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Lucie 7B LLM training environmental impact assessed

By PulseAugur Editorial · [1 sources] ·

A new life cycle assessment (LCA) details the environmental impact of training the Lucie 7B open-source large language model on the Jean Zay supercomputer. The study, which includes manufacturing emissions, operational energy, water consumption, and hardware infrastructure, reports a total training footprint of 21 tCO2eq for Lucie 7B. The Jean Zay H100 partition has an annual footprint of 417.5 tCO2eq, with an effective intensity of 36.7 gCO2eq per GPU-hour. The research also highlights water consumption and waste-heat recovery efforts, contributing to the understanding of frugal AI systems. AI

IMPACT Provides a detailed environmental footprint for LLM training, informing the development of more sustainable AI infrastructure.

RANK_REASON Academic paper detailing a life cycle assessment of LLM training. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Lucie 7B LLM training environmental impact assessed

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Academic paper detailing a life cycle assessment of LLM training. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Marc L\'eobet, Pierre-Fran\c{c}ois Lavall\'ee, Jean-Pierre Lorr\'e ·

    Life Cycle Assessment of Pre-training the Lucie 7B Open-Source Large Language Model on the Jean Zay Supercomputer

    arXiv:2607.05408v1 Announce Type: cross Abstract: The environmental impact of training large language models (LLMs) is increasingly scrutinised, yet most published estimates focus on operational energy and disclose little about manufacturing (embodied) emissions, water consumptio…

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