H200 changes the inference math in one specific way: it keeps H100's exact compute and power draw but adds 61GB of HBM3e memory and roughly 43% more bandwidth, so the gain shows up almost entirely in what a single card can hold and how fast it feeds tokens out, not in raw FLOPS. A 70B model that needed two H100s in tensor-parallel to leave headroom for KV cache often fits comfortably on one H200, and time-to-first-token drops by around 40% on long-context requests thanks to the bandwidth increase. For teams already running vLLM, SGLang or TensorRT-LLM on H100, the same engines and FP8 quantization path carry over directly to H200 with no re-architecture, which is why it's usually a drop-in upgrade rather than a new deployment decision. H100 for LLM inference remains the more cost-effective pick if your models comfortably fit in 80GB.
H200 and H100 share the same Hopper compute die, so the performance difference between them is entirely about memory: 141GB of HBM3e versus 80GB, and roughly 4.8 TB/s of bandwidth versus H100's 3.35 TB/s. Inference splits into two phases with different bottlenecks, a compute-bound prefill phase that processes the prompt and a memory-bandwidth-bound decode phase that generates tokens one at a time, and H200's bandwidth increase speeds up both, cutting time-to-first-token by roughly 40% on long-context requests specifically. The memory increase matters most for models near or above what comfortably fits in H100's 80GB: a 70B model that needed two H100s in tensor-parallel to leave real KV cache headroom for long conversations often fits on a single H200 instead, changing the cost-per-token math for that workload without requiring any change to the serving engine or quantization approach already in use.
H200 inference is placement-sensitive: prompts and responses are often personal data, so jurisdiction matters as much as latency. See H200 availability by country below.
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