Qwen 3.5 397B-A17B — B300 vs GB200 NVL72
Head-to-head AI inference benchmark comparison of B300 (NVIDIA Blackwell) and GB200 NVL72 (NVIDIA Blackwell) 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 84 tok/s/user interactivity on Qwen 3.5 397B-A17B, B300 delivers 5236 tok/s/GPU at $0.12 per million tokens; GB200 NVL72 delivers 10287 tok/s/GPU at $0.06. GB200 NVL72 is 109% cheaper per token; GB200 NVL72 delivers 96% more tok/s/GPU at this point.
B300 posts 3536 tok/s/GPU for $0.18 per million tokens at 133 tok/s/user on Qwen 3.5 397B-A17B; GB200 NVL72 posts 4367 tok/s/GPU for $0.14. GB200 NVL72 is 29% cheaper per token; GB200 NVL72 delivers 23% more tok/s/GPU.
Throughput at 182 tok/s/user on Qwen 3.5 397B-A17B: B300 hits 2591 tok/s/GPU, GB200 NVL72 hits 2380. Per-million costs land at $0.25 and $0.26 respectively. B300 is 3% cheaper per token; B300 delivers 9% more tok/s/GPU. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/gpu) | B300:5235.8GB200 NVL72:10287.4 | B300:3536.4GB200 NVL72:4366.7 | B300:2591.0GB200 NVL72:2379.6 |
| Cost ($/M tok) | B300:$0.124GB200 NVL72:$0.059 | B300:$0.183GB200 NVL72:$0.141 | B300:$0.252GB200 NVL72:$0.258 |
| tok/s/MW | B300:2755660GB200 NVL72:5501292 | B300:1861267GB200 NVL72:2335117 | B300:1363679GB200 NVL72:1272500 |
| Concurrency | B300:~29GB200 NVL72:~987 | B300:~12GB200 NVL72:~84 | B300:~7GB200 NVL72:~32 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.