🌱 Major Methodology Update for EcoLogits: More Accurate, Still Transparent, and More Impactful!
At GenAI Impact, we’re committed to advancing the field of Green AI by providing open-source tools and methodologies to assess the environmental footprint of generative AI. Today, we’re thrilled to announce a major update to the methodology behind #EcoLogits, our suite of tools for estimating the environmental impacts of LLMs at inference.
What’s new?
✅ New energy benchmark: We’ve adopted the ML.ENERGY Leaderboard for more accurate energy consumption estimates, reflecting real-world AI deployments.
✅ Water consumption footprint: We’ve added a new impact criterion to account for water usage in data centers and electricity generation, based on the latest research “Making AI Less ‘Thirsty’” from Li et al., 2025.
✅ Provider-specific configurations: We now include detailed cloud region and data center metrics (PUE, WUE) from major AI providers, improving the accuracy of our estimates.
✅ Alignment with recent studies: We compared our methodology with new disclosures from Google, Mistral AI, and OpenAI, confirming that our updated approach aligns closely with their reported impacts.
Why does this matter?
As AI adoption grows, so does its environmental footprint. Our goal is to provide transparent, open-source tools that help organizations and researchers estimate and reduce the impacts of generative AI.
📊 Want to see how we compare up against the latest AI provider disclosures? Check out our full blog post: https://lnkd.in/e3zN7uFE
💡 Interested in contributing? We’re calling for open collaborations from research labs and organizations to further improve our methodology. Reach out if you’d like to get involved!
#GreenAI #SustainableAI