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 112 tok/s/user and B300 lands at $0.06 per million tokens against GB300 NVL72's $0.02 — GB300 NVL72 pulls ahead by 202%.
B300: $0.10 per million tokens. GB300 NVL72: $0.04. Both at 196 tok/s/user on Qwen 3.5 397B-A17B, with GB300 NVL72 134% cheaper.
Toward the upper edge of the 28–364 tok/s/user interactivity band — at 280 tok/s/user — B300 runs $0.14 per million tokens on Qwen 3.5 397B-A17B while GB300 NVL72 runs $0.06. GB300 NVL72 is the cheaper choice by 121%. (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 · 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 | B300:$0.060GB300 NVL72:$0.020 | B300:$0.104GB300 NVL72:$0.044 | B300:$0.139GB300 NVL72:$0.063 |
| Concurrency | B300:~22GB300 NVL72:~1151 | B300:~16GB300 NVL72:~243 | B300:~4GB300 NVL72:~135 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.