Qwen 3.5 397B-A17B — B300 vs RTX PRO 6000 Performance per Dollar
Cost per million tokens of B300 (NVIDIA Blackwell) versus RTX PRO 6000 (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.
Near the low end of the 28–153 tok/s/user interactivity band — at 59 tok/s/user — B300 runs $0.04 per million tokens on Qwen 3.5 397B-A17B while RTX PRO 6000 runs $0.20. B300 is the cheaper choice by 361%.
On Qwen 3.5 397B-A17B at 91 tok/s/user, the per-million math comes out to $0.06 for B300 and $0.26 for RTX PRO 6000; B300 delivers 355% more output per dollar.
At 122 tok/s/user on Qwen 3.5 397B-A17B, B300 costs $0.07 per million tokens; RTX PRO 6000 costs $0.37. B300 is 416% more cost-efficient at this operating point. (Numbers reflect the default 8k/1k · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
Chip pricing (owning hyperscaler): B300 $2.26/chip/hr · RTX PRO 6000 $0.68/chip/hr. Source: SemiAnalysis Market July 2026 Pricing Surveys & AI Cloud TCO Model.
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Dollar per Million Tokens | B300:$0.043RTX PRO 6000:$0.199 | B300:$0.057RTX PRO 6000:$0.257 | B300:$0.072RTX PRO 6000:$0.369 |
| Concurrency | B300:~60RTX PRO 6000:~9 | B300:~30RTX PRO 6000:~4 | B300:~18RTX PRO 6000:~2 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.