NVIDIA L40 vs L40S: Complete Performance Comparison & Buying Guide 2026

Core Architecture and Performance

Both the L40 and L40S are built on NVIDIA’s revolutionary Ada Lovelace architecture, representing a quantum leap in GPU technology compared to previous generations. This shared architectural foundation includes an impressive array of 18,176 CUDA cores, 568 fourth-generation Tensor cores, and 142 third-generation RT cores. This architectural similarity means both GPUs excel remarkably in parallel processing tasks, AI workloads, and graphics rendering applications, delivering professional-grade performance across a wide spectrum of use cases.

The key differentiator between these two powerhouses lies in their optimization focus and performance tuning. The L40S features significantly enhanced AI capabilities with superior FP16 performance reaching an impressive 733 TFLOPS compared to the L40’s 362 TFLOPS. This substantial performance advantage makes the L40S particularly effective for demanding AI training and inference workloads, where half-precision floating-point operations are critical for achieving optimal performance and efficiency.

Memory and Bandwidth Architecture

Both GPUs feature identical memory configurations with a substantial 48GB of GDDR6 ECC memory and an impressive 864 GB/s memory bandwidth. This generous memory capacity enables seamless handling of large datasets, complex 3D models, and extensive AI models without encountering memory constraints that could bottleneck performance. The inclusion of ECC (Error-Correcting Code) memory ensures data integrity throughout computational processes, which is absolutely crucial for professional applications, scientific computing, and long-running computations where data accuracy is paramount.

The 864 GB/s memory bandwidth represents a significant improvement over previous generation GPUs, ensuring that data can be rapidly transferred between the GPU memory and processing cores. This high bandwidth is particularly beneficial for memory-intensive workloads such as large language model inference, high-resolution video processing, and complex scientific simulations that require frequent data access patterns.

AI and Machine Learning Performance

The L40S demonstrates superior AI performance capabilities with comprehensive support for FP8 precision and enhanced Tensor Core capabilities that have been specifically optimized for modern AI workloads. According to extensive NVIDIA benchmarks and real-world testing, the L40S delivers up to an astounding 1,466 TFLOPS in FP8 Tensor Core performance, significantly outpacing the L40’s capabilities in this critical metric. This performance advantage translates directly into faster training times, higher inference throughput, and improved efficiency for AI-driven applications.

For AI inference tasks specifically, the L40S shows remarkable performance improvements that make it an attractive choice for production AI deployments. The GPU delivers up to 5X higher inference performance compared to previous generation GPUs, positioning it as a compelling upgrade path for organizations looking to modernize their AI infrastructure. Additionally, the L40S achieves 1.2X generative AI inference performance compared to the A100, and demonstrates a 1.7X training performance advantage over A100 in specific workloads, particularly those involving transformer-based models and large language models.

The inclusion of NVIDIA’s innovative Transformer Engine in the L40S provides automatic precision optimization between FP8 and FP16 formats, dramatically accelerating AI performance while simultaneously improving memory utilization for both training and inference phases. This intelligent precision management ensures optimal performance without requiring manual intervention or extensive model tuning, making it easier for data scientists and ML engineers to achieve peak performance.

Graphics and Rendering Capabilities

Both GPUs excel remarkably in graphics-intensive applications thanks to their powerful third-generation RT cores and substantial compute capabilities. The L40 offers an impressive 209 TFLOPS of RT core performance, while the L40S provides a slight edge at 212 TFLOPS. This exceptional ray tracing performance makes both GPUs ideal for a wide range of professional graphics applications and creative workflows.

These GPUs are particularly well-suited for real-time ray tracing applications, delivering up to 2X performance improvement over previous generations. This performance boost enables more realistic lighting, reflections, and shadows in real-time applications, enhancing the visual fidelity of everything from architectural visualizations to virtual production environments. For 3D rendering and visualization workflows, both GPUs provide accelerated performance for CAD applications, visual effects production, and architectural visualization, significantly reducing rendering times and improving iterative design processes.

The GPUs also excel in virtual production environments, supporting high-fidelity creative workflows that demand real-time rendering capabilities. Both the L40 and L40S are fully compatible with NVIDIA Omniverse, enabling cutting-edge metaverse applications, digital twin simulations, and collaborative 3D design workflows that are becoming increasingly important in modern creative and engineering pipelines.

Power Efficiency and Form Factor

The L40 operates at a maximum power consumption of 300W, making it a relatively power-efficient choice for data center deployments where power and cooling costs are significant considerations. The L40S, meanwhile, requires 350W maximum power due to its enhanced performance capabilities and more aggressive performance tuning. While this represents a 50W increase, the additional power consumption is justified by the substantial performance improvements in AI workloads, particularly for organizations where AI performance is the primary priority.

Both GPUs utilize a dual-slot PCIe form factor, ensuring broad compatibility with standard server and workstation configurations. This standardized form factor makes it straightforward to integrate these GPUs into existing infrastructure without requiring specialized chassis or cooling solutions. The passive cooling design employed in both GPUs makes them particularly suitable for multi-GPU deployments in data center environments, where multiple GPUs can be densely packed while maintaining thermal stability and reliability.