Qwen 3.5 397B-A17B — GB300 NVL72 vs RTX PRO 6000 Performance per Dollar
Cost per million tokens of GB300 NVL72 (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.
At 56 tok/s/user on Qwen 3.5 397B-A17B, GB300 NVL72 costs $0.02 per million tokens; RTX PRO 6000 costs $0.18. GB300 NVL72 is 989% more cost-efficient at this operating point.
GB300 NVL72 edges RTX PRO 6000 at 88 tok/s/user on Qwen 3.5 397B-A17B — $0.02 per million tokens versus $0.25, a 1305% cost-per-token gap.
Push Qwen 3.5 397B-A17B to 121 tok/s/user and GB300 NVL72 lands at $0.02 per million tokens against RTX PRO 6000's $0.40 — GB300 NVL72 pulls ahead by 1756%. (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): GB300 NVL72 $2.31/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 | GB300 NVL72:$0.017RTX PRO 6000:$0.184 | GB300 NVL72:$0.018RTX PRO 6000:$0.247 | GB300 NVL72:$0.022RTX PRO 6000:$0.400 |
| Concurrency | GB300 NVL72:~2983RTX PRO 6000:~10 | GB300 NVL72:~1205RTX PRO 6000:~4 | GB300 NVL72:~1088RTX PRO 6000:~2 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.