Mistral published an 18-month life-cycle analysis of Mistral Large 2. The study measures greenhouse-gas emissions, energy use, and consumption of water and other materials across data-center construction, hardware manufacturing, training, and inference. In total, training Mistral Large 2 emitted 20,400 metric tons of greenhouse gases and used 281,000 cubic meters of water, while an average 400-token prompt-plus-reply produced 1.14 grams of emissions and used 45 milliliters of water. Learn more in The Batch: hubs.la/Q03GhWZz0
@DeepLearningAI Interesting. A broader lifecycle view is crucial for truly understanding AI's footprint. This transparency is welcome.
@DeepLearningAI this deep dive is essential. metrics like these not only enhance accountability but also set a standard for sustainable practices in ai development.
@DeepLearningAI The transparency in reporting is valuable. Do you think lifecycle analyses like this will become standard for all major model releases?
@DeepLearningAI heavy training now means creators breathe easier later
@DeepLearningAI Honestly impressed with the transparency.
@DeepLearningAI This kind of analysis is a game changer. Clear metrics can guide better practices moving forward.
@DeepLearningAI good to see deep dives into environmental impact. this kind of transparency is crucial for accountability in ai.
@DeepLearningAI sounds like a game changer for sustainable AI
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@DeepLearningAI Impressive transparency. It’s crucial for pushing sustainable AI practices.
@DeepLearningAI Mistral’s transparency on Mistral Large 2’s environmental impact is commendable. Quantifying emissions and water use across training and inference helps set benchmarks for responsible AI. Such data is vital for driving sustainability innovations while balancing performance.