Qwen 3.5 397B-A17B — H100 vs RTX PRO 6000 Performance per Dollar
Cost per million tokens of H100 (NVIDIA Hopper) versus RTX PRO 6000 (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.
Only H100 has cost data at 64 tok/s/user on Qwen 3.5 397B-A17B — $0.85 per million tokens. RTX PRO 6000 is unmeasured at this target.
H100 costs $1.15 per million tokens at 100 tok/s/user on Qwen 3.5 397B-A17B; we have no RTX PRO 6000 benchmark data at this exact target.
At 135 tok/s/user on Qwen 3.5 397B-A17B, H100 comes in at $1.54 per million tokens. RTX PRO 6000 hasn't been benchmarked at this operating point. (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): H100 $1.30/GPU/hr · RTX PRO 6000 $0.43/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 | H100:$0.848RTX PRO 6000:— | H100:$1.151RTX PRO 6000:— | H100:$1.536RTX PRO 6000:— |
| Concurrency | H100:~28RTX PRO 6000:— | H100:~13RTX PRO 6000:— | H100:~7RTX PRO 6000:— |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.