Qwen 3.5 397B-A17B — B300 vs GB300 NVL72 Performance per Dollar
Cost per million tokens of B300 (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.
Push Qwen 3.5 397B-A17B to 90 tok/s/user and B300 lands at $0.07 per million tokens against GB300 NVL72's $0.12 — B300 pulls ahead by 85%.
B300: $0.10 per million tokens. GB300 NVL72: $0.26. Both at 136 tok/s/user on Qwen 3.5 397B-A17B, with B300 166% cheaper.
Toward the upper edge of the 44–228 tok/s/user interactivity band — at 183 tok/s/user — B300 runs $0.14 per million tokens on Qwen 3.5 397B-A17B while GB300 NVL72 runs $0.55. B300 is the cheaper choice by 292%. (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.)
GPU pricing (owning hyperscaler): B300 $2.34/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 | B300:$0.066GB300 NVL72:$0.122 | B300:$0.097GB300 NVL72:$0.256 | B300:$0.141GB300 NVL72:$0.553 |
| Concurrency | B300:~26GB300 NVL72:~71 | B300:~12GB300 NVL72:~21 | B300:~6GB300 NVL72:~7 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.