H200 SXM vs L40

H200's FP16 performance of 1979 TFLOPS vastly outpaces L40's 90.5 TFLOPS, enabling over 20-fold faster tensor operations critical for deep learning training and inference. This delta favors H200 in AI pipelines where low-precision computations dominate: training large language models processes batches quicker, reducing wall-clock time. L40's balanced 90.5 TFLOPS across FP16 and FP32 suits graphics or simulation tasks requiring precise floating-point math, where H200's 67 TFLOPS FP32 trails.

Memory bandwidth defines scalability: H200's 4800 GB/s versus L40's 864 GB/s supports batch sizes up to 5x larger, minimizing data movement bottlenecks in inference serving. H200's 141 GB VRAM accommodates models like 70B-parameter LLMs fully on one GPU, avoiding multi-GPU complexity that L40's 48 GB necessitates. Power draw impacts density: L40's 300W TDP allows more units per rack, ideal for cost-sensitive inference at scale.

FP8 capability on H200 at 3958 TFLOPS further accelerates quantized inference, a growing standard for production deployment.

NVIDIA H200 stands out for workloads demanding massive VRAM, such as training or inferring on models exceeding 48 GB like 100B+ parameter LLMs. Its 141 GB HBM3e and 4800 GB/s bandwidth enable single-GPU operation, simplifying setups versus L40's fragmentation. Cloud users prioritize H200 across 22 offers averaging $3.71/hr when throughput trumps cost.

NVIDIA L40 fits budget-conscious scenarios with smaller models under 48 GB VRAM, delivering 90.5 TFLOPS FP16 at $0.67/hr starting price. Its 300W TDP and PCIe form factor support dense deployments for fine-tuning or Stable Diffusion, where H200's 700W overkill inflates costs. L40 excels in visualization or multi-GPU inference averaging $0.89/hr.