SiteFootprint: location-resolved carbon and water footprints of AI training
SiteFootprint estimates the operational carbon emissions and on-site water use of training an AI model at any location. It computes data-centre cooling hour by hour from real weather, combines it with annual grid carbon intensity, and reports results with 95% uncertainty ranges and paired comparisons between locations. The cooling model was calibrated on published Google campus PUE and tested on unseen campuses (mean absolute error 0.010), and the energy model reproduces Meta's disclosed Llama 3.1 405B training hours to within 5%. Available as a Python package, a command-line tool and a web app, with citable exports.
Resource information
- Format
- zip
- Original repository
- 22983788
- DOI
- 10.5281/zenodo.22983788
- Credit basis
- Creators in Zenodo record metadata
- Access
- Open files available from the original Zenodo deposit
- Last checked
- Nov 5, 2026
- License
- mit-license
- Last record update
- Nov 5, 2026
About this resource
SiteFootprint estimates the operational carbon emissions and on-site water use of training an AI model at any location. It computes data-centre cooling hour by hour from real weather, combines it with annual grid carbon intensity, and reports results with 95% uncertainty ranges and paired comparisons between locations. The cooling model was calibrated on published Google campus PUE and tested on unseen campuses (mean absolute error 0.010), and the energy model reproduces Meta's disclosed Llama 3.1 405B training hours to within 5%. Available as a Python package, a command-line tool and a web app, with citable exports.