According to DeepLearning.AI, Mistral has released an 18-month life-cycle analysis of its Mistral Large 2 AI model, providing detailed metrics on greenhouse-gas emissions, energy consumption, water usage, and material consumption. The report covers the full spectrum of AI deployment, including data center construction, hardware manufacturing, model training, and inference stages. This comprehensive assessment enables businesses to benchmark and optimize the environmental footprint of large language models, highlighting the need for sustainable AI practices and green data infrastructure (source: DeepLearning.AI, September 1, 2025).

According to DeepLearning.AI, researchers evaluated the energy consumption and resulting greenhouse gas emissions of 14 open-weights language models by having each model answer 100 questions across five subjects in the MMLU (Massive Multitask Language Understanding) benchmark and generate extended, open-ended responses. The study provides concrete data for AI developers and enterprise users to assess the environmental impact of deploying large language models, highlighting the need for greener AI solutions and optimization strategies in high-volume AI applications (source: DeepLearning.AI, August 13, 2025).