Qwen 3.5 397B-A17B — GB200 NVL72 vs H100
Head-to-head AI inference benchmark comparison of GB200 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.
Throughput at 70 tok/s/user on Qwen 3.5 397B-A17B: GB200 NVL72 hits 11175 tok/s/GPU, H100 hits 1371. Per-million costs land at $0.05 and $0.26 respectively. GB200 NVL72 is 381% cheaper per token; GB200 NVL72 delivers 715% more tok/s/GPU.
GB200 NVL72 / H100 on Qwen 3.5 397B-A17B at 104 tok/s/user: 7878 / 1075 tok/s/GPU, $0.08 / $0.33 per million tokens. GB200 NVL72 is 330% cheaper per token; GB200 NVL72 delivers 633% more tok/s/GPU.
Toward the upper edge of the 36–172 tok/s/user interactivity band, at 138 tok/s/user on Qwen 3.5 397B-A17B: GB200 NVL72 runs 4007 tok/s/GPU at $0.15/M tokens, H100 runs 941 at $0.41/M. GB200 NVL72 is 169% cheaper per token; GB200 NVL72 delivers 326% more tok/s/GPU. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/gpu) | GB200 NVL72:11174.9H100:1370.8 | GB200 NVL72:7878.1H100:1075.2 | GB200 NVL72:4007.0H100:940.6 |
| Cost ($/M tok) | GB200 NVL72:$0.055H100:$0.264 | GB200 NVL72:$0.078H100:$0.335 | GB200 NVL72:$0.151H100:$0.407 |
| tok/s/MW | GB200 NVL72:5975892H100:1000563 | GB200 NVL72:4212881H100:784834 | GB200 NVL72:2142774H100:686577 |
| Concurrency | GB200 NVL72:~1305H100:~20 | GB200 NVL72:~483H100:~11 | GB200 NVL72:~72H100:~9 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.