NVIDIA L40 vs. L40S: Which GPU Is Right for Your AI Infrastructure?

Introduction

As enterprise demand for AI, real-time rendering, and advanced simulation accelerates, selecting the right GPU becomes a mission-critical decision. NVIDIA’s L40 and L40S, both based on the cutting-edge Ada Lovelace architecture, stand out as top-tier options for organizations building AI factories or scaling virtual workloads. While the two cards share core technologies, they diverge significantly in compute capabilities, efficiency, and target applications.

This guide offers a comprehensive comparison between the L40 and L40S to help infrastructure planners, system architects, and AI platform engineers determine which GPU aligns best with their workload profiles and deployment environments.

The Ada Lovelace Architecture Advantage

NVIDIA’s Ada Lovelace architecture introduces significant advancements in graphics and AI acceleration. Both the L40 and L40S incorporate third-generation Ray Tracing (RT) cores and fourth-generation Tensor cores, enabling these GPUs to handle a broad spectrum of workloads ranging from real-time ray tracing to deep learning inference.

The Ada Lovelace architecture is optimized for data center deployments, delivering enhanced throughput per watt and improved processing density. These benefits make both GPUs well suited for integration into high-performance GPU infrastructure, including bare metal environments provided by Hydra Host.

NVIDIA L40 vs. L40S: Technical Overview

Despite sharing the same base architecture and memory configuration, these GPUs are tuned for different priorities. The L40 focuses on efficient rendering and visualization, while the L40S delivers enhanced AI and parallel compute capabilities for intensive workloads.

Feature Deep Dive

Memory and Ray Tracing Capabilities

Both GPUs feature 48GB of GDDR6 memory and include third-generation RT cores designed for accelerated ray tracing. This makes them suitable for large-scale 3D rendering and real-time visualization tasks. However, while memory capacity is identical, the L40S architecture drives memory bandwidth more aggressively, benefiting training and inference pipelines that depend on high-speed memory access.

Tensor Core Differences

The L40S features enhanced fourth-generation Tensor cores with improved throughput and expanded parallelism. This provides a significant performance uplift for AI applications such as LLM training, computer vision, and model fine-tuning at scale. In contrast, the L40’s standard Tensor cores are more than adequate for lightweight inference or graphics-enhanced AI but are not designed for multi-GPU deep learning clusters.

Compute Performance and AI Throughput

The L40 delivers consistent compute output for standard workloads. However, the L40S is engineered for high-performance computing environments where raw processing power is essential. Its superior Tensor core design makes it ideal for deep learning training, scientific simulations, and generative AI workflows. If your workloads include simultaneous rendering and AI inference, the L40S offers tangible advantages.

Use Case Scenarios

When to Choose the L40

The L40 is designed for organizations that prioritize visual compute, efficient GPU utilization, and cost-effective scalability. Ideal scenarios include:

• 3D modeling and rendering for architectural visualization, VFX production, or digital twin environments
• Virtual desktop infrastructure (VDI) setups that require high graphics fidelity but limited AI compute
• Budget-aware deployments that still demand large memory capacity and excellent RT performance

For Hydra Host customers deploying streaming workstations or GPU-accelerated visualization services, the L40 offers an optimal balance of performance and power efficiency.

When to Choose the L40S

The L40S targets high-demand enterprise environments where compute density and AI acceleration are top priorities. It is best suited for:

• AI training clusters running large language models or computer vision pipelines
• Scalable inference services in production environments with low latency requirements
• Scientific computing platforms performing simulations or modeling complex systems
• Hybrid AI-rendering workloads such as physics-based simulation with generative overlays

Due to its increased power draw and thermal output, the L40S is ideal in data centers that support high-efficiency cooling and robust power delivery, such as those enabled by Hydra Host’s direct liquid cooling (DLC) and airflow-optimized configurations.

Performance and Energy Considerations

AI and Machine Learning Tasks

The L40S excels in Tensor-heavy workflows. It can handle parallel model training, fine-tuning of transformer architectures, and multi-node distributed inference more effectively than the L40. Frameworks like TensorFlow, PyTorch, and JAX benefit from the L40S’s improved FP8 and BF16 throughput.

Rendering and Mixed Workloads

Both GPUs handle real-time rendering and simulation well. However, the L40S has a distinct edge in hybrid use cases that require simultaneous AI processing, such as rendering environments enhanced by machine learning algorithms or simulations integrated with dynamic decision models.

Energy Efficiency and Thermal Management

The L40 maintains a lower thermal envelope, requiring less cooling infrastructure and consuming less power. For facilities where rack density or energy constraints are a concern, the L40 is a more sustainable option.

In contrast, the L40S requires careful consideration of airflow, power delivery, and environmental control. Hydra Host’s infrastructure supports such high-density deployments with DLC options and scalable rack-level power distribution.

Cost and Value Breakdown

L40: High Efficiency at Lower Cost

The L40 provides strong value for applications that do not require maximum parallel processing. It supports GPU-accelerated rendering, interactive graphics, and VDI at a lower cost of ownership, making it a favorite among media production houses, CAD users, and engineering teams.

L40S: Premium Compute for Demanding AI

While the L40S is priced higher, it delivers premium compute performance and multi-GPU scalability. For AI infrastructure architects building out training pipelines, containerized inference services, or edge AI orchestration systems, the L40S is a strategic long-term investment.

Deployment and Infrastructure Requirements

Cooling Considerations

The L40S’s higher thermal design power (TDP) necessitates robust cooling solutions. Hydra Host supports:

• Direct Liquid Cooling (DLC)
• High-efficiency fans with hot-aisle containment
• Thermal zone monitoring for heat-intensive workloads

The L40, in contrast, can be deployed in environments with standard airflow designs, making it suitable for edge data centers or VDI deployments with space constraints.

Power and Network Integration

Both GPUs benefit from high-bandwidth I/O options such as PCIe Gen4. When deploying multiple L40S units, ensure that the power distribution units (PDUs) and redundant power supplies can support peak loads.

Interconnects such as NVLink (where supported) further enhance multi-GPU throughput, critical for parallel training tasks.

Practical Use Case Summary

Choosing the Right GPU for Your Deployment

• Choose the L40 if your infrastructure is designed for graphics-intensive applications with moderate compute needs. It delivers excellent energy efficiency and sufficient performance for rendering, virtualization, and design workstations.
• Choose the L40S if you are scaling AI training pipelines, deploying inference clusters, or integrating advanced simulation with ML components. It is the preferred option for environments where compute density and parallelism matter most.

Conclusion: Performance Tuned for Specific Needs

The NVIDIA L40 and L40S represent two highly capable options in the Ada Lovelace GPU lineup, each optimized for a different class of enterprise workload. From 3D modeling to generative AI, their shared architecture delivers flexibility, while their compute characteristics allow for precise workload targeting.

For Hydra Host clients building next-gen AI infrastructure, the choice between these two GPUs depends on performance demands, power availability, and budget strategy. Both cards are fully supported within Hydra Host’s bare metal GPU server environments, ensuring seamless deployment and workload scalability.

Key Takeaways

• Both GPUs use NVIDIA's Ada Lovelace architecture, featuring advanced Tensor and RT cores.
• The L40 excels in visualization, rendering, and energy efficiency, ideal for CAD, VDI, and budget-conscious deployments.
• The L40S delivers superior AI throughput, optimized for training, inference, and HPC workloads.
• Power and cooling demands are higher with the L40S, requiring infrastructure like Hydra Host’s DLC-ready servers.
• Selecting the right GPU depends on your workload type, budget, and scalability goals.