3 posts tagged “environment”
2025
Our contribution to a global environmental standard for AI (via) Mistral have released environmental impact numbers for their largest model, Mistral Large 2, in more detail than I have seen from any of the other large AI labs.
The methodology sounds robust:
[...] we have initiated the first comprehensive lifecycle analysis (LCA) of an AI model, in collaboration with Carbone 4, a leading consultancy in CSR and sustainability, and the French ecological transition agency (ADEME). To ensure robustness, this study was also peer-reviewed by Resilio and Hubblo, two consultancies specializing in environmental audits in the digital industry.
Their headline numbers:
- the environmental footprint of training Mistral Large 2: as of January 2025, and after 18 months of usage, Large 2 generated the following impacts:
- 20,4 ktCO₂e,
- 281 000 m3 of water consumed,
- and 660 kg Sb eq (standard unit for resource depletion).
- the marginal impacts of inference, more precisely the use of our AI assistant Le Chat for a 400-token response - excluding users' terminals:
- 1.14 gCO₂e,
- 45 mL of water,
- and 0.16 mg of Sb eq.
They also published this breakdown of how the energy, water and resources were shared between different parts of the process:
It's a little frustrating that "Model training & inference" are bundled in the same number (85.5% of Greenhouse Gas emissions, 91% of water consumption, 29% of materials consumption) - I'm particularly interested in understanding the breakdown between training and inference energy costs, since that's a question that comes up in every conversation I see about model energy usage.
I'd really like to see these numbers presented in context - what does 20,4 ktCO₂e actually mean? I'm not environmentally sophisticated enough to attempt an estimate myself - I tried running it through o3 (at an unknown cost in terms of CO₂ for that query) which estimated ~100 London to New York flights with 350 passengers or around 5,100 US households for a year but I have little confidence in the credibility of those numbers.
2019
People are suffering. People are dying. Entire ecosystem are collapsing. We are in the beginning of a mass extinction, and all you can talk about is money and fairy tales of eternal economic growth. How dare you?
2009
Google’s Chiller-less Data Center. Google are operating an outside data center in Belgium with no chillers (refrigeration units used to cool water, but at a high cost in energy) making “local weather forecasting a larger factor in its data center management”. On the 10 or so days of the year when Belgium is too warm, they can simply shut down the data center and shift the workload elsewhere.