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 66 tok/s/user and GB300 NVL72 lands at $0.51 per million tokens against H100's $0.87 — GB300 NVL72 pulls ahead by 72%.
GB300 NVL72: $1.23 per million tokens. H100: $1.16. Both at 101 tok/s/user on Qwen 3.5 397B-A17B, with H100 6% cheaper.
Toward the upper edge of the 32–171 tok/s/user interactivity band — at 136 tok/s/user — GB300 NVL72 runs $2.19 per million tokens on Qwen 3.5 397B-A17B while H100 runs $1.55. H100 is the cheaper choice by 41%. (Numbers reflect the default 1k/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:$0.508H100:$0.873 | GB300 NVL72:$1.225H100:$1.159 | GB300 NVL72:$2.193H100:$1.553 |
| Concurrency | GB300 NVL72:~167H100:~26 | GB300 NVL72:~25H100:~12 | GB300 NVL72:~11H100:~7 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.