H200 SXM vs L40S

Memory specifications dominate real-world differences: the H200's 141 GB HBM3e VRAM supports models exceeding 100 billion parameters without multi-GPU sharding, unlike the L40S limited to 48 GB GDDR6X. Bandwidth of 4800 GB/s on the H200 enables larger batch sizes in training, reducing time per epoch, while 864 GB/s on the L40S constrains throughput for memory-bound workloads.

FP16 performance favors the H200 at 1979 TFLOPS over the L40S's 362 TFLOPS, accelerating mixed-precision training by over 5x. FP8 inference sees similar gains: 3958 TFLOPS versus 724 TFLOPS, ideal for high-throughput serving of quantized LLMs. However, FP32 rates reverse with L40S at 91 TFLOPS exceeding H200's 67 TFLOPS, benefiting simulations requiring single-precision accuracy.

Power draw reflects capabilities: H200 TDP at 700W demands robust cooling, while L40S at 350W suits dense deployments. Interconnects enhance H200 scalability via NVLink and PCIe 5.0, surpassing L40S PCIe 4.0.

The H200 excels in large-scale LLM training and inference where 141 GB VRAM handles massive models without fragmentation. Scenarios include pre-training 175B+ parameter transformers: 4800 GB/s bandwidth sustains high batch sizes, and 1979 TFLOPS FP16 cuts epochs significantly.

Enterprises with NVLink clusters prioritize the H200 for its 3958 TFLOPS FP8 in production inference at scale, despite $1.19/hr starting pricing.

The L40S suits cost-sensitive inference and fine-tuning of models under 70B parameters, leveraging 48 GB GDDR6X at $0.40/hr from pricing. Lower 350W TDP enables higher density in PCIe servers for Stable Diffusion or visualization pipelines.

FP32 workloads like scientific simulations favor L40S's 91 TFLOPS over H200's 67 TFLOPS, with PCIe 4.0 sufficient for single-node tasks.