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 87 tok/s/user and B300 lands at $0.13 per million tokens against GB300 NVL72's $0.07 — GB300 NVL72 pulls ahead by 88%.
B300: $0.18 per million tokens. GB300 NVL72: $0.15. Both at 135 tok/s/user on Qwen 3.5 397B-A17B, with GB300 NVL72 20% cheaper.
Toward the upper edge of the 40–230 tok/s/user interactivity band — at 183 tok/s/user — B300 runs $0.25 per million tokens on Qwen 3.5 397B-A17B while GB300 NVL72 runs $0.27. B300 is the cheaper choice by 8%. (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.)
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:$0.127GB300 NVL72:$0.068 | B300:$0.185GB300 NVL72:$0.154 | B300:$0.253GB300 NVL72:$0.274 |
| Concurrency | B300:~27GB300 NVL72:~1100 | B300:~12GB300 NVL72:~95 | B300:~7GB300 NVL72:~37 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.