Qwen 3.5 397B-A17B — GB300 NVL72 vs H100 Performance per Dollar
Cost per million tokens of GB300 NVL72 (NVIDIA Blackwell) versus H100 (NVIDIA Hopper) 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 73 tok/s/user and GB300 NVL72 lands at $0.06 per million tokens against H100's $0.27 — GB300 NVL72 pulls ahead by 340%.
GB300 NVL72: $0.09 per million tokens. H100: $0.34. Both at 106 tok/s/user on Qwen 3.5 397B-A17B, with GB300 NVL72 290% cheaper.
Toward the upper edge of the 40–172 tok/s/user interactivity band — at 139 tok/s/user — GB300 NVL72 runs $0.16 per million tokens on Qwen 3.5 397B-A17B while H100 runs $0.41. GB300 NVL72 is the cheaper choice by 151%. (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): GB300 NVL72 $2.65/GPU/hr · H100 $1.30/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 | GB300 NVL72:$0.062H100:$0.272 | GB300 NVL72:$0.087H100:$0.338 | GB300 NVL72:$0.163H100:$0.409 |
| Concurrency | GB300 NVL72:~1315H100:~17 | GB300 NVL72:~541H100:~11 | GB300 NVL72:~80H100:~9 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.