H100 SXM5 vs H200 SXM

Compute performance remains identical between the H100 and H200: both achieve 1979 TFLOPS in FP16 for training large models and 67 TFLOPS in FP32 for general-purpose computing. This parity ensures equivalent throughput in compute-bound scenarios like matrix multiplications during LLM training or inference. The key distinction lies in memory: H200's 141 GB HBM3e versus H100's 80 to 94 GB HBM3 enables handling larger models or datasets without swapping to host memory. H200's 4800 GB/s bandwidth, compared to 3350 GB/s, accelerates data movement, supporting bigger batch sizes and longer context lengths in transformers. For inference, this reduces latency on high-concurrency workloads; for training, it shortens epochs by minimizing memory bottlenecks. Both GPUs maintain 700W TDP, so power efficiency per TFLOP stays consistent.

Opt for the H100 SXM5 in cost-sensitive deployments where 80 to 94 GB HBM3 VRAM suffices. It fits fine-tuning smaller LLMs or Stable Diffusion pipelines that rarely exceed 94 GB requirements. With pricing from $0.80 per hour and 35 live cloud offers averaging $3.54, it provides broader availability and lower entry costs than H200's $1.19 starting rate across 23 offers.

Select the H200 SXM for workloads demanding over 94 GB VRAM, such as training or inferring massive LLMs with billions of parameters. Its 141 GB HBM3e and 4800 GB/s bandwidth handle larger batches and sequences, outperforming H100 in memory-bound tasks. Despite slightly higher pricing from $1.19 per hour averaging $3.70, the capacity justifies it for cutting-edge AI research.