B200 vs H100 for model training

Choosing between NVIDIA H100 and Blackwell B200 GPUs for model training depends primarily on model memory needs, batch-size benefits, optimization effort, cost, and the value of faster iteration. H100s offer 80GB of memory and lower hourly prices, making them suitable for smaller workloads such as conventional vision-model fine-tuning, while B200s provide 192GB per GPU and can be advantageous for large models or workloads that benefit from substantially larger microbatches. In an example fine-tuning a 70B-parameter Llama model, moving from eight H100s to eight B200s reduced optimizer-step time by 18 percent without changes, while increasing the microbatch size using B200 memory reduced it from 36.8 to 19.4 seconds; H200s, with 160GB memory, showed more limited gains in the same test. The account notes that Blackwell’s advertised performance requires strong GPU utilization and Blackwell-specific software optimizations through tools such as cuDNN and TransformerEngine, so gains may be modest without tuning. It recommends benchmarking actual workloads rather than assuming newer hardware is automatically more economical, considering both listed availability and pricing differences between interruptible, on-demand, and reserved cloud instances.

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