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 84 tok/s/user and GB300 NVL72 lands at $0.06 per million tokens against H100's $0.18 — GB300 NVL72 pulls ahead by 213%.
GB300 NVL72: $0.10 per million tokens. H100: $0.24. Both at 120 tok/s/user on Qwen 3.5 397B-A17B, with GB300 NVL72 144% cheaper.
Toward the upper edge of the 48–192 tok/s/user interactivity band — at 156 tok/s/user — GB300 NVL72 runs $0.17 per million tokens on Qwen 3.5 397B-A17B while H100 runs $0.32. GB300 NVL72 is the cheaper choice by 83%. (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.)
Chip pricing (owning hyperscaler): GB300 NVL72 $2.31/chip/hr · H100 $1.17/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 | GB300 NVL72:$0.058H100:$0.181 | GB300 NVL72:$0.100H100:$0.243 | GB300 NVL72:$0.174H100:$0.317 |
| Concurrency | GB300 NVL72:~1184H100:~19 | GB300 NVL72:~257H100:~10 | GB300 NVL72:~56H100:~6 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.