Qwen 3.5 397B-A17B — GB300 NVL72 vs H100
Head-to-head AI inference benchmark comparison of GB300 NVL72 (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.
GB300 NVL72 / H100 on Qwen 3.5 397B-A17B at 73 tok/s/user: 11900 / 1329 tok/s/GPU, $0.06 / $0.27 per million tokens. GB300 NVL72 is 340% cheaper per token; GB300 NVL72 delivers 795% more tok/s/GPU.
Around the middle of the 40–172 tok/s/user interactivity band, at 106 tok/s/user on Qwen 3.5 397B-A17B: GB300 NVL72 runs 8493 tok/s/GPU at $0.09/M tokens, H100 runs 1066 at $0.34/M. GB300 NVL72 is 290% cheaper per token; GB300 NVL72 delivers 697% more tok/s/GPU.
Setting 139 tok/s/user as the target on Qwen 3.5 397B-A17B, GB300 NVL72 produces 4513 tok/s/GPU ($0.16 per million tokens) and H100 produces 936 ($0.41). GB300 NVL72 is 151% cheaper per token; GB300 NVL72 delivers 382% more tok/s/GPU. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/gpu) | GB300 NVL72:11900.0H100:1328.9 | GB300 NVL72:8493.0H100:1066.1 | GB300 NVL72:4512.8H100:935.7 |
| Cost ($/M tok) | GB300 NVL72:$0.062H100:$0.272 | GB300 NVL72:$0.087H100:$0.338 | GB300 NVL72:$0.163H100:$0.409 |
| tok/s/MW | GB300 NVL72:5613207H100:970005 | GB300 NVL72:4006133H100:778172 | GB300 NVL72:2128670H100:682999 |
| Concurrency | GB300 NVL72:~1315H100:~17 | GB300 NVL72:~541H100:~11 | GB300 NVL72:~80H100:~9 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.