NVIDIA H200 vs H100: Key Differences

Q: What is the difference between the NVIDIA H200 and H100?

The H200 and H100 share the same Hopper compute architecture; the H200’s advantage is memory, with 141GB of HBM3e versus the H100’s 80-94GB of HBM3 and roughly 40% more bandwidth.

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Because both use the same Hopper die, raw tensor throughput (FP8, BF16, FP64) is comparable at a given form factor and power, so a compute-bound workload sees only a modest generational gain. The H200’s value is in feeding that compute from a larger, faster memory pool: 4.8 TB/s versus 3.35-3.9 TB/s.

Capacity is the decisive difference. A 70B model in 16-bit (about 140GB) fits on a single H200 but requires two H100s with tensor-parallel sharding, so one H200 can replace two H100s for those model sizes, with simpler deployment and less inter-GPU latency.

OpenMetal carries the H200 (and the cost-efficient RP6000) and no longer carries the H100, so the practical choice is the H200 versus a lower-cost inference card. The H200 ships as single-tenant bare metal on fixed monthly pricing; NVIDIA AI Enterprise (NVAIE) is available for H200 deployments (contact OpenMetal for details).