Qwen 3.5 397B-A17B — B300 vs GB200 NVL72 Performance per Dollar
Cost per million tokens of B300 (NVIDIA Blackwell) versus GB200 NVL72 (NVIDIA Blackwell) on Qwen 3.5 397B-A17B. Owning-hyperscaler TCO normalized by output tokens — performance per dollar across LLM workloads. Pick the more cost-efficient SKU at every target interactivity level. Use the chart controls below to switch sequences, precisions, and metrics — same interactions as the main inference chart.
On Qwen 3.5 397B-A17B at 84 tok/s/user, the per-million math comes out to $0.12 for B300 and $0.06 for GB200 NVL72; GB200 NVL72 delivers 109% more output per dollar.
At 133 tok/s/user on Qwen 3.5 397B-A17B, B300 costs $0.18 per million tokens; GB200 NVL72 costs $0.14. GB200 NVL72 is 29% more cost-efficient at this operating point.
B300 edges GB200 NVL72 at 182 tok/s/user on Qwen 3.5 397B-A17B — $0.25 per million tokens versus $0.26, a 3% cost-per-token gap. (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.)
GPU pricing (owning hyperscaler): B300 $2.34/GPU/hr · GB200 NVL72 $2.21/GPU/hr. Source: SemiAnalysis Market August 2025 Pricing Surveys & AI Cloud TCO Model.
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Dollar per Million Tokens | B300:$0.124GB200 NVL72:$0.059 | B300:$0.183GB200 NVL72:$0.141 | B300:$0.252GB200 NVL72:$0.258 |
| 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.