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Cleer Dashboard – How much energy⇄ does AI inference use?

The Sustainable AI Group released CLEER (Closed-model Latent Energy Estimation Range), a method and public dashboard that estimates per-token energy use for closed AI models by benchmarking open models on known hardware and matching proprietary models to the closest proxies based on observed performance. The dashboard converts those per-token estimates into representative chat and agentic sessions drawn from ShareChat conversations, Qwen serving traces, and AgentX coding-agent sessions, applying an identical token workload to every model so differences reflect energy intensity rather than verbosity. Its default scenario assumes behind-the-meter gas generation at 640 gCO₂e/kWh and a US-average data center PUE of 1.45, both toggleable, and results include accelerator and host-server energy, idle capacity, cooling and power distribution, and embodied emissions from hardware and data center construction.

read1 min views4 publishedOct 8, 2026

This dashboard applies CLEER, a new approach for estimating the environmental impact of closed AI models. It combines peer-reviewed research, large-scale empirical testing, and practical application to corporate emissions inventories.

CLEER (Closed-model Latent Energy Estimation Range) estimates per-token energy for models that cannot be measured directly. It benchmarks open models on known hardware, matches proprietary models to the closest proxies based on observed performance, and projects their energy use onto measured power curves.

          [Technical Report →](https://reports.sustainableaigroup.com/CLEER-Tech-Report)

          The dashboard translates per-token estimates into representative
          **chat** and **agentic** sessions derived from public production
          datasets. Every model receives the same token workload, so differences
          primarily reflect energy intensity rather than how verbose each model
          tends to be. Actual results will also depend on the number of tokens a
          model generates in practice.

Workload sources: ShareChat conversations, Qwen serving traces, and AgentX coding-agent sessions.

      Results include accelerator and host-server energy,
      idle capacity, cooling and power distribution, and
      embodied emissions from hardware and data center construction.
          **Scenario default:** behind-the-meter gas generation at
          **640 gCO₂e/kWh**, combined with US-average data center assumptions for PUE (1.45). Both values can be toggled in the dashboard.
          Learn more: [Emissions Calculation →](#)

model data…

    Documentation [CC BY 4.0](https://github.com/sustainableaigroup/CLEER-Tech-Report/blob/main/LICENSE) · Data [CC BY-NC 4.0](https://github.com/sustainableaigroup/CLEER/blob/main/data/LICENSE) · Code [Apache-2.0](https://github.com/sustainableaigroup/CLEER/blob/main/LICENSE)

    Figures may be reused with attribution for non-commercial purposes. Use of this dashboard is subject to our [Terms of Service](https://sustainableaigroup.com/cleer_tos).
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