Qwen 3.5 397B-A17B — B300 vs H200
Head-to-head AI inference benchmark comparison of B300 (NVIDIA Blackwell) and H200 (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.
Throughput at 68 tok/s/user on Qwen 3.5 397B-A17B: B300 hits 2918 tok/s/GPU, H200 hits 490. Per-million costs land at $0.22 and $0.80 respectively. B300 is 262% cheaper per token; B300 delivers 495% more tok/s/GPU.
B300 / H200 on Qwen 3.5 397B-A17B at 106 tok/s/user: 1559 / 351 tok/s/GPU, $0.42 / $1.10 per million tokens. B300 is 165% cheaper per token; B300 delivers 344% more tok/s/GPU.
Toward the upper edge of the 30–182 tok/s/user interactivity band, at 144 tok/s/user on Qwen 3.5 397B-A17B: B300 runs 1036 tok/s/GPU at $0.63/M tokens, H200 runs 284 at $1.37/M. B300 is 120% cheaper per token; B300 delivers 264% more tok/s/GPU. (Numbers reflect the default 1k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/gpu) | B300:2918.4H200:490.2 | B300:1559.2H200:351.0 | B300:1035.7H200:284.4 |
| Cost ($/M tok) | B300:$0.221H200:$0.799 | B300:$0.416H200:$1.102 | B300:$0.626H200:$1.374 |
| tok/s/MW | B300:1344867H200:283348 | B300:718509H200:202875 | B300:477260H200:164386 |
| Concurrency | B300:~87H200:~29 | B300:~31H200:~13 | B300:~15H200:~8 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.