Qwen 3.5 397B-A17B — GB200 NVL72 vs GB300 NVL72 Performance per Dollar
Cost per million tokens of GB200 NVL72 (NVIDIA Blackwell) versus GB300 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.
GB200 NVL72 edges GB300 NVL72 at 129 tok/s/user on Qwen 3.5 397B-A17B — $0.07 per million tokens versus $0.12, a 85% cost-per-token gap.
Push Qwen 3.5 397B-A17B to 211 tok/s/user and GB200 NVL72 lands at $0.21 per million tokens against GB300 NVL72's $0.35 — GB200 NVL72 pulls ahead by 69%.
GB200 NVL72: $0.41 per million tokens. GB300 NVL72: $0.81. Both at 293 tok/s/user on Qwen 3.5 397B-A17B, with GB200 NVL72 96% cheaper. (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.)
Chip pricing (owning hyperscaler): GB200 NVL72 $1.86/chip/hr · GB300 NVL72 $2.31/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 | GB200 NVL72:$0.065GB300 NVL72:$0.120 | GB200 NVL72:$0.206GB300 NVL72:$0.348 | GB200 NVL72:$0.415GB300 NVL72:$0.812 |
| Concurrency | GB200 NVL72:~467GB300 NVL72:~140 | GB200 NVL72:~25GB300 NVL72:~17 | GB200 NVL72:~5GB300 NVL72:~3 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.