H100 SXM5 vs L40S

The H100's FP16 performance of 1979 TFLOPS dwarfs the L40S's 362 TFLOPS, enabling dramatically faster neural network training where half-precision computations dominate, such as in large language model optimization. Conversely, the L40S edges out in FP32 at 91 TFLOPS over the H100's 67 TFLOPS, benefiting traditional scientific simulations or graphics rendering that rely on single-precision floating point. FP8 metrics further favor the H100 at 3958 TFLOPS versus 724 TFLOPS, accelerating quantized inference for massive models.

Memory specifications create clear divides in real-world usage: the H100's 3350 GB/s bandwidth and 80 to 94 GB HBM3 capacity support enormous batch sizes and models exceeding 48 GB, preventing out-of-memory errors in training runs with billion-parameter transformers. The L40S's 864 GB/s and 48 GB GDDR6X limit it to smaller batches or models, though its lower 350W TDP reduces cooling demands and operational costs. Interconnects amplify this: H100's NVLink and PCIe 5.0 enable multi-GPU scaling, while L40S's PCIe 4.0 suits standalone or modest clusters.

These deltas translate to H100 excelling in memory-intensive training pipelines, where bandwidth bottlenecks throttle the L40S, but L40S handles inference or fine-tuning of mid-sized models efficiently without the H100's power overhead.

Opt for the H100 SXM5 in scenarios demanding peak AI performance, such as training large language models requiring over 48 GB VRAM and 1979 TFLOPS FP16 throughput. Its 3350 GB/s bandwidth sustains massive batch sizes in distributed setups via NVLink, ideal for research labs or enterprises scaling to hundreds of GPUs. Cloud deployments averaging $3.54 per hour justify the cost for workloads where time-to-result trumps expense.

High-performance computing tasks like FP8-optimized inference at 3958 TFLOPS also favor H100, especially in SXM5 form factors for dense server racks.

The L40S suits cost-sensitive applications with its pricing from $0.40 per hour and average $1.17 per hour, delivering solid 362 TFLOPS FP16 for inference on models fitting within 48 GB GDDR6X. Lower 350W TDP minimizes energy costs in edge datacenters or prolonged runs, and PCIe 4.0 simplifies integration without specialized infrastructure.

Choose L40S for FP32-heavy workloads at 91 TFLOPS, such as visualization or mid-scale fine-tuning where H100's 700W draw and higher price prove excessive.