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.
GB300 NVL72 edges GB200 NVL72 at 67 tok/s/user on Qwen 3.5 397B-A17B — $0.53 per million tokens versus $0.56, a 6% cost-per-token gap.
Push Qwen 3.5 397B-A17B to 103 tok/s/user and GB200 NVL72 lands at $1.35 per million tokens against GB300 NVL72's $1.27 — GB300 NVL72 pulls ahead by 7%.
GB200 NVL72: $3.06 per million tokens. GB300 NVL72: $2.27. Both at 138 tok/s/user on Qwen 3.5 397B-A17B, with GB300 NVL72 35% cheaper. (Numbers reflect the default 1k/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): GB200 NVL72 $2.21/GPU/hr · GB300 NVL72 $2.65/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 | GB200 NVL72:$0.557GB300 NVL72:$0.526 | GB200 NVL72:$1.350GB300 NVL72:$1.267 | GB200 NVL72:$3.057GB300 NVL72:$2.268 |
| Concurrency | GB200 NVL72:~79GB300 NVL72:~147 | GB200 NVL72:~19GB300 NVL72:~24 | GB200 NVL72:~6GB300 NVL72:~10 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.