3 Systems</strong></h4><p>Servers using <strong>PCIe 3</strong>&nbsp;interconnects are rapidly becoming obsolete. While they can still handle basic workloads, their earnings potential is extremely limited. Achieving ROI is highly unlikely unless:</p><ul><li>The equipment is purchased at a very low price.</li><li>Operating costs, particularly electricity, are minimal (less than $0.10 USD per kWh).</li></ul><p>Most rental platforms avoid offering multi-GPU servers with PCIe 3, even if the GPUs themselves are decent, due to these constraints.</p><p><br></p><h4><strong>Standard Tier: PCIe 4 Systems</strong></h4><p><strong>PCIe 4</strong>-based servers are the current standard for general-purpose workloads and some AI tasks. However, they are gradually being replaced by PCIe 5 systems.</p><ul><li><strong>Profitability</strong>: While still profitable in the short term, their earnings potential is diminishing over time.</li><li><strong>Use Cases</strong>: Best suited for rendering or AI inference workloads that run on single GPUs or multi-GPU setups without clustering.</li></ul><p><strong>Performance Range</strong>: PCIe 4 servers have a broad range of configurations, from entry-level systems with 8-core CPUs to high-performance dual <strong>AMD EPYC</strong>&nbsp;setups with up to 128 cores.</p><p><br></p><h4><strong>Medium to High-Performance Tier: PCIe 5 Systems</strong></h4><p><strong>PCIe 5</strong>-based servers represent the next step in performance for bare-metal machines. These systems:</p><ul><li>Deliver strong earnings when paired with powerful GPUs.</li><li>Are ideal for connecting devices on server nodes.</li></ul><p><strong>Storage Innovations</strong>: Advanced storage solutions, such as <strong>NVMe drives</strong>&nbsp;with PCIe 4 or 5 interfaces and <strong>storage-class memory (SCM)</strong>, are often integrated at this tier to support demanding workloads. These provide:</p><ul><li><strong>Low-latency performance</strong>, critical for real-time processing in AI, HPC or some high-frequency trading applications.</li><li><strong>High throughput</strong>, ensuring that even large datasets can be handled efficiently without bottlenecks.</li></ul><p><br></p><p><strong>OpenZFS File System</strong>:</p><ul><li>OpenZFS is increasingly adopted in high-performance environments at this tier for its advanced features, including <strong>data integrity checks</strong>, <strong>deduplication</strong>, and <strong>efficient snapshots</strong>.</li><li>It allows seamless integration with NVMe and scalable storage pools, making it an attractive choice for maintaining data reliability and performance.</li></ul><p><br></p><p><strong>CXL (Compute Express Link) Interconnects</strong>:</p><ul><li><strong>CXL 2.0 and 3.1</strong>&nbsp;interconnects, built on PCIe 5, allow systems to share memory and expand their capacity seamlessly.</li><li>These technologies enable the pooling of high-speed memory, which can be dynamically allocated across CPUs and GPUs.</li><li>For memory-intensive workloads like AI training, CXL dramatically increases system flexibility and scalability, reducing bottlenecks.</li></ul><p>This tier also serves as the foundation for servers in the higher-performance tiers, which incorporate additional advanced technologies.</p><p><br></p><h4><strong>High-Performance Tier: InfiniBand and RoCE Systems</strong></h4><p>Servers equipped with <strong>InfiniBand</strong>&nbsp;or <strong>RoCE</strong>&nbsp;(RDMA over Converged Ethernet) excel in distributed system setups. They offer:</p><ul><li><strong>Earnings Potential</strong>: Good to very good returns when utilised effectively.</li><li><strong>Requirements</strong>: These systems demand specialised knowledge, infrastructure, and moderate to significant investment.</li></ul><p><strong>Storage Innovations</strong>: Distributed storage systems, such as <strong>Ceph</strong>&nbsp;or <strong>BeeGFS</strong>, are commonly deployed in these configurations. These enable:</p><ul><li><strong>Scalable, high-speed storage</strong>, critical for workloads like machine learning and HPC.</li><li><strong>Data redundancy and fault tolerance</strong>, ensuring system reliability.</li></ul><p><br></p><p><strong>OpenZFS\ZFS File System</strong>:</p><ul><li>OpenZFS excels in this tier due to its scalability and advanced features such as <strong>multi-level caching</strong>&nbsp;(ARC and L2ARC) and <strong>compression</strong>, which enhance performance while optimising storage usage.</li><li>Its ability to handle petabyte-scale datasets makes it ideal for AI, HPC, and large-scale data analytics.</li></ul><p><br></p><p><strong>CXL and Memory Expansion</strong>:</p><ul><li>Systems in this tier benefit significantly from CXL-enabled memory expansion, which allows for larger memory pools that can be shared between nodes.</li><li><strong>Memory pooling and tiering</strong>&nbsp;using CXL reduces overall system costs while enhancing performance for distributed AI and HPC tasks.</li></ul><p><br></p><h4><strong>Premium GPU Clusters: NVLink and xGMI with InfiniBand or RoCE</strong></h4><p>The highest-performance tier involves <strong>NVLink</strong>&nbsp;or <strong>xGMI GPU clusters</strong>, paired with <strong>InfiniBand</strong>&nbsp;or <strong>RoCE</strong>, making them ideal for <strong>AI/ML</strong>&nbsp;or <strong>HPC</strong>&nbsp;applications. These systems:</p><ul><li>Require <strong>large-scale investment</strong>&nbsp;in premium GPUs, CPUs, and infrastructure.</li><li>Demand skilled staff for setup, maintenance, and operation.</li><li>Need a strong sales pipeline to secure renters or contracts to ensure high utilisation.</li></ul><p><strong>Storage Innovations</strong>: Premium GPU clusters incorporate cutting-edge storage solutions to complement their high-performance computing capabilities:</p><ul><li><strong>All-flash arrays (AFAs)</strong>&nbsp;provide ultra-low latency and high IOPS, essential for the massive data throughput demands of HPC and AI training.</li><li><strong>Parallel file systems</strong>&nbsp;such as <strong>Lustre</strong>&nbsp;or <strong>IBM Spectrum Scale (GPFS)</strong>&nbsp;enable rapid data access and processing across distributed clusters.</li><li><strong>Tiered storage architectures</strong>, integrating SCM or NVMe alongside more cost-effective storage, optimise both performance and cost.</li></ul><p><br></p><p><strong>OpenZFS, ZFS Or other Distributed Files Systems</strong>:</p><ul><li>OpenZFS is a popular choice for managing complex storage needs in premium clusters. Its ability to handle <strong>data compression</strong>&nbsp;and <strong>adaptive caching</strong>&nbsp;enhances both performance and efficiency.</li><li>With support for <strong>replication</strong>&nbsp;and <strong>snapshots</strong>, OpenZFS ensures data security and facilitates system recovery in high-stakes environments.</li></ul><p><strong>CXL for High-Performance Memory</strong>:</p><ul><li>CXL enables the creation of <strong>memory fabrics</strong>, allowing GPUs, CPUs, and accelerators to access shared memory pools without traditional bottlenecks.</li><li>This capability enhances scalability for AI/ML training workloads and complex simulations, making it a critical component in premium clusters.</li></ul><p><strong>Risk and Profitability</strong>:</p><ul><li>Highly profitable when fully utilised, but operational costs are significant.</li><li>Risky for inexperienced investors unless a skilled team and customer contracts are in place before deployment.</li></ul><p><br></p><h3>Maximising Revenue Across Tiers</h3><p>Each tier caters to specific customer needs, from budget-conscious workloads to cutting-edge applications requiring premium hardware, storage, and memory. The inclusion of advanced interconnects like <strong>CXL</strong>, alongside robust file systems like <strong>OpenZFS</strong>, further enhances performance and scalability, making these solutions even more attractive to diverse markets. By offering comprehensive server configurations, service providers can ensure increased revenue as they scale up the performance and complexity of their offerings.</p><p><br></p><h4><strong>3. Enhanced Customer Satisfaction and Retention</strong></h4><p>Customers benefit from faster workload completion and reliable performance:</p><ul><li><strong>Reduced Latency</strong>: Technologies like NVLink\Infinity Fabric and RDMA improve user experience for latency-sensitive tasks.</li><li><strong>Seamless Scalability</strong>: InfiniBand and RoCE ensures smooth expansion for growing workloads.</li></ul><p>Satisfied customers are more likely to renew contracts and recommend services, increasing lifetime value (LTV).</p><p><br></p><h4><strong>4. Operational Cost Savings</strong></h4><p>Efficient interconnects reduce costs associated with energy and infrastructure:</p><ul><li><strong>Lower Power Consumption</strong>: Faster job execution reduces energy costs per workload.</li><li><strong>Optimized Hardware Requirements</strong>: High-speed interconnects minimise the number of nodes required for large-scale jobs.</li></ul><p><br></p><h4><strong>5. Market Competitiveness</strong></h4><p>Providers offering advanced interconnect technologies stay ahead in the competitive server rental market:</p><ul><li><strong>AI/ML and HPC Attractiveness</strong>: InfiniBand and NVLink cater to the growing demand for these workloads.</li><li><strong>Branding as a Premium Provider</strong>: High-performance interconnects elevate the perceived value of services.</li><li><br></li></ul><h3><strong>Challenges and Considerations</strong></h3><ul><li><strong>Higher Initial Costs</strong>: Advanced interconnects like InfiniBand and NVLink require significant investment.</li><li><strong>Customer Education</strong>: Providers must educate customers on the value of interconnect technologies to justify premium pricing.</li></ul><p><br></p><h3><strong>Conclusion</strong></h3><p><br></p><p>Interconnect technologies like<strong> PCIe 5, InfiniBand, RoCE, NVLink, xGMI Infinity Fabric CXL,</strong> and <strong>RDMA</strong>&nbsp;are transforming server infrastructure, particularly in clustered CPU-GPU systems. Their ability to enhance performance, optimise resource usage, and reduce costs makes them indispensable for modern server rentals. By offering advanced interconnect-equipped servers, rental providers can:</p><p><br></p><ol><li>Accelerate workload execution for AI, ML, and HPC tasks.</li><li>Maximise resource utilisation and server profitability.</li><li>Differentiate their offerings and charge premium prices.</li><li>Build customer loyalty and strengthen market positioning.</li></ol><p><br></p><p>Incorporating these interconnect technologies into server rental strategies ensures long-term profitability and competitiveness in a rapidly evolving market.</p><p><br></p><p>This blog is for anyone interested in earning passive income by renting out servers they've purchased as part of a new and exciting asset class. If you're considering entering this market but need guidance, feel free to reach out to me, <a href="https://www.linkedin.com/in/james-walsh-15b40591/" rel="noopener noreferrer" target="_blank"><strong>James Walsh (Sytronix)</strong></a> or <a href="https://www.linkedin.com/in/alex-moody-6b2502170/" rel="noopener noreferrer" target="_blank"><strong>Alex Moody (Sytronix)</strong></a> via LinkedIn. I’ll also share this blog there for easy access.</p><p>Don’t worry if you're unfamiliar with the technologies discussed here just ask!</p><p><br></p><p>Whether you connect with me, one of my team members, or our trusted partners Panchaea&nbsp;in Europe and <strong>Hydra Host</strong>&nbsp;in the USA we’ll be happy to explain the tech and its potential in simple, straightforward terms.</p><p><br></p><p>For those looking to explore the investment opportunities in this space further, our data center partner, <a href="https://www.daylightcompute.co.uk/" rel="noopener noreferrer" target="_blank"><strong>Daylight Compute</strong></a>&nbsp;(based in the UK), can provide additional insights. Let’s get started!</p>

Learn how interconnects like PCIe, NVLink, and InfiniBand impact the profitability of AI server rentals. Optimize for speed and revenue.

Profitability Of Renting AI servers with different interconnects

<p>As the CEO and Co-Founder of Hydra Host, I have dedicated my career to transforming the landscape of high-performance computing. At HydraHost, we've created the fastest and simplest platform to buy, sell, and manage bare metal infrastructure. By leveraging an API-driven approach, we enable businesses to monetize unused GPUs and high-grade CPUs seamlessly, while also providing wholesale access to these powerful resources. This venture reflects my commitment to innovation and efficiency in technology.</p><p>Over the past decade, I've co-founded and led multiple initiatives, including the <a href="https://www.thefai.org" rel="noopener noreferrer" target="_blank">Foundation for American Innovation</a>, which bridges the tech industry with policymakers to drive meaningful change. My journey in the tech world has also included leadership roles at Everlane and StumbleUpon, where I honed my expertise in growth and product management, and co-founding <a href="https://fabiuslabs.com" rel="noopener noreferrer" target="_blank">Fabius Labs</a>, a digital marketing agency aimed at helping businesses scale products customers love.</p><p>Beyond entrepreneurship, my passion lies in blending philosophy, economics, and technology to create ventures that serve humanity and foster progress.</p><p>Recognized by Forbes <a href="https://www.forbes.com/pictures/fjle45eeelm/aaron-ginn-28/" rel="noopener noreferrer" target="_blank">Forbes 30 under 30 in 2017</a>, The Wall Street Journal, TechCrunch, and other leading platforms, I remain committed to harnessing technology to advance liberty and innovation for the betterment of society.</p>

Building GPUs Is Easier Than You Think | Aaron Ginn on TBPN

The GPU Whisperer: AI, Data Centers, and Hydra Host's Edge

Aaron Ginn on the Moral Idea Maze, China, NVIDIA and GPU Revolution

Episode 268: Aaron Ginn, Co-founder of Hydra Host | Host: Julian Torres

004: The State of GPU Demand with Hydra Host CEO Aaron Ginn

#1274 Aaron Ginn | This Founder Raised $10 Million To Protect Artificial Intelligence From Governments

Aaron Ginn

<p><strong>FOR IMMEDIATE RELEASE</strong></p><p><br></p><p><strong>Hydra Host Powers World’s First Sovereign Deployment of NVIDIA B300 Chips in El Salvador</strong></p><p><em>AI sovereignty starts with infrastructure—Hydra delivers it.</em></p><p><br></p><p><strong>San Salvador, El Salvador – June 26, 2025</strong>&nbsp;– In a historic move set to redefine the future of AI,&nbsp;<strong>Hydra Host</strong>&nbsp;has provisioned the world’s&nbsp;<strong>first sovereign acquisition of NVIDIA B300 chips</strong>&nbsp;for&nbsp;<strong>El Salvador’s National Artificial Intelligence Laboratory</strong>, marking a bold leap toward full-stack technological independence.</p><p><br></p><p>Following a meeting between President Nayib Bukele and Hydra Host CEO &amp; Co-founder&nbsp;<strong>Aaron Ginn</strong>, El Salvador secured access to the most advanced AI processors on the market, enabling the country to train, run, and secure AI models on infrastructure it fully controls.</p><p><br></p><p>“This isn’t just a chip order, it’s a sovereignty play,” said Ginn. “Hydra exists to give governments and enterprises control over their compute future.”</p><p><br></p><p>Hydra Host, an&nbsp;<a href="https://hydrahost.com/post/hydra-host-named-nvidia-dgx-cloud-partner/" rel="noopener noreferrer" target="_blank">NVIDIA Cloud Partner (NCP)</a>&nbsp;focused entirely on&nbsp;<strong>bare metal deployments</strong>, is building the infrastructure behind El Salvador’s Sovereign AI—a national strategy for strategic resilience, economic competitiveness, and digital independence.</p><p><br></p><p>This marks a new chapter in Hydra’s mission to empower sovereign AI capabilities. The company has deployed GPU infrastructure in over&nbsp;<strong>40&nbsp;global data centers</strong>, launched turnkey&nbsp;<a href="https://hydrahost.com/ai-factory/" rel="noopener noreferrer" target="_blank"><u>AI Factories</u></a>&nbsp;for enterprises and nation-states, and is now expanding its presence in Central America by hiring its&nbsp;<strong>first full-time team member in El Salvador</strong>.</p><p><br></p><p>Hydra will also play a hands-on role in&nbsp;<strong>talent development</strong>, as Aaron Ginn leads workshops with students in the country’s&nbsp;<a href="https://x.com/cubo_ai" rel="noopener noreferrer" target="_blank"><u>Cubo AI program,</u></a>&nbsp;preparing a new generation of AI engineers equipped to build, scale, and govern their own models locally.</p><p><br></p><p>This announcement follows El Salvador’s broader push for technological sovereignty through its&nbsp;<strong>National Artificial Intelligence Agency (ANIA)</strong>, with partners including Google AI, NVIDIA, ARK Invest, and Nike.</p><p><br></p><p>By placing B300 chips directly into the hands of a nation, not a hyperscaler, Hydra Host is setting the gold standard for sovereign AI infrastructure. In a world where compute is power,&nbsp;<strong>true sovereignty starts with bare metal</strong>.</p><p><br></p><p><strong>Media Contact</strong></p><p>Andrea Holt</p><p>Marketing Manager, Hydra Host</p><p><a href="mailto:andrea.holt@hydrahost.com" rel="noopener noreferrer" target="_blank"><u>andrea.holt@hydrahost.com</u></a></p>

President Bukele and Aaron Ginn announce B300 deployment for El Salvador’s National AI Lab.

Hydra Host powers El Salvador’s historic deployment of NVIDIA B300 chips, delivering the world’s first sovereign AI cluster.

Hydra Host Powers World’s First Sovereign Deployment of NVIDIA B300 Chips in El Salvador

Announcing Hydra as an NVIDIA Cloud Partner supporting global AI infrastructure and AI Factory deployments.

<h1><strong>FOR IMMEDIATE RELEASE:</strong></h1><p><br></p><h1><strong>Hydra Host Joins NVIDIA DGX Cloud Lepton, Expanding Access to High Performance AI Infrastructure</strong></h1><p><br></p><p><strong>June 11, 2025 | Miami, Florida - </strong>Hydra Host, a global<strong> AI infrastructure provider</strong> specializing in bare metal GPU deployment and cluster monetization, has been named an <strong>NVIDIA Cloud Partner (NCP) and is contributing compute resources to </strong><a href="https://nvidianews.nvidia.com/news/nvidia-dgx-cloud-lepton-connects-europes-developers-to-global-nvidia-compute-ecosystem" rel="noopener noreferrer" target="_blank"><strong>DGX Cloud Lepton</strong></a><strong>,</strong> an AI platform and marketplace connecting developers to a global network of GPU compute.</p><p><br></p><p>As one of the NCPs contributing to DGX Cloud Lepton , Hydra is uniquely positioned to help organizations build scalable <strong>AI factories</strong> - high-performance compute environments purpose built for large model training, inference, and monetization. Hydra currently manages over 20,000 bare metal GPUs across 40+ global data center partners, giving customers flexible deployment options across the U.S., Europe, and APAC.</p><p><br></p><p>This milestone aligns with <a href="https://hydrahost.com/ai-factory/" rel="noopener noreferrer" target="_blank">Hydra’s AI Factory Accelerator</a>, a full-stack program that supports customers building sovereign AI factories for procurement and financing to deployment, compliance, and monetization.</p><p><br></p><p>“Our collaboration with NVIDIA reflects a shared belief in the future of AI factories - sovereign, high-performance, and fully monetizable,” said Aaron Ginn, CEO of Hydra Host. “DGX Cloud Lepton is a strategic resource for AI developers, and we’re proud to deliver compute resources through a platform that enables customers to innovate and scale rapidly."</p><p><br></p><p>As AI demand increases globally, organizations need infrastructure that combines performance, ownership, and cost efficiency. With DGX Cloud Lepton and the AI Factory Accelerator, Hydra enables customers to turn high-performance GPU clusters into sustainable infrastructure assets.</p><p><br></p><p>For more information, visit <a href="https://www.hydrahost.com/" rel="noopener noreferrer" target="_blank">hydrahost.com</a> or explore the <a href="https://www.nvidia.com/en-us/data-center/dgx-cloud-lepton/" rel="noopener noreferrer" target="_blank">DGX Cloud Lepton initiative</a> at <a href="https://www.nvidia.com/en-us/data-center/dgx-cloud-lepton/" rel="noopener noreferrer" target="_blank">nvidia.com</a>. You can also apply for DGX Cloud Lepton early access here.</p>

Hydra Host joins NVIDIA DGX Cloud Partner, accelerating AI Factory deployments with bare metal GPU infrastructure worldwide.

Hydra Host Joins NVIDIA DGX Cloud Lepton, Expanding Access to High Performance AI Infrastructure

<h1><strong>Introduction: The Anticipation of Blackwell</strong>&nbsp;</h1><p><br></p><p>In the fast-paced world of high-performance computing (HPC) and artificial intelligence (AI), NVIDIA’s upcoming <a href="https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/?ncid=pa-srch-goog-267462&amp;_bt=709953316733&amp;_bk=nvidia%20blackwell&amp;_bm=p&amp;_bn=g&amp;_bg=169122792928&amp;gad_source=1&amp;gclid=EAIaIQobChMI-vbrv9aGiAMV2zWtBh1nQipsEAAYASAAEgIT6vD_BwE" rel="noopener noreferrer" target="_blank">Blackwell architecture</a> is the next big leap everyone is eagerly awaiting. Blackwell is expected to deliver unprecedented breakthroughs in performance, energy efficiency, and scalability, set to redefine AI, HPC, and data center applications. But with Blackwell's release still on the horizon, businesses can't afford to wait. So, what are the best <strong>GPU alternatives</strong> to rent in the meantime?&nbsp;</p><p><br></p><h2><strong>The Waiting Game: Why You Need GPU Solutions Now</strong>&nbsp;</h2><p><br></p><p>The anticipation around NVIDIA Blackwell is palpable, but waiting doesn't mean your AI and HPC projects should stall. In today’s competitive landscape, staying ahead means accessing powerful <strong>compute solutions</strong> right now. Fortunately, Hydra Host offers several <strong>GPU rental options</strong> that can seamlessly bridge the gap while you await Blackwell's official release.&nbsp;</p><p><br></p><h2><strong>Current Alternatives to Blackwell: What to Rent Now</strong>&nbsp;</h2><p><br></p><h3><strong>1. </strong><a href="https://hydrahost.com/nvidia/h200/" rel="noopener noreferrer" target="_blank"><strong>Commit to H200</strong></a>&nbsp;</h3><p>The NVIDIA <strong>H200 GPU</strong> is the next evolution from the H100, delivering even more power and performance for demanding AI and HPC applications. While it comes at a slightly higher cost than the H100, the H200’s advanced architecture and enhanced capabilities make it ideal for businesses that need top-tier performance without compromise. This GPU ensures that your AI, machine learning (ML), and data processing tasks are completed with unmatched efficiency, keeping you ahead of the competition.&nbsp;</p><p>&nbsp;</p><ul><li>Ideal for pushing the boundaries of AI and ML.&nbsp;</li><li>Suitable for running complex simulations.&nbsp;</li><li>Effective for training large-scale models.&nbsp;</li><li>Handles massive datasets with unmatched efficiency.&nbsp;</li><li>Ensures top-tier performance for businesses requiring no compromise.&nbsp;</li></ul><p><br></p><h3><strong>2. </strong><a href="https://hydrahost.com/nvidia/grace-hopper/" rel="noopener noreferrer" target="_blank"><strong>On-Demand GH200s</strong></a>&nbsp;</h3><p><br></p><p>Renting on-demand <strong>GH200 GPUs</strong> gives your business the flexibility to scale resources as needed, without the burden of long-term commitments. At just $4.99 per card on the <a href="http://brokkr.hydrahost.com/" rel="noopener noreferrer" target="_blank">Brokkr Marketplace</a>, this cost-effective option is ideal for businesses that need to stay agile and ready for Blackwell’s release.&nbsp;</p><p><br></p><ul><li>Perfect for prototyping AI/ML models.&nbsp;</li><li>Useful for testing new algorithms.&nbsp;</li><li>Prepares workloads for future transition to Blackwell.&nbsp;</li><li>Allows for nimble operations with flexible scaling&nbsp;</li><li>Cost-effective option at $4.99 per card on Brokkr Marketplace.&nbsp;</li></ul><p><br></p><h3><strong>3. </strong><a href="https://hydrahost.com/nvidia/h100-pcie-80-gb/" rel="noopener noreferrer" target="_blank"><strong>H100 Tensor Core GPUs</strong></a>&nbsp;</h3><p><br></p><p>The <strong>H100 Tensor Core GPUs</strong> remain a top choice for AI and deep learning tasks, renowned for their exceptional performance. With some H100 clusters coming off contract, businesses can seize the opportunity to access these powerful GPUs at competitive on-demand pricing or through attractive long-term deals, particularly suited for inference workloads. This flexibility allows you to efficiently scale your AI operations while optimizing costs.&nbsp;</p><ul><li>Excellent for training and inference in deep learning models.&nbsp;</li><li>Ideal for natural language processing (NLP) tasks.&nbsp;</li><li>Supports advanced scientific computing.&nbsp;</li><li>Suitable for businesses needing short-term rentals or long-term commitments.&nbsp;</li><li>Offers reliability and power to maintain momentum in AI projects.&nbsp;</li></ul><p><br></p><h3><strong>4. </strong><a href="https://hydrahost.com/nvidia/a100-pcie-40-gb/" rel="noopener noreferrer" target="_blank"><strong>A100 GPUs</strong></a>&nbsp;</h3><p><br></p><p>The <strong>A100 GPUs</strong>, part of NVIDIA’s Ampere architecture, continue to be a reliable workhorse for AI, data analytics, and HPC tasks. These GPUs offer an excellent balance of performance and cost, making them a savvy choice for businesses that want to maintain momentum without overspending while waiting for Blackwell.&nbsp;</p><ul><li>Well-suited for both training and inference workloads.&nbsp;</li><li>Ideal for AI, data analytics, and HPC tasks.&nbsp;</li><li>Provides robust power without a hefty price tag.&nbsp;</li><li>Cost-effective bridge to the future NVIDIA Blackwell architecture.&nbsp;</li><li>Maintains project momentum during the wait.&nbsp;</li></ul><p><br></p><h3><strong>GPU Comparison Table</strong>&nbsp;</h3><p><br></p><p>Here’s a detailed comparison of the H200, GH200, H100, and A100 GPUs to help you choose the best option based on your project needs:&nbsp;</p>

<h1><strong>Why Renting Makes Sense Now</strong>&nbsp;</h1><p><br></p><ul><li><strong>Avoiding Obsolescence:</strong> Why invest heavily in technology that may soon be outdated? Renting <strong>GPUs</strong> allows you to avoid large expenditures on hardware that could lose its edge once Blackwell is released.&nbsp;</li><li><strong>Flexibility and Scalability:</strong> Renting <strong>GPU resources</strong> provides the agility your business needs. You can scale up or down based on project demands, without being tied down, paying only for what you use.&nbsp;</li><li><strong>Preparing for the Future:</strong> Renting GPUs now allows your team to continue refining projects, so when Blackwell finally arrives, you’re not just catching up—you’re leading the pack in AI and HPC innovation.&nbsp;</li></ul><p><br></p><h1><strong>Conclusion: Stay Ahead of the Curve</strong>&nbsp;</h1><p><br></p><p>In the rapidly evolving AI/ML landscape, standing still is not an option. Renting GH200s, H100s, or A100s is a strategic move to keep your business moving forward during the wait for NVIDIA Blackwell. It’s about positioning yourself to leverage future advancements, not just survive them.&nbsp;</p><p><br></p><p>Explore the comprehensive <strong>GPU rental options</strong> available in the <a href="http://brokkr.hydrahost.com/" rel="noopener noreferrer" target="_blank">Brokkr Marketplace</a> today. <a href="https://meetings.hubspot.com/ryan-horjus" rel="noopener noreferrer" target="_blank">Schedule a consultation</a> with our Lead Sales Engineer, Ryan, and find out which setup best meets your needs, ensuring you’re ready when Blackwell arrives.&nbsp;</p>

Discover top GPU rental options like H100, GH200, and A100 while waiting for NVIDIA Blackwell. Optimize AI and HPC workloads today.

What to rent while waitign for NVIDIA Blackwell? 

What to Rent While Waiting for NVIDIA Blackwell: Top GPU Alternatives for AI and HPC 

<p>The GPU computing wars aren't just between AMD, Intel, and NVIDIA. This war isn't just between the imminent public cloud GPUs and NVIDIA. Nation-states are joining the mix because computing power and AI will define the next ten years, and pre-existing geo-political trends will push the GPU market to fragment.&nbsp;</p><p>&nbsp;</p><p>One of the unintended consequences of US restrictions on GPUs has been the rapid escalation of the <a href="https://www.theinformation.com/articles/chinese-regulators-tell-local-tech-firms-to-buy-fewer-nvidia-chips" rel="noopener noreferrer" target="_blank">GPU arms race</a>. These restrictions have pitted countries against each other and against international cloud companies and NVIDIA. The US government's <a href="https://www.reuters.com/technology/upcoming-us-rules-ai-chip-exports-aim-stop-workarounds-us-official-2023-10-15/" rel="noopener noreferrer" target="_blank">decision to annex NVIDIA</a> as a national security asset, a move many consider unconstitutional, has further fueled this race, undermining the world's confidence in NVIDIA's ability to transact freely.&nbsp;</p><p>&nbsp;</p><p>The fear of falling behind in the AI race is about more than losing technological prowess. It's a fear that power, stability, and even national defense could be at stake. The concept of "Bring your own GPU" (BYOG) is positioned to dominate the global computing race, leading to a rapid fragmentation with prevailing players in both the hardware and GPU rental space. This global GPU footprint will mirror broader geo-political relationships that defined the world pre-H100 and A100. Moreover, the potential for several countries to misuse AI against their own citizens is a reality we cannot ignore.&nbsp;</p>

<p>At Hydra Host, we are betting on data centers. A bet on data centers is a bet on NVIDIA. As competition for accelerators and GPUs heats up, NVIDIA will return to its reliable moat and bread and butter: data centers. As the public cloud pursues its budget GPUs and nation-state subsidies for commodity GPU alternatives, NVIDIA will remain the king and continue growing its data center footprint as the data center industry has no interest or capability of building its own GPU. You can see this clearly with NVIDIA's push towards fully incorporated builds like the GH200 and GB200.&nbsp;</p><p>&nbsp;</p><p>GPUs, being naturally agnostic and with a limited-to-zero need for associated cloud services, broke the back of a public cloud. There was no longer a need to stay locked in with these mega-cloud companies. <strong>GPU sets the computing world free.</strong> As a result, priorities have shifted, and non-GPU considerations are driving the global battle for data center capacity and GPU computing.&nbsp;</p><p>&nbsp;</p><p>Aaron Ginn, CEO</p>

Explore the GPU computing wars shaping AI and geopolitics. Learn how NVIDIA, cloud providers, and nation-states compete for global GPU dominance.

Navigating the GPU Computing Wars

<p>We are on the horizon of the inevitable shift towards GPU-dominated infrastructure, and this movement will build several unicorn companies.</p><p><br></p><p><a href="https://www.reuters.com/technology/crypto-billionaires-nonprofit-buys-500-million-ai-data-center-chips-2023-10-30/" rel="noopener noreferrer" target="_blank">Voltage Park</a>, a soon-to-be billion-dollar H100 provider, shows the momentum of the shift towards GPUs. Accessible GPUs are going to transform the infrastructure of the web. As Jensen says, “Moore’s law is dead,” and it is time for the next generation of computing infrastructure. CPU-based public cloud operations can’t move quickly and capture this demand for GPUs because they have built their entire business model on low-cost, low-power, commoditized CPUs. GPUs are energy-dense and require more advanced cooling than CPUs. GPU-specific suppliers, like Voltage Park, are designing their operations and infrastructure to support these requirements. These new types of suppliers are capturing this growing demand while the market waits for overpriced public cloud-supplied GPUs.</p><p><br></p><p>The next unicorn companies within AI are infrastructure companies. Demand is only increasing as large enterprises are now beginning to adopt AI and LLMs. NVIDIA is sold out for years to come. The GPU deficit, combined with the public cloud’s subpar design for GPU operations, has pushed customers to alternatives like Voltage Park and the secondary market.</p><p><br></p><p>The nascent secondary market for GPUs is a forcing function of the annual billion-dollar shortage of GPUs. The supply and pricing you get from system integrators and direct relationships with NVIDIA are consistent and stiff, just like buying a new car. You aren’t going to get a fantastic deal anymore from OEMs or integrators. NVIDIA has a tight grip on every single hardware builder. From shortages and high demand, a new bare metal marketplace is here.</p><p><br></p><p>When you need a car, the inevitable choice is presented: new or used. Buying a new car is a relatively standard and inflexible process with little room to make a deal. If you find a deal, it usually comes from the manufacturer as a financial incentive to the dealer (ergo, NVIDIA Inception Program). Typically, the dealership has a minimal margin on new car sales. The used car market, on the other hand, has a higher variance between cars, making it harder to price consistently. Therefore, good deals exist predominantly in the secondary market.</p><p><br></p><p><a href="https://www.linkedin.com/company/hydrahost/" rel="noopener noreferrer" target="_blank">Hydra Host</a>’s platform supports this new secondary market for GPUs, and we are seeing many massive and insane deals. Rising prices and endless demand have given birth to a bare metal marketplace. Like with cars, pricing is better on the secondary market. Some suppliers bought early and can run GPUs 30% cheaper than the public cloud. Data centers are refurbishing GPUs and running them at half the price. Resellers are buying cards on cheap and holding them.</p><p><br></p><p>If you want to see what is happening in the GPU market, sign up at Hydra Host and see what is happening.</p>

Discover how the transition to GPU-centric infrastructure is creating new unicorn companies and transforming the tech landscape.

The Inevitable Shift

<p>This was a wild week for the GPU market. Brand new export restrictions caused a tumultuous week. Additional limitations on future H100 orders are expected. The steady pricing from previous weeks was thrown out the window. Here is what my team at&nbsp;<a href="https://www.linkedin.com/company/hydrahost/" rel="noopener noreferrer" target="_blank">Hydra Host</a>&nbsp;is seeing.</p><p><br></p><p>New export restrictions lay down the hammer - The US government continues to turn up the heat on advanced computing chips with export chips. The looming new rules released this week are substantial and include additional manufacturing equipment restrictions and expanded qualifications. The new proposed regulations made “Chinese” NVIDIA chips irrelevant in design and, at the same time, valuable. These chips were essentially designed to evade export restrictions, and the new rules significantly expanded their scope and authority (see images below). Almost all advanced chips from NVIDIA and some gaming chips qualify. There are some exceptions to be more targeted, but the US government’s explicit goal is enterprise data centers. The US government has capped China’s computing power, forcing it to accelerate its internal development. The black market for GPUs will aggressively develop over the coming year. I wouldn’t be surprised if GPUs become like a new drug trade.</p><p><br></p><p>Slapping OEMs selling only cards - NVIDIA is tracking down OEMs selling GPU cards rather than building complete systems. NVIDIA aims to expand its footprint as fast as possible in data centers worldwide, and this under-the-table market is slowing it down. It is also exacerbating the shortage, so we heard NVIDIA removing allocation to OEMs, continuing to sell just chips and not building servers.</p>

<p><strong>Buy. Buy. Buy </strong>whatever you can get - From these looming restrictions from the US government and NVIDIA applying more pressure to GPU-as-a-service vendors, there is a renewed aggressive buying spree. Some companies are hoarding to resell in the future, while others are concerned that nothing will be left. A handful of companies are scooping every H800 they can find, betting that the same thing that happened to A100s will happen to H800s. We saw new and used A100 supply disappear the same day. H100 servers moved faster than usual as companies expect future price increases.</p><p><br></p><p>GPU prices will increase soon - Due to restricted supply and higher demand, prices are going up again. Orders in November and December are already higher than in October. We expect H100s to have the most considerable price increase. Resellers are also increasing their prices due to fewer buyers and more shortages. Feels like we are in a prison yard-style market again.</p><p><br></p><p>We have supply today if you need A100, H100, or other GPUs. Ping <a href="mailto:sales@hydrahost.com" rel="noopener noreferrer" target="_blank">sales@hydrahost.com</a> for availability. We can help find any GPU for you, whether renting or buying.</p>

Learn about the latest U.S. export restrictions on GPUs and their implications for the tech industry.

GPU Chip Restrictions

<p>The joke within the venture community is that they are not funding AI companies but funding infrastructure to buy NVIDIA chips. <a href="https://www.theinformation.com/articles/andreessen-horowitz-discussed-backing-a-midjourney-rival-seeking-500-million-valuation" rel="noopener noreferrer" target="_blank">Investing in AI companies </a>today is just a direct transfer to NVIDIA. Due to GPU demand, that is true today, but supply and pricing pressures on HPC infra will evolve the market in the next 12 months.</p>

<p>DCs are going direct and selling wholesale. Before the GPU revolution, there were generally only two hosting options - hyper-scalers or colocation. That is changing now due to the cost of GPUs and limited supply. Pre-GPU revolution, CPUs were highly commoditized, and you needed a massive scale to have any competitive advantage. It didn't make sense for most companies to expend capital on a commodity bare metal. The world today is changing quickly. GPUs vastly differ from CPUs: compact, immensely powerful, expensive, and not commoditized. In this new world, DCs are creating a new business line by bypassing cloud companies and renting GPUS directly to companies. Colocation space that would have previously gone to the public cloud or big corp colocation will go to the rent-a-GPU business to make cloud-like margins. Data centers are even adding financing capabilities to get more GPU business. Going direct saves AI or platform companies 15-20% on their computing and hosting costs.</p>

<p>Companies are starting to resell their GPUs to others. HPC hardware is being over-acquired and underutilized, but the reasons are similar to the situation with Web 1.0 companies (which spawned the public cloud). Due to infra limitations, Web 1.0 companies needed extra bare metal capacity to scale for something as simple as visitors. You had to build your sovereign infra to handle traffic spikes. Today, the market situation looks familiar. Companies need bursting capacity to massive clusters for better training runs. Rather than waiting for an unknown period of time, constantly hunting for capacity, or having your training run cut off by another customer, companies are sitting on underutilized hardware or inefficiently using GPUs ("Well, it is there, so might as well use it"). Companies are now taking advantage of the market dynamics and reselling their GPUs to the public. Companies can recoup 100% of their hardware investment at current rental prices in less than 12 months.</p>

<p>These two reasons are why we built&nbsp;<a href="https://www.linkedin.com/company/hydrahost/" rel="noopener noreferrer" target="_blank">Hydra Host</a>&nbsp;to take advantage of the next phases of the global bare metal market. We make it super easy to manage and sell GPUs with our bare metal marketplace -&gt;&nbsp;<a href="http://www.hydrahost.com/" rel="noopener noreferrer" target="_blank">www.hydrahost.com</a></p>

 Explore how data centers are reshaping the bare metal market by renting GPUs directly, enhancing performance and reducing costs.

Next Phases of the Bare Metal Market

<p>There are two types of moments in history: those that feel like nothing happens and those that feel as if the world changes multiple times over in a single lifetime. The last decades have seen the rise of mega tech platforms that promised obvious efficiencies with few downsides. These platforms changed our entire society. What was promised was more choice and freedom for all with lower prices and what we got was an assault on our core identity as a society. Today, we have a choice — what do we want our future to be?</p><p><br></p><p>In the past decade, the single greatest accelerant of societal change was mega internet platforms. With widespread adoption and near infinite scale, big tech platforms were sold as a public good and innovation free from moral responsibilities for every consumer and business to benefit from. On the surface, things appeared harmless and basically free, like selling books or providing information. Underneath their full ownership of the core infrastructure stack for the free and open web, from domains to computing power, lurked increasing lock-in with walled gardens that grew into a power that was destined to be abused. Without a check, this type of absolute power will be corrupted.</p>

<blockquote><em>“Power tends to corrupt, and absolute power corrupts absolutely.” — Lord Acton</em></blockquote>

<p>Then, COVID changed everything and brought light to the brewing darkness that had been brewing behind the meticulously designed and crafted walled gardens. The mask was taken off, so mega-tech platforms attempted to mask the world. They executed a near autocrat-level control over global society at once. Convenience and subsidized lock-in with “free services” turned into a society-wide hostage situation. The wolf was in the hen house. With scale, came brutal centralized control that threatens every citizen and business.</p><p><br></p><p>With COVID, quiet conversations about censorship that only impact the fringe and politically involved now reached the mainstream. What previously impacted the 1% now suddenly impacted the other 99%. The world was in shock at the speed and intensity of this change. Business owners were thrown into jail for normal operations. Dissent led to mob-like censorship the likes infamous dictators would have been in awe of and public witch burning to unperson the heterodox made a massive comeback. People were literally arrested for surfing in the ocean and walking down the street without a cloth mask on.</p><p><br></p><p>I was a victim of the thumb of mega tech platforms. I&nbsp;<a href="https://www.wsj.com/articles/controlling-the-virus-narrative-11584899715" rel="noopener noreferrer" target="_blank">wrote a piece in March of 2020</a>&nbsp;that was the first seminal criticism of lockdowns and COVID hysteria. It garnered millions and millions of views in less than 24 hours. My core argument warned that our politicians were taking things way too far and the evidence did not justify the rash and immoral dilution of basic freedoms. Then, I suffered a fate like many others —&nbsp;<a href="https://www.wsj.com/articles/the-lockdown-skeptic-they-couldnt-silence-11589566245" rel="noopener noreferrer" target="_blank">I was canceled</a>. My predictions and assessments were accurate, yet my access to the internet was revoked. I was deemed a thought criminal and put into internet jail for expressing precise and well-supported views.</p><p><br></p><p>For the length of COVID hysteria, we watched the free and open internet suffocate under the weight of mega tech platforms. These platforms were founded to achieve scale but did so with a cornerstone of lock-in and centralization. These values prohibited the abilities of companies and people to freely operate and utilize the open web when it mattered the most. We asked for the promise of a free and open internet. Instead, we got an elimination of customer choice and mega tech profiting from lock-in by bundling sub-par products and abusing power without a second thought. Despite how much society is opposed, mega tech platforms have become too big to fail.</p><p><br></p><p><strong>It is time to change that.</strong></p><p><br></p><p>The world needs a new social contract with technology companies that won’t take away power but instead enables freedom, choice, sovereignty, and dignity. The world needs a new type of infrastructure platform that follows the initial vision of a free and open web with a renewed promise to ALWAYS give you choice and privacy for whatever your needs or priorities are. This is why I started&nbsp;<a href="http://www.hydrahost.com/" rel="noopener noreferrer" target="_blank">Hydra Host</a>&nbsp;with two longtime friends, Garrett and Ariel.</p><p><br></p><p>Hydra Host is here to offer&nbsp;<em>real</em>&nbsp;customer choice in hosting, web infrastructure, and most importantly, compute. Infrastructure is the heartbeat of how the internet works and is fundamental to the social contract between platforms and users. Infrastructure is how every company or individual accesses the web. Without sound infrastructure, you could be cut off from your right to the web. Hydra Host is here to fix that.</p><p><br></p><p>Let’s be real. No one really likes their hosting provider. The public cloud failed. It can’t do everything you want. Bills are confusing. In your gut, the price feels out of proportion compared to what you receive. There is constant pressure from providers to pull you further into their toolchains so you can’t leave. Their “free credits” feel like a gateway drug because you are always hearing about the better hosting provider down the road. Your infrastructure provider should not control your business or control you.</p><p><br></p><blockquote>YOU should be in charge so YOU can do choose to do what is best for YOUR company and YOU should never be concerned about your cloud infrastructure provider deciding your company can’t do business.</blockquote><p><br></p><p>From Hydra Host’s founding moment, we have followed the best security and privacy practices and adopted cutting-edge agnosticism and open-source frameworks.&nbsp;<strong>Our vision is for any user to have the ability to access any infrastructure provider in the world, whether for cost or security reasons, with a single API.</strong>&nbsp;At any time, you can decide to leave any provider or try a new provider. No refactoring. No complexity. Freedom to choose, freedom to move as easy as a click. The choice is yours.</p><p><br></p><p>Just like with Kayak or any other marketplace, you are in the driver’s seat. Hydra Host has standardized and simplified every point of the provisioning process. Each hosting company is a common and uniform node that you can access in a simple and single programmatic framework. With our API, we prevent you from getting locked in ever again. Our commitment with Hydra Host is to constantly deliver the best hosting possible while respecting our customer's and client’s privacy. We believe the internet should be accessible to every single person and company regardless of background, creed, faith, or tradition. You can test our initial Terraform provider API for yourself —&nbsp;<a href="https://github.com/Hydra-Host/terraform-provider-hydra" rel="noopener noreferrer" target="_blank">https://github.com/Hydra-Host/terraform-provider-hydra</a></p><p><br></p><p>As&nbsp;<a href="https://twitter.com/get_hydrahost/status/1501280353838792706?s=20&amp;t=DLKQggKEfJyXdRyLpU8t3g" rel="noopener noreferrer" target="_blank">we allow mega tech companies to control more and abuse their power</a>, our ability to see what humanity can truly do is limited. We are choking humanity’s greatest asset for survival — creativity. The future is too important to let a handful of mega-tech companies determine what we’re allowed to do. It is harder than ever to see where the future could take us. At Hydra Host, we’re paving the way and working to ensure your right to the free and open web doesn’t ride or die on the whims of a random tech platform. With Hydra Host, we believe your future should be in your own hands.</p><p><br></p><p><a href="https://www.hydrahost.com/" rel="noopener noreferrer" target="_blank">Join us</a>. Choose liberty and not tech tyranny. The time is now to storm centralized infrastructure and seize the future we want, for the people and the free and open web.</p>

<blockquote><em>“A revolution without dancing is a revolution not worth having.” — V for Vendetta</em></blockquote>

Explore how centralized cloud hosting threatens internet freedom and how Hydra Host champions user autonomy and choice.

The dangerous remedy to the desperate disease of cloud hosting

<p>As the Co-Founder and Chief Product and Technology Officer at HydraHost, I drive the development of next-generation solutions that redefine high-performance computing and infrastructure management.</p><p>My focus is on building innovative, scalable, and efficient platforms that empower businesses to thrive in an increasingly tech-driven world. Alongside my role at Hydra Host, I am also a senior technical advisor for crypto and Web3 at the <a href="https://www.thefai.org" rel="noopener noreferrer" target="_blank">Foundation for American Innovation</a> (FAI), where I contribute to shaping policies and solutions that merge liberty with technological advancement.</p><p>Before co-founding Hydra Host, I served as Head of Engineering at FAI, where I oversaw the development of groundbreaking projects that align policy and technology. My career in crypto began as a writer for prominent platforms like CoinDesk and Bitcoin Magazine, where I shared insights on blockchain, cryptocurrency, and decentralized systems. My work as a Henry Hazlitt Fellow at the <a href="https://www.federationofeconomic.org" rel="noopener noreferrer" target="_blank">Foundation for Economic Education</a> deepened my commitment to exploring the intersection of technology, economics, and individual liberty.</p><p>Throughout my career, I've remained dedicated to innovation in the blockchain and Web3 spaces. My publications, such as 'Lessons from a Cryptocurrency Hack' and 'Compute Maximalism: The Symbiosis Between Bitcoin Mining and AI', reflect my passion for exploring the practical applications and future potential of these technologies.</p><p>Learn more about my work and connect with me on LinkedIn, Crunchbase, or explore my profiles on FAI and Medium.</p>

Ariel Deschapell

<p>Hydra Host, a leader in high-performance compute infrastructure, has officially launched the industry’s <strong>first interruptible GPU instances for bare metal deployments,</strong> delivering flexible, cost-efficient compute options tailored for AI and machine learning teams operating at scale.&nbsp;</p><p>&nbsp;</p><p>Unlike traditional interruptible instances limited to virtualized cloud environments, Hydra’s solution offers the same raw performance and control of bare metal, with the added benefit of <strong>up to 50% cost savings</strong> for workloads that can tolerate preemption.&nbsp;</p><p>&nbsp;</p><p>“We built interruptible bare metal to achieve maximum utilization for our <a href="https://hydrahost.com/ai-factory/" rel="noopener noreferrer" target="_blank">AI Factory</a> partners, while giving AI Platforms more options and flexibility in available plans and pricing,” said Ariel Deschapell, CTO of Hydra Host. “This is the first time interruptible logic has been applied to the GPU server layer itself, not just abstracted through cloud virtualization.”&nbsp;</p><p>&nbsp;</p><p>Hydra Interruptible is ideal for teams running:&nbsp;</p><ul><li>Cost-sensitive inference workloads&nbsp;</li><li>Kubernetes container orchestration or serverless&nbsp;</li><li>Distributed training runs&nbsp;</li><li>Benchmarking or burst experimentation&nbsp;</li><li>Low urgency but price sensitive compute workloads, like batch or Bitcoin seed phrase recovery&nbsp;</li></ul><p>&nbsp;</p><p>This feature adds to Hydra’s growing suite of infrastructure tools aimed at modernizing GPU deployment for AI builders and operators. With global deployments across <strong>40+ partner data centers</strong> and <strong>over 20,000 GPUs under management</strong>, Hydra is building the foundation for the next generation of AI infrastructure—flexible, distributed, and designed for <a href="https://hydrahost.com/brokkr-for-data-centers/" rel="noopener noreferrer" target="_blank">monetization</a>.&nbsp;</p><p>&nbsp;</p><p>Hydra customers interested in being one of the first to access interruptible reservations can contact <a href="mailto:sales@hydrahost.com" rel="noopener noreferrer" target="_blank">sales@hydrahost.com</a>.&nbsp;</p>

Hydra Host launches the first interruptible bare metal GPU instances, offering cost-efficient compute for AI workloads, inference, and short-term training jobs. 

Hydra Host Launches First Interruptible Bare Metal GPU Servers, in Beta for select partners 

<p><strong>The Cloud’s TSMC Moment: Why The Largest Neoclouds Won't Own Hardware</strong>&nbsp;</p><p><br></p><p>In 1987, the founding of Taiwan Semiconductor Manufacturing Company (TSMC) began a fundamental shift in the semiconductor industry by<a href="https://www.tsmc.com/english/aboutTSMC/dc_infographics_foundry#:~:text=computers%2C%20automotive%20electronics%2C%20the%20Internet,and%20have%20significantly%20improved%20our" rel="noopener noreferrer" target="_blank"> pioneering a “pure-play” foundry model</a>. Prior to this, industry heavyweights like Intel, Texas Instruments, and NEC Semiconductor were vertically integrated device manufacturers (IDMs), handling both integrated circuit (IC) design and manufacturing. TSMC changed that by specializing exclusively in manufacturing, enabling a new generation of fabless startups like NVIDIA to outsource this capability and specialize solely in IC design.&nbsp;</p><p><br></p><p>The decoupling of the semiconductor business has had enormously disruptive consequences. Today, NVIDIA is the largest semiconductor firm by market capitalization and stands at 30x the capitalization of Intel. TSMC itself is 7x the size of Intel, and has become one of the most valuable and critical companies in the world, responsible for supplying virtually all of the cutting edge chips for giants such as NVIDIA and Apple.&nbsp;</p><p><br></p><p>This specialization has facilitated enormous progress on both sides of the industry, which has directly led to the advancements which have facilitated the current AI boom. Now because of that same boom, similar economics are facilitating another decoupling and disruption further up the value chain.&nbsp;</p><p>Today, cloud computing is experiencing the same critical threshold of complexity and increasingly divergent capital requirements that drove the decoupling of IC design and manufacturing in the late 80’s and 90’s. In the future most clouds, and eventually even the largest, won’t own any hardware at all.&nbsp;</p><p><br></p><p><strong>Understanding the Decoupling: Moore’s First and Second Laws</strong>&nbsp;</p><p><br></p><p>Most are familiar with Moore’s Law: the number of transistors on integrated circuits doubles roughly every two years. However, Moore’s second law, less widely known but equally important, states that the cost of building state-of-the-art semiconductor foundries doubles approximately every four years.&nbsp;&nbsp;</p><p>This exponential growth in capital requirements, and the parallel growth of design sophistication, naturally set the stage for decoupling and specialization. Companies found it increasingly difficult to master both ever more sophisticated chip design and the extraordinary complexity needed to manufacture them at scale cost effectively.&nbsp;</p><p><br></p><p>Today, an almost identical dynamic is emerging in cloud computing. Cloud giants like AWS, Google Cloud, and Azure are vertically integrated, managing both massive server infrastructure and developing sophisticated software stacks on top of their own hardware. These big three are all doubling down on this strategy and attempting to take it even a step further by also designing their own in-house AI chips (<a href="https://www.wired.com/2016/05/google-tpu-custom-chips/#:~:text=This%20morning%20at%20Google%20I%2FO%2C,transforming%20the%20Google%20search%20engine" rel="noopener noreferrer" target="_blank">such as TPUs</a>) to further verticalize their stack.&nbsp;</p><p><br></p><p>The first generation of AI-centric clouds (or “neoclouds”), like Lambda Labs, roughly follow this same vertically integrated playbook by building their own hardware capacity while pursuing their own abstracted software platforms in tandem. But because of increasingly similar economic pressures, this model stands to be disrupted, and efforts to vertically integrate even further stand to have massive blow back. The first to realize and execute on this effectively will be the ultimate winners just as TSMC and NVIDIA are today.&nbsp;</p><p><br></p><p><strong>The Emergence of “Pure-Play” AI Factories</strong>&nbsp;</p><p><br></p><p>The accelerating sophistication and cost of GPU-powered data centers (referred to as<a href="https://blogs.nvidia.com/blog/dell-technologies-world-2024/#:~:text=Huang%20discussed%20the%20concept%20of,valuable%20data%20tokens%20distributed%20globally" rel="noopener noreferrer" target="_blank"> AI Factories by Jensen</a>) parallel the early history of semiconductor manufacturing facilities. Take the A100 GPU from NVIDIA which had an initial starting price of $20,000. Roughly 5 years later, the price of the soon to be released B200 GPU is close to $40,000. The power draw, meanwhile, has roughly quadrupled from 250W to 1000W, accelerating challenges in supporting infrastructure and operations. This rate of growing capex and complexity closely mirrors the dynamics of foundries in Moore’s second law.&nbsp;</p><p>This isn’t the only pressure driving decoupling. As the cost and complexity of HPC infrastructure and AI Factories continue to escalate, so too does the pace of change in the world of software applications and tooling.&nbsp;</p><p><br></p><p>As AI-driven software development accelerates, software iteration cycles will shorten dramatically, demanding frequent updates, adaptations, and specialized features to support its continued improvement and growth. If the rate of increasing costs and complexity of AI Factories seems to parallel that of foundries, then the speed of software iteration cycles seem poised to mirror the rapid progress of integrated circuit capacity in Moore’s first law.&nbsp;</p><p><br></p><p>Though this is innately harder to measure, given recent advancements it doesn’t seem outrageous to suggest that software iteration speeds are on pace to roughly double every two years. Compare the state of the art in early 2025 to early 2023: Tools such as Cursor and Replit are now able to leverage current frontier models to reduce the barrier of entry for software development to a level never seen before. They enable the completion and deployment of working app POCs with a handful of prompts, while also turbocharging the abilities of experienced software developers. An<a href="https://github.blog/news-insights/research/the-economic-impact-of-the-ai-powered-developer-lifecycle-and-lessons-from-github-copilot/#:~:text=quantitative%20research%20study%20that%20developers,for%20productivity%E2%80%99s%20sake%20means%20nothing%E2%80%94we" rel="noopener noreferrer" target="_blank"> analysis by Github in May 2024</a> showed a 50% increase in developer productivity from using Copilot. These tools and capabilities are the worst they will ever be today.&nbsp;</p><p><br></p><p>For the software side of the cloud business, this means demand for tooling and infrastructure to launch, manage, and produce this software will only continue to accelerate just as their own ability to iterate quickly on their own platforms accelerate as well. If they don’t, then their competition surely will instead.&nbsp;</p><p><br></p><p>Just as AMD eventually<a href="https://arstechnica.com/gadgets/2008/10/and-then-there-was-one-amd-spins-off-foundry/" rel="noopener noreferrer" target="_blank"> spun out their foundry business</a> to go fabless despite fierce initial resistance ("<a href="https://clausaasholm.substack.com/p/real-men-have-fabs" rel="noopener noreferrer" target="_blank">real men have fabs</a>"), cloud service providers will begin to realize that AI hardware investment and cloud software development require fundamentally different expertise, investment profiles, and intense areas of focus.&nbsp;</p><p><br></p><p><strong>Unlocking a New Generation of Neoclouds</strong>&nbsp;</p><p><br></p><p>Future neoclouds will be forced to specialize exclusively in software and user experience to beat and stay ahead of the competition, by offloading hardware investment and management to dedicated AI Factory providers. These AI Factories will function similarly to TSMC, by taking on the burdens of intense capex and reducing costs by sheer economies of scale. They will provide access to state of the art infrastructure through flexible bare metal APIs, freeing neoclouds to invest their resources entirely into differentiated user experiences and software innovation at higher levels of abstraction up the cloud stack.&nbsp;</p><p><br></p><p>Such AI Factory businesses will face early challenges similar to those TSMC encountered, which lacked large obvious early customers as the major semiconductor firms already possessed in house foundries. But this didn’t particularly concern founder and CEO Morris Chang. Chang had previously served as an executive at Texas Instruments, where he worked with numerous engineers who dreamed of one day launching their own chip companies. Raising the capital to do this was extremely prohibitive however, with the cost of building an in house foundry quickly escalating into the hundreds of millions.&nbsp;</p><p><br></p><p>By creating an independent foundry that they could outsource to, Chang reduced the barrier of entry substantially, enabling a generation of fabless chip startups to enter the market without prohibitive infrastructure investment needs. In a rare interview on the Acquired podcast, Morris Chang recollected the first time a then nearly bankrupt NVIDIA had reached out to him in the 90s: “I had always told our sales people we should never be negligent in talking to future customers, even if the customer seemed to be a very small one”.&nbsp;</p><p><br></p><p>This approach paid off. NVIDIA, Qualcomm, Broadcom, Mediatek, P.A. Semi (acquired by Apple to form their in house chip division) and countless others flourished and reshaped the industry precisely because they were able to outsource to TSMC and other foundries adopting the pure-play foundry model.&nbsp;</p><p><br></p><p><strong>Reducing Barriers to Entry</strong>&nbsp;</p><p><br></p><p>The same economic headwinds are now pushing upstream full force for cloud platforms. The capital and expertise required to operate GPU clusters and AI Factories are quickly becoming prohibitive. In parallel, the demands for the software side of the cloud business are only beginning their dramatic acceleration as well.&nbsp;</p><p><br></p><p>Neoclouds will have to keep up with the demand for ever better and more expansive tooling as the barriers for producing software continue to decrease. Those that are able to focus purely on software and customer-facing innovation are poised to be far more successful, given they no longer need to shell out capital, time, and focus on an underlying hardware business.&nbsp;</p><p><br></p><p>The emergence of dedicated AI Factories that can enable this will dramatically lower barriers to entry just as TSMC did, spawning new specialized cloud companies, each innovating faster and more creatively than today’s vertically integrated providers. Among them, several companies will inevitably scale into formidable players, reshaping the competitive landscape and potentially disrupting established hyperscalers just as Intel is nearly an afterthought today.&nbsp;</p><p><br></p><p>TSMC's choice to specialize transformed the semiconductor industry and, with it, the world. The cloud industry’s TSMC moment is now happening as we speak, as a direct consequence of the previous specialization in the semiconductor industry. Vast AI, Runpod, Salad, and SF Compute are just a handful of startups attempting to build software only cloud businesses on top of bare metal providers, while my company Hydra Host is taking the approach of enabling independent data-centers to transform into specialized AI Factories with our turnkey bare metal solution and bundled design, procurement, and financing services.&nbsp;</p><p><br></p><p>The decoupling is underway. The only questions are how quickly will it play out, and will the hyperscalers be able to adapt? Or will they become the new generation of IDMs like Intel and NEC Semiconductor before them?</p>

TSMC's choice to specialize transformed the semiconductor industry and, consequently, the world. The cloud industry's TSMC moment is occurring now, driven by semiconductor specialization. Companies like Vast AI, Runpod, Salad, SF Compute, and Hydra Host, which transforms independent data centers into specialized AI Factories, exemplify this shift. 

The Cloud’s TSMC Moment: Why Neoclouds Won't Own Hardware

<p>Recent chip export restrictions designed to deny advanced hardware to the Chinese Communist Party have swept up several other countries in the process. Most notable are the Gulf States of the Middle East- such as the UAE, Saudi Arabia, and Kuwait.</p><p>With the rapid rise in popularity of Deepseek (almost certainly trained on restricted H100s), these export rules are facing renewed scrutiny. Enforcing direct prohibitions on sales to China is simple, but preventing subsequent resale through other jurisdictions is far more complex. To address this, the heavy-handed approach used by the previous Biden administration severely limited exports to many partner and neutral countries- a policy that remains leaky and ultimately harms U.S. interests. To understand why requires asking a simple question.</p><p><br></p><p><strong>Do we want the rest of the world to buy American or Chinese?</strong></p><p><br></p><p>Our restrictive export measures effectively subsidize the Chinese chip industry by knocking American competition out of key markets and pushing neutral or friendly nations deeper into China’s technological and economic orbit. It’s a case of cutting off our nose to spite our face.</p><p>Fortunately, the choice is not between accepting the current policy or abandoning all restrictions on critical hardware altogether. We can apply better technological solutions to enable <em>effective</em> export controls without inflicting so much collateral damage—and we must do so swiftly.</p><p><br></p><h2>The Gulf States: Key to the Compute Race</h2><p><br></p><p>No place illustrates this policy dilemma more clearly than the Arabian Gulf. Historically renowned for its strategic value- from ancient trade routes to its modern status as the nucleus of global oil production- the Gulf region is on the verge of becoming a central player in AI infrastructure.</p><p><br></p><p>Countries like Saudi Arabia, the UAE, Kuwait, Oman, and Qatar have invested heavily in power generation and internet infrastructure. As a result, they not only surpass the baseline for AI development but could become the most consequential partners worldwide in the emerging “compute race.” Like the transition from early gasoline engines to mass-market automobiles where every technological leap fueled even higher demand for oil- each new AI breakthrough (à la Deepseek) will require exponentially more compute power and thus more energy to run advanced workloads at scale. All this means that raw energy output is quickly becoming the limiting factor for developing high-end compute. The United States has already tapped most “low-hanging fruit” sites for data centers, forcing some hyperscalers like AWS and Azure to even invest in <a href="https://www.datacenterdynamics.com/en/opinions/hyperscaler-nuclear-power-ambitions-heat-up/" rel="noopener noreferrer" target="_blank">restarting nuclear plants.</a></p><p><br></p><p><strong>In this context, the Gulf States’ energy capacity stands out dramatically.</strong> As of 2023, Saudi Arabia, the UAE, and Kuwait collectively produce around 16 million barrels of oil per day- nearly 17% of global output. Their proven reserves are even higher with plenty of headroom to ramp up production. In addition to fossil fuels, these nations are pouring resources into renewable energy projects such as solar further expanding their net energy surplus. As a whole, the Gulf region is the largest net exporter of energy in the world, making them ideal partners for large-scale AI endeavors that demand vast, stable power supplies.</p><p>In the near term, it will become increasingly apparent the Gulf region will rapidly become the most consequential provider of AI infrastructure capacity just as it is the most important producer of oil today.</p><p><br></p><p><strong>The only question is with</strong> <strong>whom will it partner with to develop this capacity: American businesses or the rapidly growing Chinese tech sector.</strong> Currently, the U.S. leads in advanced chip capabilities, but if we continue to withhold those capabilities from a region with such vast potential, China will not hesitate to seize the initiative to boost its own capabilities.</p><p><br></p><h2>A Technology Problem</h2><p><br></p><p>So how can we enable key international partners effective access to American chips and hardware, while ensuring they do not ultimately make their way to China once delivered?</p><p><br></p><p>Simple. By bundling deployments of restricted hardware with tailored DC management software like Hydra Host's Brokkr platform, we can ensure that delivered GPUs are actually installed and remain on site in the intended destination country. <strong><u>The Brokkr platform provides a live source of truth for all these deployments to OEMs and other stakeholders, providing ongoing and automated compliance monitoring</u></strong>. If GPUs are disconnected from the authorized data center site in which the software is installed, it triggers an immediate alert providing the assurance necessary to relax export concerns.</p><p><br></p><p>By implementing smarter policies and leveraging targeted technology to allow Gulf States to purchase American hardware without the risk of resale leaks, we can strengthen U.S. influence, bolster domestic chipmakers, and maintain our technological edge—all while countering China’s growing presence in a region critical to the future of AI.</p><p><br></p><p>This approach doesn’t mean abandoning security or ignoring export controls; it means using more nuanced tools to deny our most advanced technology to adversaries without undermining partnerships and ceding these markets to China.</p>

The Middle East is set to become a global AI hub, but US chip restrictions could push it toward China. Learn how smarter policies & Brokkr tech can secure US interests.

Advancing American Interests with GPU Exports to the Middle East

<p>In the 1950s, McDonald’s revolutionized the fast-food industry, expanding from a single restaurant to over 200 locations in just five years. This kind of growth was unprecedented, and the secret lay in Ray Kroc’s now ubiquitous innovation: franchising.</p><p><br></p><p>Kroc created a system to empower capable entrepreneurs with their own capital to scale what worked and piggyback on the parent company's brand credibility, expertise, resources, and distribution. The franchise model enabled rapid expansion while maintaining standardized operations, procurement, and delivery. Across industries ranging from dining to automotive care to shipping and logistics, the franchise model has since devoured many smaller competitors who simply lacked the scale and flexibility to compete.</p><p><br></p><p>Today, a similar transformation is playing out in real time for AI factories—high-performance computing (HPC) data centers optimized for artificial intelligence workloads. These GPU-focused facilities, the backbone of AI development, face challenges that echo those of early McDonald’s: high capital needs, ever-changing operational complexity, and scalability bottlenecks. Here's how franchising can offer the same breakthrough for AI factories as it has in many other industries.</p><p><br></p><h3>The Challenge of Monetizing AI Factories</h3><p>Building an AI factory is no small feat. These data centers require significant capital outlay for real estate, infrastructure, and high-performance servers. And that's only the beginning. Once the facility is operational, the real challenge emerges: distribution and monetization.</p><p>For most data center operators, setting up hardware is the easy part. Creating and deploying an entire cloud platform so customers can actually utilize that hardware, and acquiring those customers, present an entirely new set of challenges that's wholly alien to the traditional data center industry. As a result, even well-funded data centers often struggle to attract sustained demand or stand out in an increasingly crowded market.</p><p><br></p><p>A recent example highlights this reality: data center Equinix acquired Packet, a promising bare metal cloud company, for $335 million in 2020. Despite high expectations, Equinix recently announced the sun-setting of the renamed Equinix Metal Cloud offering to refocus on its core colocation business.</p><p>The Packet acquisition and subsequent scale up investment by Equinix failed, resulting in a massive write-off. The example of Equinix Metal underscores the complexities of running a cloud business alongside a traditional data center business. Each has vastly different operational and customer needs, and even a company with Equinix's vast resources couldn't effectively bridge the gap.</p><p>This presents a critical issue. The global AI ecosystem benefits from decentralization, where a diverse range of independent data centers can ensure competition, innovation, and resilience. However, without a scalable and replicable growth model, the industry risks GPU power becoming concentrated in the hands of a few dominant players, stifling competition and creating chokepoints for innovation. That is of course, much like the traditional cloud.</p><p><br></p><h3>Franchising as a Solution</h3><p>Franchising offers entrepreneurs the opportunity to be at an industry's cutting edge <em>day one</em>. Just as McDonald’s created a template for entrepreneurs to replicate its operations, AI factory franchising will provide a standardized blueprint for data center expansion and operations using near-turnkey software and ongoing full stack support.</p><p><br></p><p>Through a franchise model, individual operators and investors can deploy their expertise and resources in developing their own AI Factory projects, while benefiting from shared procurement operations, financing relationships, software infrastructure, distribution channels, and of course an existing customer base.</p><p><br></p><p>Contrast this with today's non-standardized approach, wherein aspiring data center operators must reinvent the wheel and spend precious time and resources rediscovering existing best practices to finance, procure, and monetize their chips.</p><p><br></p><p><strong>Here's how Hydra Host is pioneering data center franchising:</strong></p><ol><li><strong>Faster, Cheaper Infra Procurement:</strong> Small, independent data centers often struggle to acquire the latest GPU models as quickly as their large cloud company competitors. When they do get a quote, they often lack the leverage needed to access fair pricing. Hydra takes care of pricing leverage, providing small data centers the benefits of scale instantly. Through existing relationships with companies like NVIDIA and various infrastructure and OEM partners, Hydra Host aggregate orders and passes those benefits directly to our DC partners.</li><li><strong>Easy-to-Use, Standardized Infrastructure</strong>: Hydra Host's Brokkr platform features a bare metal management and provisioning system, providing a source of truth for server management and status, as well as a way to monetize bare metal servers in a secure and automated way. Bare metal skips the need for a hypervisor or containerized abstraction layer, reducing complexity and increasing stability and performance, making it a premium choice for AI model and platform companies. It also means servers on Brokkr are compatible and can be pushed to third party marketplaces, such as Runpod and <a href="http://salad.com/" rel="noopener noreferrer" target="_blank">salad.com</a>, to supplement Hydra driven demand when needed.</li><li><strong>Centralized Distribution:</strong> Creating a competitive platform is one of the biggest hurdles independent data centers face. Brokkr takes care of this and acts as a unified user interface and API for all of our DC partners, delivering end customers a reliable, centralized source of supply with a predictable and standardized purchasing experience.</li></ol><p><br></p><p>Hydra Host solves provisioning for its clients, handling utilization and passing along the benefits of a its standard and ecosystem so data centers only have to concern themselves with on-premise day to day operations and maintenance.</p><p><br></p><ol><li><strong>Outsourced Customer Acquisition and Sales</strong>: Just as McDonald’s centralized its branding and marketing, Hydra is constantly sourcing paying customers and funneling clients to data centers integrated with Brokkr. Individually, independent operators can't compete with industry giants long term. But as part of the Brokkr umbrella and ecosystem, data centers can combine to quickly scale to more locations and provide more diverse offerings and configurations than any centralized cloud provider can hope to match.</li><li><strong>Financing</strong>: Hydra Host exploits its visibility into compute and hardware pricing, as well as its procurement channels, to provide its clients advantageous and rapid access to equipment financing. Hydra's finance partners can underwrite with greater precision, confidence, and speed than any other financiers in the space. This means Hydra's data center clients enjoy better and faster financing than they could access anywhere else.</li></ol><p><br></p><h3>Why It Matters</h3><p>Franchising AI factories wouldn’t just enable faster growth; it could democratize the entire AI infrastructure market. A distributed network of AI factories, each independently owned but operating under a standardized system, could help ensure that GPU power isn’t concentrated in a handful of companies or geographies, and enable much more experimentation and innovation.</p><p><br></p><p>Franchising allows investors and operators to scale what works instead of reinventing the wheel. Data centers are overdue for a turnkey, just-add-capital approach allowing them to focus on scaling instead of fighting for relationships and rediscovering what their competitors already discovered years before.</p><p><br></p><h3>The Road Ahead</h3><p>Franchising isn’t a silver bullet, and there are significant challenges to overcome. The balance between standardization and local flexibility can be tricky to manage.</p><p><br></p><p>However, the potential benefits are significant. Franchising has historically demonstrated its power to improve the odds of success in competitive industries. For example, while only ~<strong>20% of independent restaurants</strong> survive beyond five years, <strong>top franchises like McDonald’s boast survival rates of over 90%</strong> in the same timeframe.</p><p><br></p><p>AI factories will follow a similar dynamic. Standalone data centers often face steep challenges, including building platforms, acquiring customers, and navigating competitive pressures. A franchising model for AI factories delivers the same advantages franchises provide in other industries: a proven playbook, operational support, and the strength of a unified brand. By empowering local entrepreneurs with these tools, franchised AI factories unlock a path to greater success, ensuring more resilient and distributed AI infrastructure worldwide.</p>

Franchising AI factories wouldn’t just enable faster growth; it could democratize the entire AI infrastructure market. A distributed network of AI factories, each independently owned but operating under a standardized system, could help ensure that GPU power isn’t concentrated in a handful of companies or geographies, and enable much more experimentation and innovation. 

AI Factories In A Box: Why Franchising is the Future of Data Centers

<h2>Brokkr | Hydra Host's High-Performance Computing Marketplace</h2>

<p>Today the Hydra Host team is excited to announce <strong>Brokkr</strong>, a two sided marketplace for high-performance computing. The Brokkr platform aims to connect users with an extensive network of computing resources, offering unparalleled optionality and flexibility when it comes to compute heavy workloads. From scientific research and data analytics to artificial intelligence development and simulations, Brokkr matches users with the power they need to tackle complex tasks efficiently and effectively.&nbsp;</p><p><br></p><p>"Brokkr" is a reference not only to the platform's function as a compute brokerage or marketplace but also to the mythological Norse figure, known as a skilled blacksmith and metalworker. This intentional terminology emphasizes Hydra Host's focus in designing and building Brokkr: <strong>enabling the provisioning of bare metal servers from any data center, anywhere.&nbsp;</strong></p><p><br></p><p>By addressing bare metal provisioning, Brokkr paves the way for aggregating compute resources from providers worldwide, irrespective of size, location, or existing provisioning or API availability. For end users, this translates to a gateway and the first genuine marketplace for compute. For data centers, it enables them to monetize and optimize infrastructure utilization by joining the Brokkr network with no need for provisioning expertise or marketing prowess.</p>

<h2>Understanding the Benefits of High-Performance Computing with Brokkr</h2>

<p>High-performance computing is swiftly emerging as a vital instrument across industries, from scientific research and engineering to financial modeling and artificial intelligence. Yet, these computing resources are capital-intensive, and sourcing, acquiring, and maintaining them can be an intimidating undertaking for many organizations. In practice, this often means navigating disparate supply chains and providers or becoming excessively dependent on a single provider. Brokkr introduces a novel approach by furnishing a centralized marketplace where users can effortlessly access and provision high-performance computing resources.</p><p><br></p><p>Brokkr allows you to harness a growing network of providers and data centers to find the best match for your specific computing needs. Regardless of the provider you choose, there's no need to concern yourself with managing multiple accounts, billing, or learning and integrating new APIs. Hydra Host standardizes all these aspects to minimize operational overhead and ensure a smooth and vendor-neutral experience for users.</p>

<p>Hosting has been largely monopolized by a few major hyperscaler providers for over a decade. However, in the past few years, the landscape has been changing. A rapidly evolving digital environment has amplified the need for dramatically more compute, and compute in more places than ever before. From the unprecedented demand for cost-effective GPUs for AI/ML workloads to an escalating focus on edge computing and optimized latency, the hyperscalers are struggling to keep pace.</p><p><br></p><p>This situation stems from the inherent unsustainability of the hyperscaler model, which has been living on borrowed time. By creating isolated ecosystems and maximizing vendor lock-in, they've endeavored to insulate themselves from competition and camouflage this simple reality: <strong>compute is a commodity, and ought to be treated as such. </strong></p><p><br></p><p>The Brokkr platform seizes this evolving shift in the industry by offering a distributed alternative to the hyperscaler model, thereby enabling independent providers with unique insights and localized advantages to bring them to the table. With Brokkr, users seeking the highest performance, lowest cost, or least latency compute for their respective use cases will be able to discover and provision it seamlessly, from anyone and anywhere.</p>

<p>The key to Brokkr lies in our growing network of data center partners. While there are a plethora of multi-cloud platforms and hosting solutions available, none of them permit independent data centers to easily offer and monetize their resources. Brokkr bridges this gap by incorporating bare metal provisioning capabilities, an essential step towards commoditizing compute. From the outset, our focus has been to support independent data centers, enabling them to concentrate on their core competency, i.e., setting up and maintaining physical hosting infrastructure, by resolving provisioning and customer acquisition challenges.</p><p><br></p><p>Through our dialogues and industry research, we've interacted with several independent data centers and gained a first-hand glimpse at emerging industry trends poised for acceleration. This includes the modular DCs established and managed by one of our partners, EnergyX. Their approach allows for industry-leading construction and setup times, and the flexibility to deploy to any location with superior economic factors, such as latency, power cost, or environmental cooling. Brokkr aspires to make innovative approaches like EnergyX's accessible and transfer the advantages they offer over traditional hyperscaler resources directly to end users. If you're a data center or a supplier of computing, we welcome you to discuss listing your resources on Brokkr.</p>

<p>Brokkr is still in its initial stages, and there's lots of work to be done. We're looking for early testers to help us iterate quickly and effectively. Below is a list of features currently on our roadmap, which you can expect in the comings weeks and months:</p><p><br></p><ul><li>A growing list of inventory from our hardware providers</li><li>Fine-grained filtering and search capabilities to allow users to easily find the specific compute resources they need based on performance, cost, and latency requirements.</li><li>Supply chain visibility: It's no surprise GPUs are in high demand, with most high-end models like the new H100 being sold out before they hit the import docks. Currently, buyers of high-end computing resources face challenges in tracking the supply chain and availability of these resources, and often have to do so by juggling several email threads and contacting multiple vendors. We aim to address this problem in Brokkr by enabling suppliers to list upcoming inventory available for pre-order and enabling interested buyers to browse this upcoming inventory all on one centralized dashboard for shopping and pre-ordering.</li><li>Terraform provider: In addition to our web interface and API, we are developing a Terraform provider for Brokkr. This will allow users to easily provision and manage their compute resources using modern infrastructure as code patterns.</li><li>Advanced billing dashboard: We understand that managing and tracking billing can be a complex task, especially in the world of high-performance computing. But it doesn't have to be. With Brokkr we are working to provide an easy-to-understand high-level billing experience, with the ability to zoom in to any desired level of granularity for accurate tracking and analysis. Compare and project compute resource and pricing options, and gain insights into resource utilization and costs, all in one comprehensive dashboard.</li><li>Secure boot and other security add ons: To ensure the highest level of security for our users, Brokkr will provide secure boot capabilities and other features to ensure the hardening of critical workloads regardless of where they are deployed.</li><li>Spot market: Utilization is one of the biggest problems when it comes to high performance compute. High end hardware is a massive investment, and making sure the most is being made out of it is critical. That's why Hydra Host is planning on extending Brokkr with a spot market for interruptible workloads, tailored to high end GPUs, in 2024.&nbsp;</li></ul>

<h2>Getting Started: How to Join the Brokkr Community</h2>

<p>For both data centers and end users, all Brokkr usage requires manual on-boarding with the aim of working closely with users to identify potential bugs and gather feedback for further improvement. Whether you're a data center or end users, you can get started by registering below.</p>

<p>The Hydra Host team is looking forward to rapidly improve on the Brokkr platform, and add more compute partners and inventory. Be among the first with us on this journey, and enjoy limited time access to extremely cost effective Hydra managed CPU resources, and Founder level engagement and support.&nbsp;</p>

Discover Brokkr by Hydra Host—a marketplace connecting users with high-performance computing resources for AI, data analytics, and more.

Introducing Brokkr

<p><br></p><p><br></p><p>It is difficult to overemphasize how much “The Cloud” has become a mainstay of the Silicon Valley zeitgeist since its meteoric ascendancy from 2008 onward.</p><p><br></p><p>Consequently, it may seem like an impossibly far-fetched idea that the growth and evolution of the cloud was a phenomenon as artificial as the interest rates which fueled it. The public cloud has witnessed roughly 2350% growth between the years 2008 and 2020, with even more headwind made immediately following the COVID-19 response and global lock downs.</p><p><br></p><p>This particular spike in growth has been reasonably traced to the skyrocketing demand for digital services from an unprecedentedly shut-in population. But here’s something else this period had in common with the initial 2008 takeoff: historically low interest rates.</p><p><br></p><p><strong>Federal Funds&nbsp;Rates</strong></p><p><br></p><p>Since the 2008 financial crisis, central banks around the world have maintained historically low interest rates to stimulate economic growth and recovery. These artificially low interest rates have had an expansive range of effects on the economy, including influencing the growth and evolution of the public cloud.</p><p><br></p><p>Recently, interest rates have experienced a sudden and sharp reversal. Correspondingly, the decade-long trend of ceaseless migration of digital infrastructure to hyperscalers has also witnessed its first major slowdown and vocal push backs. This article will explore the causative connection between low interest rates and the expansion of public cloud services. Furthermore, we’ll be discussing the alternatives to the public cloud paradigm that are poised to surge in growth, and what that means for various players in the industry.</p><p><br></p><p><strong>Interest Rates and the Public Cloud Growth</strong></p><p><br></p><p>The tech industry does not operate in a vacuum and is heavily influenced by macroeconomic forces in simultaneously subtle and dramatically impactful ways.</p><p><br></p><p>We’ve already pointed at the correlation between the rapid growth of the cloud and historically low interest rates beginning in 2008. But to explore the causative mechanisms at play between these concepts, we need to ensure we understand the economic backdrop of the cloud’s development.</p><p>First and foremost, interest rates represent the cost of borrowing money, which is ultimately determined by the Federal Reserve and other central banks worldwide.</p><p><br></p><p>When interest rates are pushed artificially low, liquidity is abundant and inexpensive, resulting in both profound and non-obvious downstream impacts on the behaviors and trends of entire economic sectors. In such conditions, companies and investors are strongly incentivized to pursue riskier investments and are more likely to take on debt and fundraising to finance their ventures. The market begins optimizing for growth, often and even inevitably at the cost of capital efficiency, fiscal discipline, and long-term strategic planning.</p><p><br></p><p>This is due to the reality that if a company is not leveraging the lower interest rates and increased credit availability to focus on growth while their competitors are, they are unlikely to survive. Consequently, all market participants eventually adopt similar optimization strategies. This leads to the development of entire industries on shaky foundations that would have been impractical and imprudent outside of the influence of extraordinary macroeconomic conditions.</p><p><br></p><p>It’s only once conditions return to the norm, that the decision making that seemed sound and rational previously is met with a harsh dose of reality. When interest rates have been kept low for an unprecedented amount of time, it should be no surprise then that the downstream consequences too are unprecedented.</p><p><br></p><p>Most obvious amongst these consequences has been the total fragility of the financial system, as highlighted by the recent collapses of Silicon Valley Bank, Signature Bank, and First Republic Bank, in rapid response to the semi-normalization in interest rates. This is only the tip of the iceberg, and the longer interest rates remain raised, the more the economic growth of the last decade will be shown to have severe structural problems. In the case of cloud computing, there are already signs that a half a trillion dollar industry that came to maturity entirely in this easy money time period is in trouble.</p><p><br></p><p><strong>Condensing the Cloud </strong></p><p><br></p><p>What do we mean when we talk about the “The Cloud”? For most, the public cloud is defined solely by the outsourcing of hardware management to a specialized provider. Despite the adage, the cloud isn’t just someone else’s computer. To be abundantly clear, there are incredibly good reasons for companies to outsource the physical management of compute to other market players better suited to the task.</p><p><br></p><p>These decisions are rooted in the simple ideas of comparative advantage and economic specialization.&nbsp;However the major hyperscalers in the cloud oligopoly (AWS, GCP, Azure) share specific traits which define the entirety of their business models, modes of operation, and trade-offs that are far more nuanced than simply outsourcing hardware management.</p><p><br></p><p>These massive platforms derive most of their usage, stickiness, and earnings not from compute alone, but ultimately from the additional services and tools tightly bundled with that compute. This includes a vast catalog ranging from managed databases, to identity management, CDNs, managed orchestration tools like Kubernetes, and so much more.</p><p><br></p><p>In essence, the hyperscale cloud providers today are an intricate and often confoundingly complex array of proprietary tools and managed services. Why? To lock users into their platforms by bundling their software and services add-ons with their hardware. Want to switch to using a competitor’s compute resources instead? Not so fast: a cobweb of interlocking managed software dependencies now keeps you tethered to your host with no easy way out.</p><p><br></p><p>This is the cloud today: a few major walled gardens defined by the bundling of hardware, software, and services in an effort to de-commoditize what is and should be a commodity business: supplying compute.&nbsp;The resulting size, concentration, and control over digital infrastructure that these providers leverage have subsequently become a far cry from the early visions of an open and decentralized internet. This state of affairs is a direct result of the macroeconomic incentives that have been in place almost continuously since 2008.&nbsp;</p><p><br></p><p><strong>All That Glitters isn’t Gold</strong></p><p><br></p><p>The deliberately “bundled” design of the cloud offers a clear tradeoff: speed over portability and flexibility. Lack of portability results in higher fiscal costs to customers, especially long term, as these providers know moving to a competitor is no trivial matter. The more services and tools customers use from their current cloud host, the more they are disadvantaged over time as the cost of migrating to a competitor tends to only increase.</p><p><br></p><p>While using these more easily integrated tools and managed services to increase productivity and optimize for growth may have seemed like a straightforward choice to the customer it is ultimately a Faustian bargain.</p><p><br></p><p>For most players operating in an economic environment defined by unprecedentedly low interest rates, this optimization for immediate growth over longer term considerations made total economic sense.</p><p>In such a case, fundraising is easy, and as such fiscal discipline is not as valued by investors as speed to market and growth at all costs. If this sounds far fetched, believe me, it was extremely common up to early 2022 for angels and VCs to tell portfolio companies they weren’t spending money fast enough.</p><p><br></p><p>When cash is cheap, other things are simply more important, and rationally so within the context of the economic environment. The costs of increasingly large AWS bills hardly registered as long as revenue growth continued and fundraising remained extremely easy, both themselves buoyed by low interest rates driving less discretionary spending all across the economy.</p><p><br></p><p>If your competitors are leveraging the dynamics of the easy money environment, and you’re not, you’re likely dead in the water before the longer term considerations even matter. However unlike death or taxes, artificially low interest rates aren’t and can never be constant. As inflation concerns began to reach fever pitch over the last year, the Federal Reserve and other central banks have quickly reversed course by aggressively hiking interest rates once more.</p><p><br></p><p>Very abruptly, the calculus driving economic decision making has turned on its head, and the impact on many industries, like banking, have been hard and obvious. Many other’s have been heavily impacted as well, albeit less dramatically. For now. The cloud included.</p><p><br></p><p>Revenue and fundraising capabilities are both sinking in lockstep, and suddenly the abrupt need for aggressive fiscal discipline for the first time in over a decade is bringing to light certain glaringly obvious truths.&nbsp;</p><p><br></p><p><strong>The Cloud Doesn’t Actually Make Much Sense</strong></p><p><br></p><p>As it turns out, making yourself entirely reliant on a single provider with absolute leverage over your infrastructure is a really bad deal if you actually have to think long term or care about fiduciary responsibilities. That’s the conclusion many companies are rapidly coming to, a notable recent example being 37 Signals, as explained by co-founder David Hansson the creator of Ruby on Rails.</p><p>In their effort to move from the Cloud, the team at 37Signals has built MRSK, a framework to help manage higher level tooling and application orchestration on top of raw compute, completely agnostic to where that compute is hosted. This serves a crucial function: It effectively helps unbundle some of the functionality typically bundled by cloud providers, giving you greater portability and control over your software.</p><p><br></p><p>There is still a bit more additional work involved with this solution compared to ready-to-go cloud tooling to be sure, but MRSK and tools like it are relatively niche and underdeveloped, especially when compared to the vast investment the public cloud tooling has seen over the past decade plus.</p><p>These tools and the paradigm it represents offers a starkly different alternative to how the cloud works today. By simply buying or renting raw compute and running the open source infrastructure stack of your choice on top of it, an “un-bundling” occurs which undoes the position of power the cloud oligopoly have created for themselves while the industry has slept.</p><p><br></p><p>In this model, businesses can crucially still leverage more specialized third parties to manage their physical compute in the actual actual data center, and still leverage third party software to maximize their productivity, but in a portable and natively agnostic fashion.</p><p><br></p><p>In our now higher interest rate environment, that reality of cloud costs and it’s long term implications for organizational flexibility and efficiency is becoming much less palatable for a huge swath of companies all at once. As more continue to flee the lock in and corresponding costs of the cloud out of pure fiscal necessity, the tools to make this “un-bundling” movement even easier and more practical will continue to be improved upon.</p><p><br></p><p>With that in mind, we can scarcely imagine how the ecosystem of independent bare metal providers and portable infrastructure tooling might look like 10 years from now, or how they might have looked like today had there not been more than 10 years of artificially low interest rates to begin with. “The Cloud” as we know it today, may never have come to be.</p><p><br></p><p>Indeed, the fact that it did evolve in the way it did increasingly seems like the anomaly. A development that was broadly accepted but is in fact seeming more counterintuitive in hindsight by the day. In it’s stead, we are seeing the growth of a much more diverse, distributed, and commoditized pool of compute providers, upon which an ecosystem of open source and highly portable software can flourish.&nbsp;</p><p><br></p><p><strong>Unwinding The Cloud</strong></p><p><br></p><p>The problem with the growth of major cloud providers isn’t the idea that certain businesses shouldn’t specialize in running the hardware on behalf of other businesses with other priorities. The problem is actually that that specialization doesn’t go far enough. There should be a competitive industry for offering compute as a commodity, and distinct industries for orchestration, managed services, and tool chains to effectively utilize that compute, independent of where the physical hardware is located and who manages it.</p><p><br></p><p>Certainly, there will always be a market for vertically integrated hardware and software products, typically thought of as hosted platforms as a service. But if we accept that the expansive proliferation of this bundled pattern was in large part driven by unsustainable incentives derived from low interest rates, then we must conclude that the relative market for these offerings is much smaller than what we’ve seen up to this point.</p><p><br></p><p>The logical conclusion of this is a continued “un-bundling” movement, and the gradual but accelerating transformation of a more than half a trillion dollar industry. That means growth for providers of "less-strings-attached" bare metal as a service (BMaaS) and basic virtualized offerings, and vastly increased interest, attention, and development of portable software and services that can be deployed on top of any provider of compute resources.</p><p><br></p><p>This reorientation of how we build and deploy applications will have profound effects, but ultimately will likely be a huge blow to the defacto oligopoly and over centralization of infrastructure in the hands of AWS/GCP/Azure, and a big win for industry disintermediation and competition in the long run.</p><p>In the midst of ongoing economic uncertainty, we might actually be seeing the internet trending towards a more natural and distributed equilibrium as skewed incentives are removed from the markets comprising it.</p><p><br></p><p>One thing seems certain: Were it not for artificially low interest rates to begin with, the way we would use “someone else’s computer” would look very different from the cloud we see today, and much more in line with the original vision of an open, decentralized, and interoperable internet.</p><p><br></p><p><strong>Practical Takeaways </strong></p><p><br></p><p>For developers and dev ops engineers:</p><p><br></p><p>Instead of that next AWS certification, start familiarizing yourself more seriously with bare metal, and how to deploy and work with more fundamental building blocks. Frameworks like MRSK, Supabase, and others offer paths to effectively deploy to bare metal as well as VPSs in manageable ways. Demand for technical talent that can facilitate migrations to this paradigm will continue to grow rapidly.</p><p><br></p><p>For companies and organizations:</p><p><br></p><p>Are you on the cloud? If so, you’ve likely already taken a recent look at your AWS bill and are wondering how to make some improvements. It’s time to start thinking outside of the box, and ask your engineering teams to take a serious look at some of these trends and technologies mentioned in this write up and present a plan to get to higher ground. Still operating on-premise infrastructure? You might have actually dodged a bullet there, and you’re in a position to carefully plan what your future looks like. There’s plenty of options between remaining on-prem, out sourcing on-prem management, going to a BMaaS model, or some hybrid of these options.</p><p><br></p><p>For data centers and independent compute providers:</p><p><br></p><p>This is your time to shine. There’s never been a bigger opportunity to grow your business, but to do so you need to offer comparable user experiences and programmatic capabilities to the large cloud providers to make transitions easier for new customers, but otherwise simply focus on delivering superior hardware for the price. At Hydrahost.com, we’re building a data center directory, and helping our data center partners offer modern Bare Metal Self Service capabilities with easy to use UIs and robust API. Contact us to learn more.</p>

Discover how low interest rates accelerated cloud adoption and the challenges emerging as rates climb, reshaping digital infrastructure strategies.

The Cloud: A Low Interest Rate Phenomena 

<p>In software, everyone wants to be in on the Next Big Thing™. Of course, identifying that Thing can be tricky. Today, Hydra is doing that work for you. In this primer we’ll cover:</p><p><br></p><ul><li>What is Sky Computing, and why it’s quickly emerging as the Next Big Thing</li><li>Sky Computing use cases, and how it compliments other trends such as AI/ML</li><li>How it works, the different implementation approaches, and their trade-offs</li><li>How industry adoption is likely to play out</li><li>The early companies and projects, like Hydra Host, poised to be early major players</li></ul><p><br></p><p>Whether you’re a software developer, platform/DevOps engineer, or a business owner, if you rely on the cloud and want to keep a competitive edge, being early to the Sky Computing discussion is a must.</p><p><br></p><p>The goal of this article is to provide a high-level primer on this subject and its ongoing emergence, with actionable starting points on how to get in on the ground floor to the benefit of your career, business, and customers.</p><p><br></p><h2>What even is this Thing</h2><p><br></p><p>“Sky Computing” registers under 1,000 monthly searches on Google, so what is it, and why are we certain it’s not just a Thing, but poised to be a Big Thing?</p><p><br></p><p>The term was cemented relatively recently in a paper titled <a href="https://arxiv.org/ftp/arxiv/papers/2205/2205.07147.pdf" rel="noopener noreferrer" target="_blank">“The Sky Above the Clouds: A Berkley View on the Future of Cloud Computing” by Ion Stoica and company</a>, and is meant to evoke a radical extension and enhancement of today’s cloud computing landscape.</p><p><br></p><p>As the name suggests, the central idea of Sky Computing is users should be able to leverage all different clouds and other resource providers, whether major hyperscalers, independent datacenters, or edge compute, as easily as they’re able to leverage a single cloud provider. Rather than being reliant on any single cloud, the entire skyscape of computing should be at all of our fingertips.</p><p><br></p><p>It’s worth noting that there are competing terms being used to describe the same ideas, including <a href="https://siliconangle.com/2022/05/21/supercloud-becoming-thing/" rel="noopener noreferrer" target="_blank">“Supercloud” and even “Metacloud”</a>. None have a clear edge yet, but Sky Computing has the benefit of being a completely novel term without any of the potential baggage of previous associations. The “Sky Above The Clouds” paper also does the best job of comprehensively describing the technology and challenges so far.</p><p><br></p><h2>The Problem</h2><p><br></p><p>Cloud was a pivotal step forward for the software industry. The cloud allowed a natural division between two compute spaces - the building and maintaining of applications and the operating hardware required to host them. However, the cloud as it works today also has significant down-sides.</p><p><br></p><p>Today, all the major clouds have one anti-feature in common: cloud vendor lock in. Once you’ve built your entire infrastructure to a particular provider, shifting to another one entirely is typically a complicated and expensive undertaking.</p><p><br></p><p>As a result, what we have today are different isolated “clouds”, or walled gardens with little agility and optionality for businesses. On the macro scale, this reduces competition and increases pricing. On the micro scale, this means the norm is to be forced to entirely rely on a single hosting operator for business operations, continuity, and their ongoing fair pricing and policies.</p><p><br></p><h2>Multi-Cloud is a Cry for Help</h2><p><br></p><p>Businesses already intuitively understand the criticality of the issues associated with being stuck with a single provider and are working to imperfectly address them.</p><p><br></p><p><a href="https://www.prnewswire.com/news-releases/virtana-research-finds-more-than-80-of-enterprises-have-a-multi-cloud-strategy-and-78-are-using-more-than-three-public-clouds-301548266.html" rel="noopener noreferrer" target="_blank">In a recent survey, more than 80% of enterprises have a multi-cloud strategy and 78% are already using more than 3 public clouds.</a></p><p><br></p><p>The factors pushing these enterprises towards leveraging new providers stretch from cost optimization, to available geographic locations, and to the need for diversification in the event of a provider outage. However, in Hydra’s experience of offering multi-cloud advisory and consultation services, most businesses are multi-cloud in the sense that they leverage other providers to deploy new services, rather than making legacy services cloud agnostic and multi-cloud capable. This is because migrating legacy infrastructure built to a specific cloud remains so difficult.</p><p><br></p><p>Managing disparate resources across providers creates its own problems however, due to the burden of orchestrating and maintaining multiple different cloud infrastructure stacks and accounts which sport their own distinct tool chains and interfaces.</p><p><br></p><p>There’s a better way, and that’s exactly what Sky Computing aims to achieve.</p><p><br></p><h2>The Next Big Thing</h2><p><br></p><p>Imagine a single dashboard that allows you to click and choose which provider you deploy your resources to, filterable by variables such as prices, locations, latency estimates, historical uptime, performance, compliance standards, and legal jurisdictions. You can deploy any of your services and applications independently to whichever provider is a superior fit for the needs of that particular component, while also redeploying them as often as there is new favorable options to take advantage of.</p><p><br></p><p>Substituting this dashboard with an API or Terraform provider enables the same capabilities programatically, allowing you to build pipelines which can deploy and redeploy services to different providers automatically depending on stated preferences.</p><p><br></p><p>One obvious use case for these capabilities is the ability to shop around for the best deal on compute intensive operations, such as model training for machine learning. Whenever you’re ready to begin a new job training a model from a large data set, you can do an active search of the least expensive (or most performant) hardware available on the market to leverage.</p><p><br></p><p>Another use case is the ability to compare and choose between all resources available that meet required jurisdictional or compliance guidelines, such as GDPR, HIPAA, or ISO. Yet another can be optimizing for uptime, by spreading clusters of the same critical service across multiple different providers as an active risk management strategy.</p><p><br></p><h2>How It Works</h2><p><br></p><p>The key component required for unlocking this experience and the subsequent use cases is described by the Sky Above the Clouds authors as a “intercloud brokerage”, or what Hydra Host originally conceived as an “arbitrage layer.” These brokers serve as a middleware layer and API gateway for each cloud provider, providing a unified and standardized interface for leveraging all of their resources. They do the work of integrating and maintaining the interfaces of the various cloud providers, so you can focus on your application level services.</p><p><br></p><p>Several projects are already pursuing differing visions of this broker-like model today, Hydra Host included. Differing approaches exist with different sets of tradeoffs. The largest distinction to be made in categorizing them so far is what we call either the maximalist approach or the minimalist approach (what the Sky Above the Clouds refers to as a “shim layer”).</p><p><br></p><h2>Maximalist vs Minimalist</h2><p><br></p><p>The goal of the maximalist cloud broker approach is to piece together and standardize the interfaces for the various product catalogues of the major cloud service providers. This can include high level services such as EKS, RDS, data analytics tools, and more.</p><p><br></p><p>The advantage here is the significant demand and use cases for combining the best services from all providers, say RDS from AWS and data analytic tools from GCP, as they are widely used. Given the sheer number of services that cloud providers now provide, and the inherent complexity of higher-level services, there is a disadvantage. Building and maintaining an ever-growing list of products across clouds becomes tricky to scale and maintain even for a company dedicated to the function.</p><p><br></p><p>On the other hand, the goal of the minimalist approach is to conversely bridge only the lowest levels of cloud and hosting resources together, such as compute, storage, and networking. Once bridged, a protocol like standard is provided on top so higher-level services can be rebuilt on to a common interface. This interface is natively cloud agnostic, meaning applications built on top of it are able to run uniformly in all hosting environments without changes.</p><p><br></p><p>The advantage to the minimalist approach is that integrations with different providers are far simpler to build and maintain than the maximalist approach. There are fewer moving targets and less complexity. Like any good protocol, this approach makes the “broker” strategy much simpler to understand and lightweight to run. Additionally, it is easier to quickly integrate hosting providers. This expands possibilities far beyond the major cloud providers, including the large landscape of independent data centers and rapidly growing number of edge compute locations. Furthermore, this approach forces providers to compete on the commodity level that is the hardware, increasing competition where it can have the biggest daily impact: your wallet.</p><p><br></p><p>The downside of the minimalist approach is development complexity. Development shifts from integration work to redeveloping the infrastructure stack, meaning that the platform won’t be ideal right away for every application if they are heavily reliant on higher level cloud services like EKS. This will be overcome through time as the infrastructure stack matures and offers effective analogues to common high level cloud services.</p><p><br></p><h2>Adoption Roadmap</h2><p><br></p><p>So, what will the rollout of Sky Computing look like? Right now, adoption is nascent, which provides more upside for the innovators and early adopters. Ultimately, the adoption path will be influenced by one major factor: friction. While cloud brokers make it easy to deploy applications to multiple clouds by offering a unified interface, migrating off cloud interfaces to sky brokers takes the same type of work that makes cloud migration a pain to begin with.</p><p><br></p><p>This means that similar to current multi-cloud trends, most early adoption of the intercloud broker model will come from new services and applications. Emerging startups and SMBs are uniquely positioned to leverage this technology, as they are relatively unencumbered by sprawling legacy applications.</p><p><br></p><p>However, another group that that is well positioned to be early adopters includes large enterprises who are already facing the need to undergo infrastructure migrations. Perhaps this is due to recent M&amp;A or simply an effort to dramatically cut costs. For comparable effort, these enterprises can migrate to a Sky Computing model to solve their dilemma and never have to worry about extensive hosting migration pains ever again.</p><p><br></p><h2>Getting Involved</h2><p><br></p><p>It’s not often that you’re able to get in on the ground floor of a powerful new paradigm, but here we are. There are multiple “broker” providers to explore which we’ve outlined here (non-exhaustively, we’re sure), and given the early stages all are easy to reach out to. We, of course, encourage you to read the original <a href="https://arxiv.org/ftp/arxiv/papers/2205/2205.07147.pdf" rel="noopener noreferrer" target="_blank">"The Sky Above the Clouds"</a> paper, and other links in this article.</p><p><br></p><p>Here at Hydra Host we’re working on launching a State of the Sky newsletter you can subscribe to if interested, as well as a community chat which we’ll keep subscribers alerted to. Sky Computing is poised to effectively address rampant pain points in today’s cloud computing landscape and change the entire cost benefit equation distributed computing. Together, we can build a community of early practitioners that can accelerate this change and provide outsized benefits for early adopters.</p><p><br></p><h2>Inter-Cloud Brokers:</h2><p><br></p><ul><li><strong>Hydra Host:</strong> We’ve been researching and experimenting with various approaches to the “arbitrage/broker” function since April 2021, settling on a Minimalist strategy. We’re working with early adopters to onboard them on to our fully managed platform, with plans to open source our software in the second half of 2023. 2023 roadmap goes beyond public clouds, and integrating independent bare metal value providers. Make sure to contact us for a demo or if interested in our future newsletter, and keep an eye on our blog!</li><li><a href="https://controlplane.com/" rel="noopener noreferrer" target="_blank"><strong>Controlplane</strong></a><strong>:</strong> Founded in 2019, it allows user to mix and match cloud services from the big 3 (AWS, GCP, Azure) in the vein of our described “Maximalist” Sky strategy.&nbsp;</li><li><a href="https://www.upbound.io/" rel="noopener noreferrer" target="_blank"><strong>Upbound</strong></a><strong>: </strong>Founded in 2017, Unbound is building a universal cloud platform powered by their crossplane framework, which can be self hosted to help manage a variety of service providers.&nbsp;</li><li><a href="https://www.anyscale.com/" rel="noopener noreferrer" target="_blank"><strong>Anyscale</strong></a><strong>:</strong> Founded in 2019, Anyscale is a platform to run “Ray” based AI/ML workloads on top of any cloud. From the outside they seem to employ a more minimalist strategy to ensure workloads are inherently more portable, and is focused entirely on the hosted Ray use case, for now.</li></ul><p><br></p><p>We’ll be expanding this list on an ongoing basis as we see more players in the inter-cloud broker space continue to appear. If we missed anyone, let us know!</p>

Explore Sky Computing's potential to unify cloud services. Get seamless integration and enhanced flexibility beyond traditional cloud limitations.

Beyond the Cloud: Get Ahead on Sky Computing

Andrea Holt

<h3><strong>Introduction</strong></h3><p>&nbsp;</p><p>As artificial intelligence (AI) continues to transform industries like healthcare, finance, automotive, and digital media, the demand for high-performance GPUs capable of handling AI workloads at scale has intensified. Among NVIDIA’s top offerings, the RTX 4090 and the L40S stand out as two powerful but distinct options. While the RTX 4090 is commonly associated with high-end gaming and creative tasks, the L40S is engineered specifically for AI inference, training, and data center deployment.</p><p>&nbsp;</p><p>This guide explores the architectural differences, compute capabilities, energy efficiency, and cost considerations of both GPUs, providing a data-driven breakdown to help you make the best choice for your specific AI workload or compute infrastructure.</p><p>&nbsp;</p><h3><strong>Understanding the Basics: RTX 4090 vs. L40S</strong></h3><p>&nbsp;</p><h4><strong>Key Specifications Overview</strong></h4><p>&nbsp;</p><figure class="table"><table><tbody><tr><td><strong>Specification</strong></td><td><strong>RTX 4090</strong></td><td><strong>L40S</strong></td></tr><tr><td>CUDA Cores</td><td>14,592</td><td>18,176</td></tr><tr><td>Tensor Cores</td><td>456</td><td>568</td></tr><tr><td>VRAM</td><td>24GB GDDR6X</td><td>48GB GDDR6</td></tr><tr><td>Memory Bandwidth</td><td>1008 GB/s</td><td>864 GB/s</td></tr><tr><td>TDP</td><td>425W</td><td>300W</td></tr></tbody></table></figure><p>&nbsp;</p><p>The L40S delivers a significant advantage in parallel compute capabilities, offering more CUDA and Tensor Cores and double the VRAM capacity compared to the RTX 4090. These specifications make it especially powerful for memory-bound AI workloads, such as training large transformer models or performing inference on high-resolution data.</p><p>&nbsp;</p><p>Meanwhile, the RTX 4090 excels in clock speed and memory bandwidth, providing unmatched performance in real-time rendering and frame-intensive applications.</p><p>&nbsp;</p><h3><strong>CUDA Cores and Their Role in AI Processing</strong></h3><p>&nbsp;</p><p>CUDA Cores are the primary engines for executing parallel workloads on an NVIDIA GPU. With more CUDA Cores:</p><p>&nbsp;</p><ul><li><strong>Matrix operations</strong> in AI models are executed faster</li><li><strong>Batch processing</strong> of images or text is accelerated</li><li><strong>Inference latency</strong> is reduced for real-time systems</li></ul><p>&nbsp;</p><p>With 3,584 more CUDA Cores than the RTX 4090, the L40S offers better scalability in deep learning frameworks like PyTorch or TensorFlow, especially when training or serving models in production pipelines.</p><p>&nbsp;</p><h3><strong>Tensor Cores and Deep Learning Acceleration</strong></h3><p>&nbsp;</p><p>Tensor Cores are designed for mixed-precision operations; key to efficient AI training and inference. They enable:</p><p>&nbsp;</p><ul><li>High-throughput matrix multiplications</li><li>Accelerated support for FP8, BF16, and INT8 formats</li><li>Faster execution of transformer-based models and CNNs</li></ul><p>&nbsp;</p><p>The L40S’s 568 Tensor Cores offer improved performance across all precision levels, giving it an edge in applications like reinforcement learning, large-scale inference clusters, and generative AI. While the RTX 4090 also features 4th-gen Tensor Cores, they are tuned for gaming-enhanced AI effects like DLSS rather than continuous deep learning workloads.</p><p>&nbsp;</p><h3><strong>Comparative Analysis: Performance and Capabilities</strong></h3><p>&nbsp;</p><h4><strong>FP32 Performance in HPC and AI Training</strong></h4><p>&nbsp;</p><p>FP32 precision remains relevant in training large neural networks, scientific simulations, and traditional HPC workloads. The L40S offers approximately 24% more FP32 throughput than the RTX 4090, leading to shorter training cycles and faster convergence in large models.</p><p>&nbsp;</p><p>This gain translates into real productivity benefits in compute-bound environments, where throughput determines how many experiments or iterations can be completed in a given time.</p><p>&nbsp;</p><h4><strong>Importance of VRAM for Model Training</strong></h4><p>&nbsp;</p><p>Modern AI models often require massive memory overhead for input data, parameters, and temporary computations. In this area:</p><p>&nbsp;</p><ul><li><strong>L40S (48GB)</strong> supports training of large-scale models without frequent memory swapping</li><li><strong>RTX 4090 (24GB)</strong> handles mid-sized models but may require partitioning or model pruning for very large datasets</li></ul><p>&nbsp;</p><p>For use cases like GPT-4 fine-tuning, diffusion models, or 3D vision tasks, the L40S enables smoother training workflows and supports higher batch sizes, improving both speed and accuracy.</p><p>&nbsp;</p><h4><strong>Memory Bandwidth: Throughput for Real-Time Tasks</strong></h4><p>&nbsp;</p><figure class="table"><table><tbody><tr><td><strong>Factor</strong></td><td><strong>RTX 4090</strong></td><td><strong>L40S</strong></td></tr><tr><td>Memory Bandwidth</td><td>1008 GB/s</td><td>864 GB/s</td></tr><tr><td>Memory Type</td><td>GDDR6X</td><td>GDDR6</td></tr></tbody></table></figure><p>&nbsp;</p><p>While the RTX 4090 offers superior bandwidth, this advantage is most relevant for workloads involving real-time rendering, texture streaming, or frame buffering. AI workloads typically benefit more from higher capacity and Tensor core parallelism, both of which are stronger in the L40S.</p><p>&nbsp;</p><h3><strong>Power Efficiency and Thermal Design</strong></h3><p>&nbsp;</p><h4><strong>TDP and Cooling Requirements</strong></h4><p>&nbsp;</p><figure class="table"><table><tbody><tr><td><strong>Factor</strong></td><td><strong>RTX 4090</strong></td><td><strong>L40S</strong></td></tr><tr><td>TDP</td><td>425W</td><td>300W</td></tr><tr><td>Cooling Design</td><td>Air-cooled</td><td>Data center-optimized</td></tr></tbody></table></figure><p>&nbsp;</p><p>With a TDP that is 125W lower than the RTX 4090, the L40S provides a more energy-efficient profile suitable for 24/7 data center operations. It also integrates well into thermally optimized server racks and supports higher density deployments without straining HVAC systems.</p><p>&nbsp;</p><h4><strong>Importance of Power Efficiency in AI Operations</strong></h4><p>&nbsp;</p><p>Long training sessions and inference workloads can last hours or even days. Energy-efficient GPUs like the L40S reduce:</p><p>&nbsp;</p><ul><li><strong>Total cost of ownership (TCO)</strong> over extended usage</li><li><strong>Cooling requirements</strong>, improving rack-level performance density</li><li><strong>Carbon footprint</strong>, aligning with sustainability initiatives in enterprise IT</li></ul><p>&nbsp;</p><p>In contrast, the RTX 4090 is better suited for environments with fewer operational constraints and a stronger focus on peak performance in short sessions.</p><p>&nbsp;</p><h3><strong>AI Model Training and Simulation Use Cases</strong></h3><h4>&nbsp;</h4><h4><strong>L40S in Enterprise AI Workloads</strong></h4><p>&nbsp;</p><p>The L40S is optimized for deployment in:</p><p>&nbsp;</p><ul><li><strong>AI training clusters</strong> handling transformer, RNN, and vision models</li><li><strong>Medical diagnostics</strong> powered by AI-assisted scan analysis</li><li><strong>Financial forecasting</strong> involving large-scale simulations and real-time analytics</li></ul><p>&nbsp;</p><p>It integrates easily into infrastructure supporting direct liquid cooling (DLC), high-bandwidth PCIe lanes, and multi-GPU orchestration frameworks like Kubernetes or Slurm.</p><p>&nbsp;</p><h4><strong>RTX 4090 for Creative and Mixed Workflows</strong></h4><p>&nbsp;</p><p>The RTX 4090 is a powerhouse for creators who require:</p><p>&nbsp;</p><ul><li><strong>High-end 3D rendering</strong>, animation, or simulation</li><li><strong>Game development environments</strong> using Unreal Engine or Unity</li><li><strong>Video production</strong> with 4K and 8K media timelines</li></ul><p>&nbsp;</p><p>It also supports AI-enhanced effects for rendering pipelines and is commonly used by hybrid users who alternate between AI prototyping and media production.</p><p>&nbsp;</p><h3><strong>Cost-Benefit Analysis and Deployment Planning</strong></h3><p>&nbsp;</p><h4><strong>Pricing and Optimization</strong></h4><p>&nbsp;</p><figure class="table"><table><tbody><tr><td><strong>Factor</strong></td><td><strong>RTX 4090</strong></td><td><strong>L40S</strong></td></tr><tr><td>Approx. Price</td><td>$1,600 – $2,000</td><td>$4,000 – $5,000</td></tr><tr><td>Optimized For</td><td>Gaming, creative</td><td>AI inference, HPC</td></tr><tr><td>VRAM</td><td>24GB</td><td>48GB</td></tr></tbody></table></figure><p>&nbsp;</p><p>The RTX 4090 is significantly more affordable, making it a compelling option for developers and researchers with lighter AI workloads. However, the L40S justifies its premium price with better scalability, ECC memory support, and data center-grade reliability.</p><p>&nbsp;</p><h4><strong>Scalability in Multi-GPU Environments</strong></h4><p>&nbsp;</p><ul><li><strong>L40S</strong> scales more effectively in multi-GPU configurations, thanks to its thermal design, lower power draw, and high Tensor core count</li><li><strong>RTX 4090</strong> lacks enterprise features like ECC and data center tuning, limiting its role in production-level AI infrastructure</li></ul><p>&nbsp;</p><p>For Hydra Host clients or any organization scaling AI training pipelines, the L40S represents a more robust and future-proof investment.</p><p>&nbsp;</p><h3><strong>Conclusion: Selecting the Right GPU for AI Success</strong></h3><p>&nbsp;</p><h4><strong>Choose the L40S if You Need:</strong></h4><p>&nbsp;</p><ul><li>48GB VRAM for massive datasets</li><li>Enterprise-grade energy efficiency</li><li>Superior AI training and inference performance</li><li>Long-term deployment in production AI pipelines</li></ul><p>&nbsp;</p><h4><strong>Choose the RTX 4090 if You Need:</strong></h4><p>&nbsp;</p><ul><li>Peak rendering and gaming performance</li><li>Lower-cost GPU for prototyping or experimentation</li><li>Real-time creative workloads with AI-accelerated graphics</li><li>Occasional AI workloads that do not exceed 24GB VRAM</li></ul><p>&nbsp;</p><h3><strong>Key Takeaways</strong></h3><p>&nbsp;</p><ul><li>L40S delivers stronger AI throughput, better scalability, and data center readiness for enterprise use.</li><li>RTX 4090 offers superior real-time rendering and affordability, ideal for hybrid creators and solo developers.</li><li>Memory capacity and core count favor the L40S, making it the better choice for heavy AI training.</li><li>Power draw and cooling requirements differ, with the L40S being more efficient for continuous workloads.</li><li>Select based on infrastructure readiness, budget constraints, and workload type to maximize long-term value.<br>&nbsp;</li></ul>

Compare RTX 4090 vs. L40S GPUs for AI and machine learning. Learn about performance, efficiency, and scalability to choose the best option. 

RTX 4090 vs. L40S: Choosing the Best GPU for AI Workloads

Hydra Host and Inflect partner to provide bare metal GPU servers on Inflect’s digital infrastructure marketplace

<h2><strong>Partner Spotlight: Hydra x Inflect</strong>&nbsp;</h2><p>Hydra is committed to making bare metal GPUs accessible wherever teams build and scale AI. That’s why we’ve partnered with <a href="https://platform.inflect.com/">Inflect</a>, a trusted marketplace and advisory platform for digital infrastructure. This collaboration expands Inflect’s offerings beyond colocation, internet access, and network testing, giving their customers direct access to Hydra’s bare metal GPU servers at extremely competitive pricing.&nbsp;</p><p>&nbsp;</p><h3><strong>About Inflect</strong>&nbsp;</h3><p>Inflect transforms digital infrastructure procurement by connecting buyers and sellers in one frictionless platform. From colocation and connectivity to advisory services for building entire data centers, Inflect provides clarity, speed, and expert support in a traditionally fragmented space.</p><p>&nbsp;</p><h3><strong>Hydra on Inflect</strong>&nbsp;</h3><p>Through this partnership, users can now browse and rent <a href="https://inflect.com/quote-tool/svr/all?provider=Hydra+Host">Hydra’s bare metal GPU servers</a> directly from the Inflect platform. Customers looking to build a data center from the ground up can rely on Inflect’s advisory services, while those who need immediate compute can spin up Hydra’s GPU infrastructure with just a few clicks.&nbsp;</p><p>&nbsp;</p><p>Hydra delivers the <strong>raw compute power</strong> from the latest NVIDIA GPUs, like B200s, to large-scale clusters optimized for AI and HPC workloads. Combined with Inflect’s marketplace, this gives enterprises a streamlined way to either plan long-term infrastructure strategies or deploy GPU capacity instantly.&nbsp;</p><p>&nbsp;</p><h3><strong>Why This Partnership Matters</strong>&nbsp;</h3><p>The Inflect x Hydra partnership turns Inflect into a full-service IT infrastructure platform.&nbsp;</p><p>&nbsp;</p><p>For customers, it means flexibility:&nbsp;</p><p>•<strong>Strategic build-out</strong> with Inflect advisory&nbsp;</p><p>•<strong>Immediate access</strong> to GPUs through Hydra’s bare metal rentals&nbsp;</p><p>&nbsp;</p><p>Together, Hydra and Inflect are reducing friction, cutting time-to-deployment, and helping customers innovate faster.&nbsp;</p>

Hydra Host partners with Inflect to expand its marketplace, offering direct access to bare metal NVIDIA GPUs at competitive pricing. Together, Hydra and Inflect simplify digital infrastructure by combining advisory services with instant GPU rentals.

Hydra and Inflect Bring Bare Metal GPUs to the Marketplace

<p>We are witnessing the most striking generational shift: by 2030, the United States will use more electricity for AI than it will use to manufacture all industrial products combined. 40% of new grid usage will be for AI.&nbsp;</p><p>For this collaboration, we have built an end-to-end solution that optimizes the load on the electrical grid to deliver significant savings to AI customers. We deliver state-of-the-art open source models (e.g. Kimi K2) to thousands of users on the latest NVIDIA GPUs (B200s, H200s). These customers are building frontier applications with previously unimaginable ramp ups in users, so cost and scale is critical to them.&nbsp;</p><p>&nbsp;</p><p>Because the power grid is the most sensitive and overprovisioned to small moments of peak load, reducing this load for brief moments totalling only 3% of the day can turn a “no” from a utility company into a “yes”.&nbsp;This leads to a surprising magnification in cost savings across the physical infrastructure chain. With Parasail’s AI Deployment Network easily able to match load to supply and rebalance loads, we can pass cost savings &gt; 10% onto end customers with no downsides or impact, and we can ensure hardware is available when they scale.&nbsp;</p><p>&nbsp;</p><p>As this project demonstrates, great things happen when companies that are masters of their craft of the respective parts of this value chain come together. From end AI users to the inference platform through the data center and GPU rental ecosystem, all the way to power generation and grid operation.&nbsp;Here is how Parasail, Hydra Host, and Mercury Computing came together to build a unique solution to save customers money and help solve the toughest challenge our electrical grid has ever seen.</p><p>&nbsp;</p><p><strong><em>Hydra's Key contribution:</em></strong></p><p>With Hydra, Parasail and Mercury did not have to worry about sourcing GPUs, managing the physical infrastructure, or running the software that managed power load events. Our <a href="https://brokkr.hydrahost.com/docs/brokkr-api-overview" rel="noopener noreferrer" target="_blank">Brokkr API</a> automated the provisioning of bare metal B200s and H200s across a vast network of data center partners, allowing compute to be deployed close to power sources and scaled with real-time demand. Parasail simply obtained high-performance GPUs in a familiar interface at a price far better than anything else on the market, due to the cost advantages of bare metal over cloud. Mercury focused on securing power from providers and installing their software in Hydra’s infrastructure. Parasail lives on the far end of demand (AI inference provider) and Mercury lives on the far end of supply (power supply to data centers). Hydra Host handled everything in between.</p><p><br></p><h3><strong>Parasail:</strong></h3><p><strong><em>About Parasail:</em></strong></p><p>Parasail pulls together the world’s supply of GPU compute to build the best <a href="https://www.parasail.io/" rel="noopener noreferrer" target="_blank">AI inference platform</a>. Our AI Deployment Network connects a massive fleet of GPUs around the world into a single global pool with managed inference services, giving customers the ability to run AI deployments at peak performance and efficiency anywhere in the world with no effort or complexity. With over 40B tokens of production traffic served daily, Parasail is quickly becoming the AI inference platform of choice for customers that care about running the latest models and deploying large-scale workloads while maintaining the flexibility and cost effectiveness critical for success.&nbsp;</p><p><br></p><p><strong><em>Parasail's Key Contribution:</em></strong></p><p>Parasail’s role is to bring AI demand to this GPU infrastructure collaboration and manage the inference endpoints for customers. Parasail’s AI inference platform is built for the unprecedented explosion of demand for AI and the planetary-scale buildout of GPU infrastructure. They power a wide variety of workloads and use cases from high-reliability production-grade AI endpoints to overnight processing of massive batch workloads. Their platform excels at matching the supply of compute at a given time to demand, meaning power throttle events can easily be absorbed by load balancing to other sources or assigning the GPUs to less time-sensitive workloads.</p><p>&nbsp;</p><p><br></p><h3><strong>Mercury Computing:</strong></h3><p><strong><em>About Mercury Computing:</em></strong></p><p><a href="https://mercurycomputing.io/" rel="noopener noreferrer" target="_blank">Mercury Computing</a> provides workload flexibility for data centers, speeding time to power through flexible utility interconnection. Mercury’s FlexSLA solution, ideal for AI workloads, lowers the cost of computing in exchange for occasional, non-interruptive power scaling—turning data centers into flexible grid resources and aligning incentives between data center operators, their compute tenants and local utilities.</p><p>&nbsp;</p><p><strong><em>Mercury's Key contribution:</em></strong></p><p>Through its partnership with Parasail and Hydra Host, Mercury has officially launched its FlexSLA solution—and along with it the first live demonstration of AI workload flexibility.</p><p><br></p><p>Mercury’s FlexSLA provides discounted computing in exchange for occasional workload flexibility via non-interruptive power scaling. Workload flexibility is highly valued by both data center operators and local utilities. A recent study by the Nicholas Institute for Energy, Environment and Sustainability at Duke University found that flexibility in just one quarter of one percent of hours—roughly 20 hours per year—would unlock 76 gigawatts of headroom for data center expansion over today’s grid infrastructure, avoiding the need for costly and prolonged grid infrastructure upgrades.</p><p><br></p><p>Through collaboration with Parasail and Hydra Host, FlexSLA is now live and in production at a state-of-the-art colocated AI data center in Brooklyn, New York, powered by local utility Con Edison. The flexible workloads are production inference requests run by Parasail against Kimi 2, a recently released open source model with over 1 trillion parameters. Parasail is hosting <a href="https://www.kimi.com/" rel="noopener noreferrer" target="_blank">Kimi 2</a> on the newest generation NVIDIA Blackwell B200 8-way HGX server managed by Hydra Host.</p><p><br></p><p><strong>FlexSLA is currently available for any Hydra Host GPU reservation in data centers across the US.</strong> It can be added to existing agreements or embedded into new GPU reservations. Moreover, Mercury has integrated its software solution with Brokkr, Hydra Host’s bare metal GPU server management platform, making it fast and easy for customers to get started. There’s no install, configuration, or hardware retrofit necessary.</p><p>&nbsp;</p><p>For Parasail, Mercury’s first live FlexSLA customer, the impact on performance has been minimal, with initial results showing up to 25 percent decrease in energy consumption with only a 5 percent reduction in tokens per second inference throughput. Parasail’s distributed AI infrastructure easily handles occasional periods of brief power scaling, making the performance impact of FlexSLA barely noticeable—and its economics a no-brainer.</p>

Discover how Hydra Host, Parasail, and Mercury Computing are reshaping AI infrastructure. By integrating GPU rentals with grid-aware workload flexibility, this collaboration delivers 10%+ cost savings and accelerates AI deployment at scale.

AI Infrastructure Meets Grid Optimization | Hydra Host, Parasail & Mercury

<h3>Introduction to Model Parallelism</h3><p>&nbsp;</p><h4>What Is Model Parallelism?</h4><p>&nbsp;</p><p>Model parallelism is a technique used in machine learning to train large neural networks by distributing the model architecture itself across multiple GPUs or devices. Rather than replicating the model on each GPU (as in data parallelism), different layers or operations are assigned to different processors. This strategy is essential when the model's size exceeds the memory capacity of any single GPU.</p><p>&nbsp;</p><h4>Model Parallelism vs. Data Parallelism</h4><p>&nbsp;</p><p>While data parallelism trains multiple copies of a model on different slices of data, model parallelism divides the model itself across devices. This distinction makes model parallelism the go-to method when dealing with architectures that cannot physically fit on a single GPU.</p><p>&nbsp;</p><h4>Why It Matters</h4><p>&nbsp;</p><p>As AI models grow in complexity (such as large language models and deep transformers) model parallelism becomes critical. Without it, training these networks would be constrained by hardware limitations. It unlocks new possibilities for building and scaling next-generation AI systems.</p><p>&nbsp;</p><h3>Benefits of Model Parallelism</h3><p>&nbsp;</p><h4>Enables Training of Extremely Large Models</h4><p>&nbsp;</p><p>Model parallelism allows developers to train massive models that would otherwise exceed the memory limits of individual GPUs. This is particularly important for cutting-edge applications in NLP, computer vision, and generative AI.</p><p>&nbsp;</p><h4>Improved Efficiency</h4><p>&nbsp;</p><p>By splitting the workload across devices, training processes can become faster and more memory-efficient. With careful configuration, developers can reduce idle time and improve resource utilization.</p><p>&nbsp;</p><h4>Scalability</h4><p>&nbsp;</p><p>Model parallelism supports horizontal scaling: as models grow, additional GPUs or nodes can be added to distribute the training load. This scalability is key to long-term AI infrastructure planning.</p><p>&nbsp;</p><h3>Implementing Model Parallelism</h3><p>&nbsp;</p><h4>Key Steps</h4><p>&nbsp;</p><ol><li><strong>Divide the Model:</strong> Identify layers or submodules that can be run independently.</li><li><strong>Assign Devices:</strong> Distribute these model segments across multiple GPUs.</li><li><strong>Manage Communication: </strong>Ensure synchronization between devices as data flows through the model.</li><li><strong>Optimize for Latency and Throughput:</strong> Use appropriate strategies to minimize inter-GPU communication delays.</li></ol><p>&nbsp;</p><h4>Infrastructure Requirements</h4><p>&nbsp;</p><p>Successful implementation of model parallelism depends on high-memory GPUs and low-latency communication links. Support for distributed training frameworks and efficient interconnects are equally important.</p><p>&nbsp;</p><h3>Technical Challenges</h3><p>&nbsp;</p><h4>Load Imbalance</h4><p>&nbsp;</p><p>Unequal distribution of work across devices can cause bottlenecks. Effective model partitioning strategies are necessary to ensure all devices are utilized evenly.</p><p>&nbsp;</p><h4>Communication Overhead</h4><p>&nbsp;</p><p>Transferring data between GPUs introduces latency. While unavoidable, this can be mitigated with high-bandwidth links and smart scheduling.</p><p>&nbsp;</p><h4>Debugging and Profiling</h4><p>&nbsp;</p><p>Distributed environments make it harder to identify performance issues. Specialized tools and experience are essential to maintain stability and efficiency in training.</p><p>Real-World Applications</p><p>&nbsp;</p><h4>GPT-3 by OpenAI</h4><p>&nbsp;</p><p>With 175 billion parameters, GPT-3 is a prime example of a model that relies on model parallelism to function. Its architecture spans multiple GPUs and nodes, making parallel execution a necessity.</p><p>&nbsp;</p><h4>Vision Transformers (ViTs)</h4><p>&nbsp;</p><p>ViTs handle large image patches and benefit from splitting both attention layers and feedforward components across devices. This allows them to scale effectively while maintaining performance.</p><p>&nbsp;</p><h4>Megatron by NVIDIA</h4><p>&nbsp;</p><p>NVIDIA’s Megatron architecture uses advanced model and tensor parallelism to train massive transformer models. It's a benchmark for what distributed training at scale looks like in practice.</p><p>&nbsp;</p><h3>Scaling with the Right Infrastructure</h3><p>&nbsp;</p><p>Training large models efficiently requires compute environments that support model parallelism. This includes:</p><p>&nbsp;</p><ul><li>Multi-GPU configurations</li><li>High memory capacity</li><li>Low-latency interconnects</li><li>Framework compatibility with distributed training tools</li></ul><p>&nbsp;</p><p>Hydra Host provides high-performance GPU servers with access to NVIDIA’s most advanced GPUs (including the A100, H100, and H200) designed to support scalable AI training workloads. For organizations training frontier-scale models, infrastructure matters.</p><p>&nbsp;</p><h3>Conclusion</h3><p>&nbsp;</p><p>Model parallelism is essential for modern AI. It allows researchers and engineers to work with architectures that push beyond the limits of a single GPU, enabling larger models, faster training, and more efficient use of resources.</p><p>&nbsp;</p><p>As demand for large-scale AI grows, organizations need robust compute platforms to support this complexity. With access to dedicated GPU infrastructure and support for multi-GPU workflows, providers like Hydra Host offer a strong foundation for scaling your AI operations effectively.</p><p>&nbsp;</p>

Learn how model parallelism enables large-scale AI training & how multi-GPU infrastructure, like Hydra Host’s, powers scalable neural network development.

Model Parallelism: Scaling AI Models Beyond a Single GPU

<h2>Understanding the Role of Graphics Cards in 3D Modeling</h2><p><br></p><p>Graphics cards are essential for 3D artists, animators, and professionals working in architectural visualization, character animation, and product design. A powerful GPU accelerates rendering times, enhances real-time previews, and improves visual quality, allowing creatives to work more efficiently. Choosing the right GPU ensures seamless performance across different 3D software applications, making it a critical component in any rendering setup.</p><h2><br></h2><h2>Assessing Your 3D Rendering Needs</h2><h3><br></h3><h3>What Types of Projects Require 3D Rendering?</h3><p>3D rendering is used in various industries, including:</p><p><br></p><ul><li>Architecture → Creating realistic building visualizations</li><li>Game development → Designing immersive environments and characters</li><li>Product design → Developing high-detail prototypes</li><li>Visual effects (VFX) → Simulating complex physics-based animations</li></ul><p><br></p><p>Each of these applications has different GPU performance requirements, meaning the complexity of your projects directly influences GPU selection.</p><p><br></p><h3>How Does Project Complexity Influence GPU Selection?</h3><p>More complex projects require higher processing power and more VRAM. Factors such as:</p><p><br></p><ul><li>Detailed textures and high polygon counts</li><li>Advanced lighting and ray tracing effects</li><li>Large-scale environmental models</li></ul><p><br></p><p>demand powerful GPUs with ample memory to prevent slowdowns and crashes during rendering.</p><h2><br></h2><h2>Balancing Budget with Performance</h2><h3><br></h3><h3>How to Determine Your Budget for a GPU?</h3><p>Your budget should reflect the scope of your projects and the level of performance you require. Entry-level GPUs can handle basic rendering, while professional-grade GPUs deliver faster results with advanced rendering features.</p><p><br></p><ul><li>Entry-level (under $500): Suitable for beginners or light rendering tasks</li><li>Mid-range ($500–$1500): Ideal for moderate workloads with 4K rendering capabilities</li><li>High-end ($1500+): Best for large, complex scenes requiring real-time ray tracing and multi-GPU setups</li></ul><p><br></p><h3>Should Power Supply Capabilities Affect Your GPU Choice?</h3><p>Yes, high-performance GPUs require more power. A robust Power Supply Unit (PSU) is necessary to handle the energy demands of advanced GPUs. Before purchasing, check:</p><p><br></p><ul><li>Wattage requirements → Ensure your PSU has enough power</li><li>Available power connectors → Match your PSU to your GPU’s connectors</li><li>Cooling needs → High-powered GPUs generate more heat and may require additional cooling solutions</li></ul><p><br></p><h2>The Importance of Video RAM (VRAM) for 3D Processing</h2><h3><br></h3><h3>How Much VRAM Do You Need for Different 3D Software?</h3><p>Different 3D rendering software has varying VRAM requirements:</p><p><br></p><ul><li>Blender / SketchUp → 8GB VRAM is sufficient for most projects</li><li>Cinema 4D / 3ds Max / Maya → 12GB–16GB VRAM recommended for complex scenes</li><li>Unreal Engine / Houdini → 16GB+ VRAM required for large-scale real-time rendering</li></ul><p><br></p><h3>What Factors Influence VRAM Requirements?</h3><p>Several factors determine the amount of VRAM needed:</p><p><br></p><ul><li>Resolution → Higher resolutions require more VRAM</li><li>Texture sizes → Large textures increase memory demands</li><li>Scene complexity → More objects and polygons require higher VRAM capacity</li></ul><p><br></p><p>Selecting a GPU with sufficient VRAM ensures smoother performance and prevents out-of-memory errors during rendering.</p><h2><br></h2><h2>Evaluating Graphics Card Speed and Processing Power</h2><h3>What Are Core Clock Speed, CUDA Cores, and Stream Processors?</h3><p><br></p><ul><li>Core Clock Speed → Measures how fast the GPU processes instructions</li><li>CUDA Cores (NVIDIA) / Stream Processors (AMD) → Parallel processors that execute rendering tasks</li><li>Ray Tracing Cores → Dedicated hardware for real-time lighting and shadow calculations</li></ul><p><br></p><h3>How Do These Specs Affect 3D Rendering Performance?</h3><p><br></p><ul><li>More CUDA cores / Stream Processors → Faster rendering speeds</li><li>Higher clock speed → Quicker frame calculations</li><li>Dedicated ray tracing cores → Improved realism in lighting and shadows</li></ul><p><br></p><p>For GPU-accelerated rendering, a higher number of processing cores and a fast clock speed significantly reduce render times.</p><h2><br></h2><h2>Ensuring Compatibility with Computer Hardware</h2><h3><br></h3><h3>What Are PCIe Interfaces and Why Are They Important?</h3><p>PCIe interfaces connect the GPU to the motherboard, affecting data transfer speed and performance. Ensure:</p><p><br></p><ul><li>Your motherboard has a compatible PCIe slot (most modern GPUs use PCIe 4.0 or 5.0)</li><li>Your case has enough space for the GPU</li><li>Your system supports the GPU’s power requirements</li></ul><p><br></p><h3>How to Assess the Size and Power Connections of a GPU?</h3><p><br></p><ul><li>Check the dimensions to ensure a proper fit in your PC case</li><li>Review power requirements to match the PSU’s wattage and connectors</li><li>Consider airflow and cooling to prevent overheating</li></ul><p><br></p><h3>What Input Options Should You Consider (DisplayPort, HDMI, DVI)?</h3><p>Different monitors use different connections:</p><p><br></p><ul><li>DisplayPort → Best for high-refresh-rate displays and 4K+ resolutions</li><li>HDMI → Common for gaming monitors and TVs</li><li>DVI → Older connection used in legacy monitors</li></ul><p><br></p><h2>Exploring Additional Features: Cooling, Noise, and Overclocking</h2><h3><br></h3><h3>Why Is Cooling Important for a GPU?</h3><p>Rendering-intensive tasks cause GPUs to generate heat, which can:</p><p><br></p><ul><li>Reduce performance due to thermal throttling</li><li>Shorten GPU lifespan if not properly cooled</li></ul><p><br></p><p>High-end GPUs often include:</p><p><br></p><ul><li>Dual-fan or triple-fan cooling systems</li><li>Liquid cooling solutions for overclocking</li></ul><p><br></p><h3>How Do Noise Levels Impact Your Work Environment?</h3><p>Some GPUs run louder under heavy loads due to fan speeds. If working in a quiet studio, consider:</p><p><br></p><ul><li>Low-noise GPU models</li><li>Custom cooling solutions</li><li>Hybrid liquid cooling for silent operation</li></ul><p><br></p><h3>Should You Consider a GPU’s Overclocking Potential?</h3><p>Overclocking can boost performance but requires proper cooling. If planning to overclock, choose a GPU with:</p><p><br></p><ul><li>High power headroom</li><li>Efficient cooling systems</li><li>Factory-overclocked models for stability</li></ul><p><br></p><h2>Comparing Different GPU Brands and Models</h2><p><br></p><h3>Which GPUs Are Known for the Best Rendering Performance?</h3><p>Top GPUs for 3D rendering professionals include:</p><p><br></p><ul><li>NVIDIA RTX A6000 → High VRAM (48GB) for large projects</li><li>NVIDIA RTX A40 → Ideal for AI-assisted rendering</li><li>AMD Radeon Pro W6800 → Great for high-fidelity rendering in CAD software</li></ul><p><br></p><h3>How to Compare Specs Among Major Brands Like NVIDIA and AMD?</h3><p>When comparing NVIDIA vs. AMD GPUs, consider:</p><p><br></p><ul><li>CUDA Cores (NVIDIA) vs. Stream Processors (AMD)</li><li>Ray tracing performance</li><li>Driver support and software optimization for rendering software</li></ul><p><br></p><h2>Making Informed Decisions: Multiple Small GPUs vs. Fewer Large GPUs</h2><p><br></p><h3>How Does GPU Memory Footprint Influence Your Decision?</h3><p><br></p><ul><li>Multiple GPUs → Faster rendering through parallel processing</li><li>Single high-VRAM GPU → Better for large, complex scenes</li></ul><p><br></p><p>Consider multi-GPU setups for speed, but ensure your software supports GPU scaling.</p><p><br></p><h3>What Benchmarks Are Useful for Cost-Effective Rendering?</h3><p><br></p><p>Look for GPU rendering benchmarks that measure:</p><p><br></p><ul><li>Render time in Blender, OctaneRender, or Redshift</li><li>Power efficiency vs. performance ratio</li><li>Cost per frame rendered</li></ul><p><br></p><h2>Conclusion: Unlock Ultimate 3D Performance with the Right GPU</h2><p><br></p><h3>Why Is It Important to Continually Reassess Your GPU Needs?</h3><p>Rendering demands evolve, so regularly reassess your hardware needs to ensure optimal performance.</p><h3><br></h3><h3>How Can Embracing New Technologies Improve Your 3D Rendering Projects?</h3><p>New advancements, like real-time ray tracing and AI-assisted rendering, can streamline workflows and enhance visual quality, keeping you ahead in the competitive 3D industry.</p><p><br></p><p>Choosing the right GPU for 3D rendering is a balance of power, VRAM, cooling, and cost-efficiency. By selecting the best GPU for your workflow, you can boost productivity, enhance rendering quality, and optimize your creative process.</p>

Learn how to choose the best GPU for 3D rendering based on performance, VRAM, cooling, and budget to optimize your workflow.

How to Select the Best GPUs for 3D Rendering Projects

<h2>Why GPUs Are Essential for AI Model Training</h2><p><br></p><p>Graphics Processing Units (GPUs) are a crucial component in deep learning and AI model training. Their ability to perform parallel processing makes them significantly more efficient than CPUs for handling complex neural networks. GPUs typically feature thousands of cores, enabling them to process multiple AI tasks simultaneously, accelerating model training and improving computational performance.</p><h3><br></h3><h3>What Makes GPUs Ideal for AI Workloads?</h3><p>GPUs are optimized for matrix and vector operations, which are fundamental to AI model computations. Their parallel processing capabilities allow them to handle large datasets and complex neural networks efficiently, making them the preferred choice for machine learning, deep learning, and AI acceleration.</p><h3><br></h3><h3>How Do GPUs Compare to CPUs in AI Processing?</h3><p>Compared to CPUs, GPUs:</p><p><br></p><ul><li>Process tasks in parallel, leading to higher throughput</li><li>Handle larger AI workloads without bottlenecks</li><li>Reduce training time for deep learning models</li></ul><p><br></p><p>While CPUs are more versatile for general computing, GPUs are essential for high-performance AI processing due to their parallel architecture.</p><h2><br></h2><h2>Understanding GPU Architecture for AI Tasks</h2><p><br></p><p>GPU architecture plays a vital role in AI model performance. Key elements to consider include:</p><p><br></p><ul><li>Core count (more cores enable greater parallelism)</li><li>Memory (VRAM) capacity (determines dataset size capability)</li><li>Clock speed (affects processing efficiency)</li><li>AI-specific optimizations (such as Tensor Cores)</li></ul><p><br></p><h3>What Role Does Core Count Play in AI Performance?</h3><p>Core count is a critical factor in AI workloads. A higher number of cores enables:</p><p><br></p><ul><li>Greater parallelism, accelerating deep learning computations</li><li>Faster AI training times, reducing development cycles</li><li>Improved efficiency in handling large datasets</li></ul><p><br></p><h3>Why Is Parallel Processing Important in AI?</h3><p>Parallel processing allows AI models to:</p><p><br></p><ul><li>Process vast amounts of data simultaneously</li><li>Train deep learning models faster</li><li>Optimize neural network operations with minimal latency</li></ul><p><br></p><p>GPUs excel in parallel processing due to their ability to execute multiple operations concurrently, unlike CPUs, which rely on sequential task execution.</p><h2><br></h2><h2>Key GPU Features for Maximizing AI Workloads</h2><h3><br></h3><h3>What Is the Significance of Total Memory and VRAM?</h3><p>VRAM stores training datasets, model parameters, and intermediate results during computations. Higher VRAM:</p><p><br></p><ul><li>Supports larger AI models</li><li>Prevents memory bottlenecks</li><li>Reduces reliance on slower system memory</li></ul><p><br></p><p>For generative AI models, at least 12GB of VRAM is recommended, with higher-end models requiring 24GB or more.</p><h3><br></h3><h3>How Do Memory Clock Speed and GPU Clock Speed Affect Performance?</h3><p><br></p><ul><li>Memory clock speed: Affects data transfer rate between GPU and VRAM</li><li>GPU clock speed: Determines how fast processing units execute instructions</li></ul><p><br></p><p>Higher clock speeds improve training efficiency, but parallel computing capacity (core count) is usually a bigger factor in AI performance.</p><h3><br></h3><h3>What Are AI-Specific Hardware Optimizations?</h3><p>Many modern GPUs include specialized AI acceleration features, such as:</p><p><br></p><ul><li>Tensor Cores (NVIDIA) – Accelerate matrix operations for deep learning</li><li>RT Cores (Ray Tracing Cores) – Optimize image-based AI models</li><li>FP16 and INT8 Computation Support – Enhance AI inference efficiency</li></ul><p><br></p><p>GPUs with dedicated AI acceleration hardware provide significantly improved processing speeds for training deep learning models.</p><h2><br></h2><h2>Navigating the Complexity of GPU Specifications</h2><h3><br></h3><h3>What Are CUDA Cores and How Do They Affect AI Tasks?</h3><p>CUDA Cores, found in NVIDIA GPUs, handle parallel computing tasks. More CUDA cores:</p><p><br></p><ul><li>Increase computational throughput</li><li>Enhance AI model training performance</li><li>Reduce processing time for complex AI models</li></ul><p><br></p><h3>How Do Tensor Cores Accelerate Machine Learning?</h3><p>Tensor Cores significantly speed up AI training and inference by:</p><p><br></p><ul><li>Optimizing tensor operations used in deep learning</li><li>Reducing processing time for neural network computations</li><li>Improving model accuracy through hardware-accelerated calculations</li></ul><p><br></p><h3>What Are the Benefits of More VRAM for AI?</h3><p>More VRAM enables:</p><p><br></p><ul><li>Larger batch sizes during training</li><li>Efficient storage of deep learning parameters</li><li>Better performance for image-based AI models</li></ul><p><br></p><p>For beginner AI models, 8GB of VRAM is sufficient, but larger generative AI models may require 16GB to 24GB or more.</p><h2><br></h2><h2>Cost Considerations When Choosing a GPU</h2><h3><br></h3><h3>How Does Price Impact Your GPU Decision?</h3><p>High-end GPUs provide better performance but come at higher costs. Businesses must balance:</p><p><br></p><ul><li>Computational needs vs. budget</li><li>Long-term investment in AI hardware</li><li>Scalability and future-proofing GPU purchases</li></ul><p><br></p><h3>What Are the Cost-Effective GPU Options for Beginners?</h3><p>For beginners, mid-range GPUs such as:</p><p><br></p><ul><li>NVIDIA RTX 3060 (12GB VRAM)</li><li>NVIDIA RTX 4060 (8GB VRAM)</li></ul><p><br></p><p>These GPUs provide an excellent balance of affordability and performance for entry-level generative AI models.</p><h2><br></h2><h2>Vendor Software Support and Ease of Deployment</h2><h3><br></h3><h3>What Type of Software Drivers Are Necessary for AI Workloads?</h3><p>AI workloads require specialized GPU drivers, including:</p><p><br></p><ul><li>NVIDIA CUDA &amp; cuDNN (for deep learning acceleration)</li><li>TensorFlow and PyTorch compatibility</li><li>AI model optimization frameworks (TensorRT, ONNX Runtime)</li></ul><p><br></p><h3>How Do Machine Learning Libraries Impact GPU Selection?</h3><p>Choosing a GPU that supports TensorFlow, PyTorch, and JAX ensures:</p><p><br></p><ul><li>Smooth model deployment</li><li>Optimized AI inference performance</li><li>Access to AI acceleration features</li></ul><p><br></p><h2>Additional Considerations for Advanced Users</h2><h3><br></h3><h3>What Cooling Systems Are Needed for High-Performance GPUs?</h3><p>High-performance GPUs generate significant heat. Effective cooling solutions include:</p><p><br></p><ul><li>Liquid cooling systems (DLC) for data centers</li><li>High-airflow PC cases with multiple fans</li><li>Custom water-cooled GPU setups for extreme workloads</li></ul><p><br></p><h3>How Do Specialized Connections Affect Motherboard Integration?</h3><p>AI GPUs require:</p><p><br></p><ul><li>PCIe Gen 4 or Gen 5 slots (for higher bandwidth)</li><li>Sufficient power supply connectors (high-end GPUs require 300W+ power)</li></ul><p><br></p><p>Before purchasing a GPU, verify motherboard compatibility and power requirements.</p><h2><br></h2><h2>Comparing GPUs with Alternative Solutions Like FPGAs and GPUaaS</h2><h3><br></h3><h3>When Should You Consider FPGAs Over GPUs?</h3><p>Field Programmable Gate Arrays (FPGAs) offer:</p><p><br></p><ul><li>Customizable hardware configurations</li><li>Better energy efficiency for specialized AI tasks</li><li>Lower latency than GPUs for real-time inference</li></ul><p><br></p><p>However, FPGAs are harder to program and may not be suitable for all AI applications.</p><h3><br></h3><h3>What Are the Advantages of GPU-as-a-Service (GPUaaS)?</h3><p>GPU-as-a-Service (GPUaaS) provides:</p><p><br></p><ul><li>On-demand GPU access without upfront hardware costs</li><li>Scalability for cloud-based AI training</li><li>Lower operational expenses for short-term AI projects</li></ul><p><br></p><p>GPUaaS is ideal for startups and research institutions that need temporary access to high-performance AI hardware.</p><h2><br></h2><h2>Recommendations for Beginner-Level Generative AI Workloads</h2><h3><br></h3><h3>Why Is the NVIDIA RTX 3060 or 4060 Ideal for Starters?</h3><p><br></p><ul><li>Balanced performance and affordability</li><li>8GB to 12GB VRAM for AI workloads</li><li>Supports TensorFlow, PyTorch, and CUDA-based deep learning</li></ul><p><br></p><h3>What GPU Specifications Are Essential for Newcomers?</h3><p>For beginners, look for:</p><p><br></p><ul><li>At least 2000+ CUDA Cores</li><li>Tensor Core support for AI acceleration</li><li>8GB+ VRAM for deep learning</li></ul><p><br></p><p>By selecting the right GPU for generative AI, users can optimize performance, reduce training time, and improve model efficiency while staying within budget.</p>

Learn how to select the right GPU for training generative AI models by evaluating core count, VRAM, Tensor Cores, and cost-effective options.

Selecting the Best GPUs for Generative AI Model Training

<h2>Introduction to Radeon Instinct GPUs</h2><h3><br></h3><h3>What Are Radeon Instinct MI300 and MI250?</h3><p>The Radeon Instinct MI300 and MI250 are high-performance workstation GPUs from AMD, designed for AI acceleration, machine learning, and data center applications. The MI300, released in 2023, is built on CDNA 3.0 architecture and features the Aqua Vanjaram GPU, while the MI250, introduced in 2021, is based on CDNA 2.0 architecture and uses the Aldebaran GPU. Both GPUs deliver cutting-edge computational power for professionals handling complex workloads.</p><h3><br></h3><h3>Who Should Consider These GPUs?</h3><p>The MI300 and MI250 are suited for data centers, machine learning engineers, researchers, and enterprises requiring high-performance computing. Their memory technologies, computational efficiency, and AI acceleration make them ideal for demanding workloads.</p><h3><br></h3><h3>Why Compare MI300 and MI250?</h3><p>Comparing these GPUs highlights improvements in architecture, processing power, memory efficiency, and scalability. Understanding these differences helps professionals select the most suitable option for their specific workloads.</p><h2><br></h2><h2>GPU Architecture and Design</h2><h3><br></h3><h3>CDNA 2.0 vs. CDNA 3.0: Key Differences</h3><p>AMD's CDNA (Compute DNA) architecture is designed for high-performance computing. CDNA 2.0 in the MI250 provides optimized parallel processing and efficiency, while CDNA 3.0 in the MI300 enhances AI acceleration, memory bandwidth, and processing cores, resulting in higher performance for complex workloads.</p><h3><br></h3><h3>5 nm vs. 6 nm Manufacturing Process</h3><p>The MI300 uses a 5 nm manufacturing process, allowing for a higher transistor count and better power efficiency. The MI250, built on 6 nm, is slightly less power-efficient. The MI300’s denser transistor layout translates to better processing speeds and energy optimization.</p><h2><br></h2><h2>Performance and Computing Power</h2><h3><br></h3><h3>Pipeline and Processing Cores</h3><p>The MI300 features 14,080 processing pipelines, while the MI250 has 13,312 pipelines. This increase in cores allows the MI300 to deliver higher throughput and improved floating-point performance.</p><h3><br></h3><h3>Core and Boost Clock Speeds</h3><p>Both GPUs have a base clock of 1000 MHz and a boost clock of 1700 MHz, ensuring consistent high-performance computing across various workloads.</p><h3><br></h3><h3>Floating-Point Processing Power</h3><p>The MI300 reaches 47.87 TFLOPS, while the MI250 offers 45.26 TFLOPS. The higher TFLOPS rating of the MI300 enables faster AI computations and deep learning tasks.</p><h3><br></h3><h3>Transistor Count</h3><p>The MI300 integrates 153 billion transistors, compared to 58.2 billion in the MI250. This significant difference gives the MI300 a major edge in data processing, AI workloads, and high-performance computing tasks.</p><h2><br></h2><h2>Memory Capabilities and Technology</h2><h3><br></h3><h3>Memory Type: HBM2e vs. HBM3</h3><p>The MI250 utilizes HBM2e memory, while the MI300 features HBM3, offering higher bandwidth and improved efficiency. Both GPUs provide 128 GB of memory, but HBM3 allows for faster data transfers and reduced latency.</p><h3><br></h3><h3>Maximum RAM and Bandwidth</h3><p>Both GPUs come with 128 GB of RAM and a memory bandwidth of 3,277 GB/s, ensuring seamless handling of large datasets, simulations, and AI models.</p><h3><br></h3><h3>Memory Bus Width and Clock Speed</h3><p>With an 8192-bit memory bus and 1600 MHz clock speed, both GPUs deliver high-speed data access and improved parallel processing performance.</p><h2><br></h2><h2>Power Consumption and Efficiency</h2><h3><br></h3><h3>Total Power Draw: MI250 vs. MI300</h3><p>The MI250 has a TDP of 500W, while the MI300 consumes 600W. Despite its higher power draw, the MI300 is more power-efficient per watt due to its 5 nm process and improved architecture.</p><h3><br></h3><h3>Recommended PSU Requirements</h3><p><br></p><ul><li>MI300: 1000W PSU</li><li>MI250: 900W PSU</li><li>A higher power supply capacity ensures stable operation under heavy workloads.</li></ul><h3><br></h3><h3>Power Efficiency: A Key Factor</h3><p>Despite drawing more power, the MI300 processes data more efficiently, leading to lower overall energy costs and greater computing performance per watt.</p><h2><br></h2><h2>Technology and Interface Features</h2><h3><br></h3><h3>PCIe Interface Differences</h3><p><br></p><ul><li>MI250: PCIe 4.0 x16</li><li>MI300: PCIe 5.0 x16</li><li>The PCIe 5.0 interface in the MI300 enables faster data transfer rates and lower latency, optimizing communication with high-speed storage and processors.</li></ul><p><br></p><h3>Tensor Cores and AI Acceleration</h3><p>The MI300 integrates 880 Tensor Cores, significantly improving AI training and inference speeds. The MI250, while powerful, lacks dedicated Tensor Cores, making it less efficient for AI workloads.</p><h2><br></h2><h2>GPU Suitability for High-Performance Tasks</h2><h3><br></h3><h3>Which GPU Is Best for Machine Learning?</h3><p>The MI300 is better suited for AI and deep learning due to its Tensor Cores and enhanced floating-point performance. The MI250, while capable, is less optimized for AI workloads.</p><h3><br></h3><h3>How Do They Compare in High-Performance Computing?</h3><p>Both GPUs perform well in scientific simulations, AI model training, and data-intensive tasks, but the MI300 provides higher efficiency and better scalability for next-gen applications.</p><h3><br></h3><h3>Best GPU for Data Centers</h3><p>The MI300 is the superior choice for future-proofed AI and deep learning applications, whereas the MI250 remains a viable option for high-performance computing with lower power consumption needs.</p><h2><br></h2><h2>Conclusion: Making the Right Choice</h2><h3><br></h3><h3>When to Choose MI300 Over MI250</h3><p>The MI300 is the better choice for professionals requiring AI acceleration, faster data processing, and superior power efficiency. It is ideal for cutting-edge research, deep learning, and high-performance enterprise computing.</p><h3><br></h3><h3>Is the MI250 Still a Good Option?</h3><p>Yes, for users with lower AI processing needs, the MI250 provides strong computational performance and reliability at a lower power draw.</p><h3><br></h3><h3>Future-Proofing Your GPU Investment</h3><p>The MI300 is significantly more future-proof, thanks to its advanced architecture, memory technology, and AI optimization. The MI250 remains a solid choice but may struggle to keep up with future AI workloads.</p><h2><br></h2><h2>Final Verdict: MI300 vs. MI250</h2><p><br></p><p>The MI300 is the best option for AI, machine learning, and next-generation computing, while the MI250 remains a reliable choice for traditional high-performance computing tasks.</p>

Compare AMD’s Radeon Instinct MI300 & MI250 GPUs, exploring architecture, memory, power efficiency, and performance for AI and high-performance computing.

Radeon Instinct MI300 vs. Instinct MI250: A Comprehensive Comparison

<h1><strong>FOR IMMEDIATE RELEASE:</strong></h1><p><br></p><h2><strong>Hydra Host and Introl Announce Strategic Collaboration to Power AI Infrastructure Deployments&nbsp;</strong></h2><p><br></p><p><br></p><p><strong>Miami, Florida – Sept 5, 2025</strong> – Hydra Host, a global<a href="https://nvidianews.nvidia.com/news/nvidia-dgx-cloud-lepton-connects-europes-developers-to-global-nvidia-compute-ecosystem" rel="noopener noreferrer" target="_blank"> NVIDIA Cloud Partner </a>specializing in bare-metal GPU infrastructure, today announced a joint initiative with <a href="https://introl.com/" rel="noopener noreferrer" target="_blank">Introl</a>, a leader in mission-critical data center deployments. Together, the two companies are delivering comprehensive solutions that make it faster and easier for organizations to scale artificial intelligence infrastructure without compromise.&nbsp;</p><p><br></p><p>Hydra Host provides customers with direct access to NVIDIA’s most advanced GPUs through its <a href="https://brokkr.hydrahost.com/inventory" rel="noopener noreferrer" target="_blank">Brokkr platform</a>, offering flexible rental and ownership models without the added costs of cloud virtualization. With decades of experience in high-performance computing, the team at Introl have supported some of the world’s largest GPU cluster deployments and fiber-optic installations. Introl’s global network of field engineers deliver end-to-end deployment services—from rack-and-stack to power and cooling integration—ensuring optimized sites and 4-hour SLAs in key markets.</p><p><strong>&nbsp;</strong></p><p><strong>A Complete Solution for AI Growth</strong>&nbsp;</p><p>By combining Hydra’s ability to provision and finance* GPU clusters with Introl’s deployment expertise, the collaboration aims to reduce infrastructure build timelines from months to weeks. Customers gain a seamless, production-ready environment for training foundation models or running large-scale inference workloads.&nbsp;</p><p><br></p><h3><strong>“Hydra is committed to giving organizations sovereign control over their compute,” said Nick Sedar, VP of Business Development. “Joining with Introl means our customers not only get the best GPUs, but they know those GPUs will be deployed securely, compliantly, and on time.”&nbsp;</strong></h3><p><strong>&nbsp;</strong></p><p><strong>Scaling with Confidence</strong>&nbsp;</p><p>The collaboration enables enterprises, governments, and AI platforms to scale infrastructure at speed and with confidence. Hydra expands access to GPUs across its global data center footprint, while Introl ensures those resources are integrated into customer environments with precision and reliability. Whether building a handful of racks or a full AI factory, the initiative is designed to ensure continuity, predictable performance, and reduced downtime.&nbsp;</p><p><br></p><p>Together, Hydra and Introl deliver high-performance, sovereign, and deployable compute that supports the next generation of artificial intelligence innovation.&nbsp;</p><p><br></p><p>*Financing is provided by Hydra Host and is subject to credit approval and applicable terms. Introl does not originate or service financing agreements.</p>

Hydra Host and Introl join forces to deliver faster, sovereign AI infrastructure with NVIDIA GPUs, financing, and mission-critical deployment expertise.

Hydra Host and Introl Announce Strategic Collaboration to Power AI Infrastructure Deployments 

<h2>Introduction to GPU Memory in AI Model Training</h2><p><br></p><p>Graphics Processing Unit (GPU) memory is one of the most important components in deep learning and AI model training, particularly for large language models (LLMs). As these models continue to grow in complexity, they require more VRAM to store training data, activations, and gradients efficiently. The amount of available GPU memory directly affects training speed, model size, and computational efficiency, making it a critical factor in deep learning performance.</p><h3><br></h3><h3>What Is GPU Memory and Why Is It Important for AI?</h3><p>GPU memory, also known as VRAM (Video Random Access Memory), is where neural network weights, activations, and training data are stored during AI computations. Unlike standard system RAM, VRAM is optimized for high-speed parallel processing, making it essential for deep learning, gaming, and scientific simulations. A GPU with more VRAM can train larger AI models, process bigger datasets, and reduce computational bottlenecks.</p><h3><br></h3><h3>How Does GPU Memory Impact the Training of Large Language Models?</h3><p>Training large language models involves processing millions or even billions of parameters, which requires significant memory. More GPU memory allows:</p><p><br></p><ul><li>Larger batch sizes, speeding up training efficiency</li><li>Storage of more model parameters, reducing computational overhead</li><li>Minimized reliance on CPU memory, preventing slowdowns</li></ul><p><br></p><p>When GPU memory is insufficient, training slows down, requires multiple GPUs, or forces compromises in model complexity. This is why choosing the right GPU is essential for AI research and deployment.</p><h2><br></h2><h2>Deep Learning Hardware Essentials</h2><h3><br></h3><h3>What Is Deep Learning Hardware?</h3><p>Deep learning hardware consists of specialized computational components that accelerate AI model training. These include:</p><p><br></p><ul><li>GPUs (for matrix multiplications and parallel computing)</li><li>CPUs (for managing data preprocessing and task orchestration)</li><li>High-speed VRAM &amp; RAM (for fast data access and storage)</li></ul><p><br></p><p>Selecting the right combination of these components maximizes efficiency and reduces training time.</p><h3><br></h3><h3>How Does Different GPU Hardware Compare in Terms of Memory Capacity?</h3><p>Different GPUs offer varying VRAM capacities, affecting their suitability for AI tasks:</p><p><br></p><ul><li>NVIDIA V100 → 32GB VRAM (suitable for mid-sized models)</li><li>NVIDIA A100 → 40GB VRAM (optimized for large-scale AI training)</li><li>NVIDIA H100 → 80GB VRAM (next-gen GPU for extreme AI workloads)</li></ul><p><br></p><p>The choice of GPU depends on the model’s parameter size, batch size, and computational needs.</p><h2><br></h2><h2>Graphics Card Performance and GPU Memory</h2><h3><br></h3><h3>How Does GPU Memory Affect Graphics Card Performance?</h3><p>The size of GPU memory affects how much data can be processed simultaneously. More VRAM enables:</p><p><br></p><ul><li>Faster processing of large datasets</li><li>Higher-resolution graphics rendering</li><li>More stable performance in AI applications</li></ul><p><br></p><p>For AI, more VRAM allows training larger models without splitting data across multiple GPUs, improving efficiency and scalability.</p><h3><br></h3><h3>What Role Does GPU Memory Play in AI Model Training Efficiency?</h3><p>In AI training, GPU memory stores entire model weights, activations, and intermediate calculations. When memory is optimized:</p><p><br></p><ul><li>Training runs faster with fewer interruptions</li><li>Models can be trained on a single GPU instead of requiring multiple GPUs</li><li>Computational costs are reduced, improving overall efficiency</li></ul><p><br></p><p>This is why high-memory GPUs are a priority for AI researchers and data scientists.</p><h2><br></h2><h2>Optimizing GPU Memory for AI Model Training</h2><h3><br></h3><h3>What Strategies Can Optimize GPU Memory Usage During Training?</h3><p>Several optimization techniques can help manage limited GPU memory while training large models:</p><p><br></p><ul><li>Gradient accumulation → Splits updates across multiple iterations to reduce memory load</li><li>Activation checkpointing → Stores only necessary activations to free up memory</li><li>Mixed precision training → Uses FP16 instead of FP32 to lower memory consumption</li></ul><p><br></p><p>Using these strategies, AI researchers can train larger models on smaller GPUs.</p><h3><br></h3><h3>How Does ZeRO Aid in Memory Optimization?</h3><p>ZeRO (Zero Redundancy Optimizer) is a memory-saving method used in distributed AI training. It optimizes:</p><p><br></p><ul><li>Optimizer state partitioning (reduces redundant memory usage)</li><li>Gradient partitioning (splits computations efficiently across GPUs)</li><li>Model parameter partitioning (allows training larger models with less VRAM)</li></ul><p><br></p><p>ZeRO helps push the boundaries of AI scalability without requiring high-end GPUs.</p><h2><br></h2><h2>Case Study: Training Large Language Models on GPUs</h2><h3><br></h3><h3>How Much GPU Memory Is Required for a 10B Parameter Model?</h3><p>Training a 10 billion parameter model in different settings:</p><p><br></p><ul><li>16-bit precision training: Requires 20GB of GPU memory</li><li>Mixed precision training: Requires 160GB+ of GPU memory</li></ul><p><br></p><p>Larger models require multiple high-memory GPUs or advanced memory management techniques.</p><h3><br></h3><h3>Comparison of V100 and A100 GPUs in Terms of Memory Efficiency</h3><p><br></p><ul><li>V100 (32GB VRAM): Can train a 10B model but with memory constraints</li><li>A100 (40GB VRAM): Handles larger models with greater efficiency</li></ul><p><br></p><p>Choosing the right GPU depends on batch size, dataset complexity, and required training speed.</p><h2><br></h2><h2>Technical Approaches to Memory Efficiency</h2><h3><br></h3><h3>How Does Activation Checkpointing Reduce Memory Usage?</h3><p>Activation checkpointing stores only essential activations and recomputes intermediate results when needed. This:</p><p><br></p><ul><li>Reduces memory load by 33%</li><li>Allows training larger models without running out of VRAM</li><li>Comes with a minor recomputation cost</li></ul><p><br></p><h3>What Are the Stages of Memory Optimization in ZeRO?</h3><p>ZeRO has three progressive memory optimization stages:</p><p><br></p><ol><li>Stage 1 – Partitions optimizer states</li><li>Stage 2 – Distributes gradients across processes</li><li>Stage 3 – Splits model parameters</li></ol><p><br></p><p>By combining these techniques, researchers can train massive models with minimal memory waste.</p><h2><br></h2><h2>Advanced Techniques for GPU Memory Management</h2><h3><br></h3><h3>How Do Quantization Techniques Assist in Memory Optimization?</h3><p>Quantization reduces precision levels (e.g., FP32 → FP16 or INT8) to:</p><p><br></p><ul><li>Cut memory usage in half</li><li>Improve computational efficiency</li><li>Lower power consumption</li></ul><p><br></p><p>This technique makes AI training more accessible on lower-memory GPUs.</p><h3><br></h3><h3>What Is CPU Offloading and How Does It Help in Memory Management?</h3><p>CPU offloading moves certain computations from GPU to CPU, freeing up VRAM. Benefits include:</p><p><br></p><ul><li>Allows training models that exceed GPU memory capacity</li><li>Balances workload distribution for more efficiency</li><li>Improves overall system resource utilization</li></ul><p><br></p><p>This technique is especially useful for large-scale AI workloads.</p><h2><br></h2><h2>Scalability and Computational Efficiency</h2><h3><br></h3><h3>How Does GPU Memory Contribute to Machine Learning Scalability?</h3><p>Larger GPU memory enables:</p><p><br></p><ul><li>More scalable AI model training</li><li>Processing of bigger datasets without slowdown</li><li>Reduced computational overhead for complex AI tasks</li></ul><p><br></p><h3>What Techniques Enhance Computational Efficiency in GPU Utilization?</h3><p><br></p><ul><li>Model state partitioning → Distributes data across GPUs</li><li>Activation checkpointing → Saves memory without affecting accuracy</li><li>Gradient accumulation → Improves batch size efficiency</li></ul><p><br></p><p>By optimizing memory, AI systems achieve better scalability with less hardware investment.</p><h2><br></h2><h2>Conclusion</h2><p><br></p><p>GPU memory is a critical factor in AI training, influencing efficiency, scalability, and computational speed. By optimizing memory usage, AI researchers can train larger, more powerful models with fewer hardware constraints.</p><h3><br></h3><h3>Key Insights on GPU Memory and AI Model Training</h3><p><br></p><ul><li>Larger VRAM enables more efficient AI model training</li><li>Memory optimization techniques (ZeRO, quantization, CPU offloading) improve scalability</li><li>Choosing the right GPU depends on model size, budget, and performance needs</li></ul><h3><br></h3><h3>Future Trends in GPU Memory Usage for AI and Machine Learning</h3><p><br></p><ul><li>Larger VRAM capacities in next-gen GPUs</li><li>More advanced memory management techniques</li><li>Wider adoption of distributed AI training</li></ul><p><br></p><p>As AI continues to evolve, efficient GPU memory utilization will be key to pushing the boundaries of deep learning capabilities.</p>

Discover how GPU memory impacts AI training, model scalability, and optimization techniques for efficient deep learning performance.

Understanding the Impact of GPU Memory on Training Large Language Models

<p><br></p><h2>Introduction to GPU Servers</h2><p><br></p><p>With the rise of AI, machine learning (ML), and high-performance computing, the demand for GPU servers has surged. These powerful computing systems provide significant processing power for applications requiring parallel computations. This guide explores GPU servers, their benefits, and how to choose the right one to enhance your computational capabilities.</p><h3><br></h3><h3>What Is a GPU Server?</h3><p>A GPU server is a specialized computing system designed to process large volumes of data simultaneously. Unlike CPUs, which perform sequential tasks, GPUs excel at parallel processing, making them ideal for AI, ML, rendering, and deep learning applications. By leveraging GPU servers, businesses and researchers can significantly improve computational efficiency and processing speed.</p><h3><br></h3><h3>Importance of GPU Servers for High-Level Tasks</h3><p>GPU servers are critical for AI training, scientific simulations, and large-scale data processing. They offer faster computations compared to traditional CPU servers. With cloud-based GPU servers, even small businesses and startups can access high-end computing resources, making AI and ML more widely available and scalable.</p><h2><br></h2><h2>Key Characteristics of GPU Servers</h2><h3><br></h3><h3>Compatibility with AI Frameworks</h3><p>GPU servers are optimized for deep learning and AI model training, offering compatibility with frameworks such as TensorFlow, PyTorch, and CUDA. These frameworks leverage GPU acceleration to reduce training times and enhance model accuracy and efficiency.</p><h3><br></h3><h3>Benefits for High-Performance Computing</h3><p>Beyond AI, GPU servers excel in scientific computing, rendering, and large-scale simulations. Their ability to handle parallel workloads makes them indispensable in fields like medicine, finance, and physics research, where massive datasets require quick analysis.</p><h2><br></h2><h2>Advantages of GPU Hosting</h2><h3><br></h3><h3>Scalability and Flexibility</h3><p>GPU hosting provides businesses with scalable computing power. Whether a startup or enterprise, GPU hosting solutions allow companies to expand their resources on demand. Cloud-based GPU hosting offers remote access, enabling global collaboration and resource sharing.</p><h3><br></h3><h3>Budget-Friendliness and Worldwide Access</h3><p>GPU hosting eliminates the need for expensive hardware investments while offering on-demand access to powerful GPUs. Companies can select hosting plans based on budget and usage requirements, ensuring cost-effective computing solutions.</p><h2><br></h2><h2>Exploring Dedicated GPU Server Hosting</h2><h3><br></h3><h3>Full Access to Hardware Assets</h3><p>Dedicated GPU servers grant businesses exclusive access to GPU resources, eliminating shared performance bottlenecks. This ensures uninterrupted computing power for high-demand applications like AI model training and real-time data processing.</p><h3><br></h3><h3>Speed and Reliability Without Resource Sharing</h3><p>Since dedicated GPU servers are not shared with other users, they deliver consistent performance and low-latency computing. This makes them ideal for mission-critical applications that require stable and uninterrupted performance.</p><h3><br></h3><h3>Ideal for Advanced AI and ML Tasks</h3><p>Dedicated GPU hosting is the preferred choice for AI research labs, deep learning projects, and data-intensive workloads. With full control over hardware, businesses can optimize configurations to maximize performance and efficiency.</p><h2><br></h2><h2>Understanding GPU VPS Hosting</h2><h3><br></h3><h3>Ease of Setup and Management</h3><p>GPU VPS (Virtual Private Server) hosting is a cost-effective alternative for businesses that need GPU computing without managing dedicated hardware. GPU VPS allows users to customize environments, making it an excellent option for development and small-scale AI projects.</p><h3><br></h3><h3>Cost-Effectiveness for Small and Medium Projects</h3><p>GPU VPS hosting offers a balance of affordability and performance, making it suitable for small businesses, research institutions, and freelance AI developers who need GPU power without large infrastructure costs.</p><h2><br></h2><h2>Best Uses for GPU Servers</h2><h3><br></h3><h3>AI &amp; ML Applications</h3><p>GPU servers accelerate machine learning models and AI training, significantly reducing the time required for data processing and analysis. This is essential for autonomous systems, natural language processing, and image recognition.</p><h3><br></h3><h3>Advanced Graphic Rendering</h3><p>Industries such as gaming, film production, and 3D design rely on GPU servers for real-time rendering, visual effects, and high-resolution simulations. The increased processing power enables faster frame rendering and improved graphic fidelity.</p><h3><br></h3><h3>Scientific Research Opportunities</h3><p>GPU servers play a vital role in weather modeling, genomic research, and physics simulations, enabling researchers to process massive datasets efficiently and accelerate discovery in various scientific fields.</p><h2><br></h2><h2>The Benefits of a Dedicated Server with GPU</h2><h3><br></h3><h3>High-Performance Computing and Increased Security</h3><p>Dedicated GPU servers provide maximum processing power and enhanced security. Unlike shared environments, dedicated hosting protects sensitive data from potential breaches and ensures stable performance.</p><h3><br></h3><h3>Reliability and Customizability</h3><p>Dedicated GPU servers allow businesses to customize hardware and software configurations, optimizing them for specific workloads. This ensures reliable, high-speed computing tailored to unique business needs.</p><h2><br></h2><h2>Steps to Setting Up a GPU Server</h2><h3><br></h3><h3>Selecting an Appropriate Hosting Plan</h3><p>Choosing the right hosting plan depends on computing needs, budget, and project scalability. Providers offer various configurations, from shared GPU resources to high-end dedicated GPU servers.</p><h3><br></h3><h3>Installing GPU Drivers and Applications</h3><p>After selecting a hosting plan, businesses must install necessary GPU drivers (CUDA, OpenCL) and AI frameworks to maximize computing performance. Providers may offer pre-configured GPU environments for quick setup.</p><h3><br></h3><h3>Choosing the Right Operating System</h3><p>Selecting an OS compatible with AI and ML applications is essential. Popular options include Ubuntu, CentOS, and Windows Server, each supporting GPU acceleration and deep learning libraries.</p><h2><br></h2><h2>Security and Maintenance of GPU Servers</h2><h3><br></h3><h3>Best Practices for Security Measures</h3><p>Maintaining a secure GPU server involves:</p><p><br></p><ul><li>Implementing strong access controls</li><li>Using firewalls and intrusion detection systems</li><li>Encrypting sensitive data to prevent unauthorized access</li></ul><p><br></p><h3>Importance of Regular Updates and Monitoring</h3><p>Regular software updates and real-time server monitoring help prevent system vulnerabilities. Many GPU hosting providers offer automated patching and proactive threat detection for enhanced security.</p><h2><br></h2><h2>Conclusion</h2><p><br></p><p>GPU servers provide unparalleled computational power for businesses and researchers engaged in AI, ML, rendering, and scientific computing. Whether using cloud-based, dedicated, or VPS GPU hosting, the right solution depends on scalability, security, and budget considerations. By leveraging GPU servers, businesses can accelerate innovation, enhance efficiency, and stay ahead in a data-driven world.</p>

Learn what a GPU server is, how it works, its benefits, and how to choose the right GPU hosting solution for AI, ML, and computational tasks.

What Is a GPU Server? A Comprehensive Guide for Beginners

<p><br></p><h2>What Is a Bare Metal Server?</h2><p><br></p><p>A bare metal server, also known as a physical server, is a dedicated machine rented by a single customer. Unlike virtual servers, which share resources among multiple users, a bare metal server provides complete control over hardware and software configurations. This exclusivity ensures better performance, enhanced security, and more reliable computing power; making bare metal an ideal solution for high-performance applications, big data processing, and workloads that require consistent resources.</p><p><br></p><h2>How Do Bare Metal Servers Work?</h2><p><br></p><p>Bare metal servers function by allocating an entire physical machine to a single tenant, giving businesses:</p><p><br></p><ul><li>Customizable hardware configurations – Users can select the processor type, RAM, storage type (SSD/HDD), and network speed to match their specific needs. This level of customization ensures the best possible performance for each workload.</li><li>Full control over the operating system – Unlike shared environments where the OS is predefined, bare metal users can install and configure their preferred OS, security tools, and management software, ensuring compatibility with their applications.</li><li>Maximum performance with dedicated resources – Because there is no virtualization layer, all of the server’s resources (CPU, memory, disk I/O) are fully allocated to a single user, eliminating the risk of resource contention.</li></ul><p><br></p><p>These factors make bare metal servers a superior choice for industries that demand stable, high-speed computing with strict security and performance standards.</p><h2><br></h2><h2>Bare Metal vs. Other Server Types</h2><h3><br></h3><h3>Bare Metal vs. Dedicated Servers</h3><h3><br></h3><p>Both bare metal and dedicated servers offer single-tenant hosting, meaning resources are not shared. However, bare metal servers differ in key areas:</p><h3><br></h3><ul><li>Faster provisioning – Traditional dedicated servers often require manual setup and longer deployment times, while many bare metal servers support automated provisioning, allowing businesses to deploy hardware much faster.</li><li>More flexible hardware configurations – Dedicated servers typically come with fixed configurations, while bare metal servers allow for custom hardware selection, optimizing performance for different workloads.</li><li>Cloud integration – Bare metal servers are designed to support hybrid and multi-cloud strategies, allowing businesses to seamlessly connect their physical infrastructure with cloud environments for enhanced flexibility.</li></ul><h3><br></h3><h3>Bare Metal vs. Virtual Servers</h3><h3><br></h3><p>While virtual servers provide scalability and cost efficiency, they come with trade-offs:</p><h3><br></h3><ul><li>Shared hardware resources – Multiple users share the same underlying hardware, which can lead to performance bottlenecks due to the "noisy neighbor" effect (where other users' activities impact your performance).</li><li>Higher latency – Virtual servers introduce overhead from the hypervisor layer, which can slow down performance compared to direct hardware access in bare metal environments.</li><li>Security concerns – Since virtual machines exist within a shared environment, they may be more susceptible to cross-tenant security risks if the hypervisor is compromised.</li></ul><h3><br></h3><p>Bare metal servers eliminate these challenges by offering dedicated hardware with no virtualization overhead, making them the preferred choice for workloads that require stability, security, and high computational power.</p><h2><br></h2><h2>Key Benefits of Bare Metal Servers</h2><h3><br></h3><h3>1. Enhanced Performance and Security</h3><h3><br></h3><p>Bare metal servers offer unmatched processing power and security due to their single-tenant nature:</p><h3><br></h3><ul><li>No resource sharing – Unlike virtualized environments, where users compete for CPU and memory, bare metal servers dedicate 100% of the hardware’s power to a single workload, ensuring consistent performance.</li><li>Stronger security – With no shared hardware, the risk of data breaches, hypervisor attacks, and multi-tenant vulnerabilities is significantly reduced, making them ideal for compliance-heavy industries like finance and healthcare.</li></ul><h3><br></h3><h3>2. Full Control Over the Software Stack</h3><h3><br></h3><p>For businesses that require customized environments, bare metal servers provide:</p><h3><br></h3><ul><li>Complete OS and software control – Companies can install any operating system (Linux, Windows, BSD) and fine-tune configurations to match their specific application requirements.</li><li>Better compliance and security controls – Since the user manages the full software stack, they can implement advanced security policies, encryption methods, and access controls without restrictions imposed by cloud providers.</li></ul><h3><br></h3><h3>3. Reliable Disk and Network Performance</h3><h3><br></h3><p>Bare metal servers maintain stable disk and network performance, which is essential for:</p><h3><br></h3><ul><li>Big data analytics and machine learning, where large datasets must be processed quickly without I/O slowdowns.</li><li>High-speed financial trading, where milliseconds can impact profitability.</li><li>Streaming and media applications, which require uninterrupted network throughput.</li></ul><h3><br></h3><p>By providing dedicated storage and network bandwidth, bare metal servers ensure low-latency, high-speed computing.</p><h3><br></h3><h3>4. No "Noisy Neighbor" Effect</h3><h3><br></h3><p>Unlike virtualized environments, bare metal servers:</p><p><br></p><ul><li>Eliminate resource contention, as all CPU, RAM, and storage are dedicated to a single user.</li><li>Guarantee performance consistency, making them ideal for applications where latency and stability are critical.</li></ul><p><br></p><p>This makes them a top choice for workloads that cannot afford unpredictable slowdowns.</p><h2><br></h2><h2>Challenges and Considerations</h2><h3><br></h3><h3>1. Setup Complexity and Costs</h3><p><br></p><ul><li>Requires technical expertise – Unlike cloud VMs, which offer pre-configured environments, bare metal servers require manual setup, OS installation, and maintenance.</li><li>Higher upfront costs – While bare metal servers offer superior performance, their initial investment is often higher than virtualized solutions. However, businesses benefit from long-term savings due to increased efficiency and fewer performance-related issues.</li></ul><h3><br></h3><h3>2. Scalability Limitations</h3><h3><br></h3><ul><li>Physical hardware constraints – Unlike cloud-based virtual machines, which can be scaled up or down instantly, scaling a bare metal server requires adding new physical hardware, which can be time-consuming.</li><li>Best suited for stable workloads – Businesses with predictable resource demands will benefit the most, while companies needing rapid scaling may prefer a hybrid solution combining bare metal and cloud resources.</li></ul><h2><br></h2><h2>Ideal Use Cases for Bare Metal Servers</h2><h3><br></h3><p>Due to their high performance, security, and reliability, bare metal servers are best suited for:</p><h3><br></h3><ul><li>High-performance computing (HPC) – Scientific modeling, AI, and machine learning applications that require massive processing power and minimal latency.</li><li>Big data applications – Workloads that process large datasets benefit from fast disk I/O and dedicated memory resources.</li><li>Financial services and trading – Low-latency computing ensures fast transactions and real-time data processing.</li><li>Online gaming – Multiplayer games require stable network performance and low ping times.</li><li>Regulated industries – Finance, healthcare, and government sectors require strict compliance with security and data privacy regulations.</li></ul><h2><br></h2><h2>Conclusion</h2><h3><br></h3><p>Bare metal servers offer the perfect balance of high performance, security, and control, making them a powerful alternative to virtualized environments. While they require more setup and upfront investment, their benefits (dedicated resources, reduced security risks, and predictable performance) make them an invaluable asset for businesses handling demanding workloads.</p><p><br></p><p>Companies that prioritize long-term efficiency, reliability, and performance should strongly consider bare metal servers as a strategic IT investment.</p>

Discover how bare metal servers provide performance, security, and control for high-demand applications. Learn how they compare to virtual servers.

Bare Metal Servers: The Ultimate Choice for High-Performance Hosting

<p><strong>Lenovo x Hydra Host: Delivering the Future of AI Infrastructure</strong></p><p><br></p><p>When AI infrastructure needs to move from idea to reality, the right procurement partner makes all the difference. Hydra Host teamed up with Lenovo to deploy one of the first production-ready NVIDIA H200 clusters in the world. The full story is now available in <a href="https://www.lenovo.com/us/en/case-studies-customer-success-stories/hydra-host/" rel="noopener noreferrer" target="_blank"><u>Lenovo's official case study.</u></a></p><p><br></p><p>This project was about more than just speed. It was about getting the details right. Hydra’s procurement team collaborated closely with Lenovo to source and deliver high-performance ThinkSystem SR680a V3 servers under strict deadlines. This successful execution highlights how Hydra helps customers navigate a complex supply chain and secure top-tier AI hardware quickly and confidently.</p><p><br></p><p>Beyond the hardware, this deployment is a cornerstone of <a href="https://hydrahost.com/ai-factory/" rel="noopener noreferrer" target="_blank">Hydra Host’s AI Factory Accelerator</a>, a full-stack program designed to help teams build sovereign AI infrastructure from the ground up. Lenovo’s modular systems are a key part of this effort, providing the performance and scalability needed for real-world AI production environments.</p><p><br></p><p>Hydra Host’s strength lies in its ability to act as <a href="https://hydrahost.com/procurement/" rel="noopener noreferrer" target="_blank">a true procurement partner, managing everything from sourcing and shipping to deployment and monetization</a>. Together with partners like Lenovo, Hydra is redefining what’s possible for companies to build the next generation of AI factories.</p>

Hydra Host partnered with Lenovo to deliver a production-ready H200 cluster, powering sovereign AI infrastructure through expert GPU procurement.

Lenovo x Hydra Host: Delivering the Future of AI Infrastructure

<p>As Director of Hydra Capital, Kai Golden is responsible for connecting GPU deployments with investors across the capital stack. Kai previously served as an associate at Variant Investments, a $3 billion private credit fund, where he invested in asset-backed credit across litigation finance, AI infrastructure, hospitality, and fintech. Prior to that, Kai co-founded Belay, a fintech focused on income stability within the gig economy. In his free time, Kai enjoys producing music and practicing Muay Thai in Miami. Kai holds a BA in International Relations and Economics from Tulane University.</p>

Kai Golden

<p><strong>The Real AI Play: Infrastructure</strong>&nbsp;</p><p><br></p><p>The next big opportunity in AI isn’t another model; it’s the supercomputers fueling them. While most headlines fixate on software-level breakthroughs, superior returns exist in the backbone of those innovations: <strong>GPU infrastructure</strong>.&nbsp;&nbsp;</p><p><br></p><p>In the Substack below, Kai Golden, Managing Director of Hydra Capital, breaks down why<strong> </strong>bare metal GPU clusters are emerging as a powerful alternative asset class. With <strong>30%+ IRRs</strong>, fast paybacks, and the depreciation benefits of direct ownership, they offer a compelling response to the rising costs and opaque pricing of public cloud providers.&nbsp;</p><p><br></p><p>Hydra Host is making this opportunity more accessible than ever. Our <a href="https://hydrahost.com/financing/" rel="noopener noreferrer" target="_blank">financing solutions</a> and <a href="https://hydrahost.com/calculator/" rel="noopener noreferrer" target="_blank">GPU calculator</a> are designed to help investors and operators underwrite and scale infrastructure with speed and confidence. If you're serious about supporting the AI boom and capturing yield through high-performing hardware, <strong>this is where the real play begins</strong>.&nbsp;</p><p><br></p><p><a href="https://kaigolden.substack.com/p/how-to-invest-in-gpus" rel="noopener noreferrer" target="_blank">Read the full analysis</a>&nbsp;&nbsp;</p>

PU infrastructure powering AI data centers with trading-style dashboards – The Real AI Play: Infrastructure by Hydra Host

Discover why bare metal GPU clusters are emerging as a top-performing asset class in the AI era. Hydra Host's Director of Capital breaks down ROI, utilization, and infrastructure economics for long-term investors.

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