Qwen 3.5 397B-A17B — B200 vs H100
Head-to-head AI inference benchmark comparison of B200 (NVIDIA Blackwell) and H100 (NVIDIA Hopper) on Qwen 3.5 397B-A17B. Latency, throughput, and cost across LLM workloads. Use the chart controls below to switch sequences, precisions, and metrics — same interactions as the main inference chart.
At 66 tok/s/user interactivity on Qwen 3.5 397B-A17B, B200 delivers 3409 tok/s/GPU at $0.16 per million tokens; H100 delivers 422 tok/s/GPU at $0.87. B200 is 446% cheaper per token; B200 delivers 709% more tok/s/GPU at this point.
B200 posts 1731 tok/s/GPU for $0.32 per million tokens at 101 tok/s/user on Qwen 3.5 397B-A17B; H100 posts 308 tok/s/GPU for $1.16. B200 is 266% cheaper per token; B200 delivers 462% more tok/s/GPU.
Throughput at 136 tok/s/user on Qwen 3.5 397B-A17B: B200 hits 1096 tok/s/GPU, H100 hits 234. Per-million costs land at $0.49 and $1.55 respectively. B200 is 214% cheaper per token; B200 delivers 368% more tok/s/GPU. (Numbers reflect the default 1k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/gpu) | B200:3408.7H100:421.6 | B200:1730.6H100:308.0 | B200:1095.7H100:233.9 |
| Cost ($/M tok) | B200:$0.160H100:$0.873 | B200:$0.317H100:$1.159 | B200:$0.494H100:$1.553 |
| tok/s/MW | B200:1570810H100:243681 | B200:797489H100:178055 | B200:504913H100:135210 |
| Concurrency | B200:~112H100:~26 | B200:~38H100:~12 | B200:~18H100:~7 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.