GLM 5/5.1 — GB300 NVL72 vs H200 Performance per Dollar
Cost per million tokens of GB300 NVL72 (NVIDIA Blackwell) versus H200 (NVIDIA Hopper) 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 33 tok/s/user, the per-million math comes out to $0.28 for GB300 NVL72 and $0.99 for H200; GB300 NVL72 delivers 257% more output per dollar.
At 44 tok/s/user on GLM 5/5.1, GB300 NVL72 costs $2.88 per million tokens; H200 costs $1.28. H200 is 126% more cost-efficient at this operating point.
H200 edges GB300 NVL72 at 56 tok/s/user on GLM 5/5.1 — $1.65 per million tokens versus $8.90, a 441% cost-per-token gap. (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 · H200 $1.41/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:$0.277H200:$0.990 | GB300 NVL72:$2.880H200:$1.277 | GB300 NVL72:$8.903H200:$1.646 |
| Concurrency | GB300 NVL72:~4993H200:~64 | GB300 NVL72:~229H200:~28 | GB300 NVL72:~61H200:~18 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.