H100 vs L40S

The H100's superior FP16 throughput of 1979 TFLOPS vastly outpaces the L40S's 362 TFLOPS, enabling dramatically faster neural network training where mixed-precision computations dominate. This delta translates to shorter training cycles for large language models, as FP16 handles the bulk of tensor core operations. Conversely, the L40S edges ahead in FP32 at 91 TFLOPS over the H100's 67 TFLOPS, benefiting workloads like scientific simulations reliant on single-precision arithmetic.

Memory bandwidth profoundly impacts real-world usage: the H100's 3350 GB/s supports larger batch sizes and quicker data transfers for models exceeding 48 GB VRAM, preventing out-of-memory errors common on the L40S. For inference, the H100's FP8 performance of 3958 TFLOPS accelerates quantized deployments, reducing latency for high-throughput serving. Power consumption differs too, with the H100 at 700W TDP demanding robust cooling versus the L40S's efficient 350W.

Interconnect options further the gap: H100 supports NVLink, PCIe 5.0, and InfiniBand for multi-GPU scaling, while L40S limits to PCIe 4.0, constraining cluster performance.

The H100 excels in scenarios demanding extreme compute and memory capacity, such as training massive LLMs requiring over 48 GB VRAM and 1979 TFLOPS FP16. Its 3350 GB/s bandwidth ensures seamless handling of large batches, ideal for research labs or enterprises scaling models across NVLink-connected nodes. Cloud users prioritizing raw performance over cost will favor it, especially at minimum pricing of $0.80 per hour.

Opt for the L40S in cost-sensitive deployments or FP32-dominant tasks like visualization and scientific computing, where its 91 TFLOPS FP32 surpasses the H100's 67 TFLOPS. Lower TDP of 350W suits dense server racks with power constraints, and average pricing of $1.66 per hour offers value for inference at moderate scales. PCIe form factor simplifies integration without specialized infrastructure.