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Our Contribution to a Global Environmental Standard for AI★ 72
Mistral AI News6 days agoRegulationMistral AI reports lifecycle impacts for LLM training and inference across greenhouse gas emissions, water use, and resource depletion. It discloses figures for Mistral Large 2 after training and 18 months of use, plus marginal impacts for a 400-token Le Chat response. The company argues AI vendors should use standardized, internationally recognized reporting so buyers and policymakers can compare models more responsibly.
Open LLM Leaderboard 碳排放與模型性能分析：效能與環保的權衡啟示
Hugging Face Blog521 days agoCommentaryHugging Face recently published an in-depth analysis of its well-known Open LLM Leaderboard, examining the carbon dioxide (CO₂) emissions generated during…
機器學習專家訪談：Sasha Luccioni 談 AI 的碳足跡與永續發展
Hugging Face Blog1,489 days agoOpinionThis interview profiles Sasha Luccioni, a research scientist at Hugging Face whose work centers on measuring and reducing the environmental impact of…