Is the NVIDIA H200 Faster Than the H100 for Inference?

Q: Is the NVIDIA H200 faster than the H100 for AI inference?

For memory-bound LLM inference, yes: the H200’s higher HBM3e bandwidth (4.8 TB/s vs 3.35-3.9 TB/s) directly raises tokens-per-second, even though it shares the H100’s Hopper compute cores.

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LLM inference is overwhelmingly memory-bandwidth-bound, so throughput scales closely with memory bandwidth rather than raw FLOPS. The H200’s roughly 40% bandwidth advantage translates fairly directly into higher tokens-per-second at the same precision, and its 141GB capacity supports larger KV caches and longer context windows.

For compute-bound work limited by FLOPS rather than memory, the two are close, since both are Hopper-class on raw tensor throughput. The H200’s real edge is single-card model fit: a 70B model that needs two H100s fits on one H200, removing tensor-parallel sharding overhead and often improving end-to-end serving efficiency.

OpenMetal offers the H200 today as single-tenant bare metal; NVIDIA AI Enterprise (NVAIE) is available for H200 deployments (contact OpenMetal for details), and the H100 is no longer carried. For smaller models that are not memory-constrained, the lower-cost RP6000 may be the better economic fit.