Qwen 3.5 397B-A17B — GB200 NVL72 vs H100 Performance per Dollar
Cost per million tokens of GB200 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.
GB200 NVL72: $0.51 per million tokens. H100: $0.85. Both at 64 tok/s/user on Qwen 3.5 397B-A17B, with GB200 NVL72 68% cheaper.
Around the middle of the 29–171 tok/s/user interactivity band — at 100 tok/s/user — GB200 NVL72 runs $1.27 per million tokens on Qwen 3.5 397B-A17B while H100 runs $1.15. H100 is the cheaper choice by 11%.
On Qwen 3.5 397B-A17B at 135 tok/s/user, the per-million math comes out to $2.84 for GB200 NVL72 and $1.54 for H100; H100 delivers 85% more output per dollar. (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): GB200 NVL72 $2.21/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 | GB200 NVL72:$0.506H100:$0.848 | GB200 NVL72:$1.272H100:$1.151 | GB200 NVL72:$2.838H100:$1.536 |
| Concurrency | GB200 NVL72:~113H100:~28 | GB200 NVL72:~20H100:~13 | GB200 NVL72:~7H100:~7 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.