GLM 5/5.1 — GB200 NVL72 vs GB300 NVL72 Performance per Dollar
Cost per million tokens of GB200 NVL72 (NVIDIA Blackwell) versus GB300 NVL72 (NVIDIA Blackwell) on GLM 5/5.1. 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.
On GLM 5/5.1 at 68 tok/s/user, the per-million math comes out to $0.10 for GB200 NVL72 and $0.07 for GB300 NVL72; GB300 NVL72 delivers 39% more output per dollar.
At 98 tok/s/user on GLM 5/5.1, GB200 NVL72 costs $0.35 per million tokens; GB300 NVL72 costs $0.13. GB300 NVL72 is 158% more cost-efficient at this operating point.
GB300 NVL72 edges GB200 NVL72 at 127 tok/s/user on GLM 5/5.1 — $0.60 per million tokens versus $0.63, a 6% cost-per-token gap. (Numbers reflect the default 8k/1k · fp4 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 · GB300 NVL72 $2.65/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:$0.101GB300 NVL72:$0.073 | GB200 NVL72:$0.346GB300 NVL72:$0.134 | GB200 NVL72:$0.635GB300 NVL72:$0.598 |
| Concurrency | GB200 NVL72:~725GB300 NVL72:~726 | GB200 NVL72:~84GB300 NVL72:~395 | GB200 NVL72:~49GB300 NVL72:~41 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.