gpt-oss 120B — GB200 NVL72 vs H200 Performance per Dollar
Cost per million tokens of GB200 NVL72 (NVIDIA Blackwell) versus H200 (NVIDIA Hopper) on gpt-oss 120B. 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.
At 101 tok/s/user on gpt-oss 120B, GB200 NVL72 costs $0.02 per million tokens; H200 costs $0.10. GB200 NVL72 is 506% more cost-efficient at this operating point.
GB200 NVL72 edges H200 at 157 tok/s/user on gpt-oss 120B — $0.03 per million tokens versus $0.19, a 555% cost-per-token gap.
Push gpt-oss 120B to 214 tok/s/user and GB200 NVL72 lands at $0.06 per million tokens against H200's $0.41 — GB200 NVL72 pulls ahead by 597%. (Numbers reflect the default 1k/1k · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
Chip pricing (owning hyperscaler): GB200 NVL72 $1.86/chip/hr · H200 $1.22/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 | GB200 NVL72:$0.017H200:$0.102 | GB200 NVL72:$0.030H200:$0.193 | GB200 NVL72:$0.058H200:$0.406 |
| Concurrency | GB200 NVL72:~512H200:~64 | GB200 NVL72:~3055H200:~53 | GB200 NVL72:~105H200:~8 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.