H200's higher hourly rate doesn't automatically mean a higher cost per token; it depends entirely on whether the memory and bandwidth advantage translates into fewer GPUs or faster throughput for your specific workload. For a model that needed two H100s in tensor-parallel purely to fit KV cache, moving to a single H200 can lower total cost per token despite the higher per-GPU rate, since you're no longer paying for two GPUs. For a model that already fits comfortably on one H100, H200 typically raises cost per token for the same throughput, since compute is identical but the hourly rate is higher. H100 cost per token covers the same batching, quantization and engine levers that apply equally to H200.
The naive comparison, H200's $4.40/hr against H100's $3.33/hr, misses the actual question, which is total cost per token for the complete setup a given model needs. A model that fits comfortably on one H100 sees no benefit from H200 at all, since compute is identical between the two generations; the higher hourly rate directly raises cost per token for the exact same work. The calculation flips for models that need multi-GPU tensor-parallel splitting on H100 purely to fit KV cache or optimizer state, not because the compute demands it: two H100s at their combined hourly rate can cost more in total than one H200, and if the single H200 achieves comparable throughput, its cost per token comes out lower despite the higher per-GPU rate. The practical rule is straightforward: check whether your model needs multi-GPU splitting on H100 for memory reasons before assuming H200's premium is automatically a worse deal.
The hourly rate you pay for H200 capacity is one input into cost per token, and it varies meaningfully by region and provider. See H200 availability by country below.
Read the full guide to GPU cloud in this location →Wholesale rates against cloud list price for a 64-GPU cluster.
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