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 consumption, or the underlying highperformance computing (HPC) infrastructure. We present a life cycle assessment (LCA) of the pre-training of Lucie 7B, an open-source multilingual Foundation Model developed by the OpenLLM-France consortium and trained on the NVIDIA H100 partition of the Jean Zay supercomputer operated by IDRIS
The increasing scrutiny on the environmental impact of AI, particularly LLMs, has created a demand for comprehensive assessments that go beyond operational energy to include embodied emissions and water consumption.
This research provides a more holistic understanding of AI's environmental footprint, which is crucial for sustainable development of AI infrastructure and for informing policy and investment decisions.
The focus for evaluating AI's environmental impact shifts from solely operational energy to a full life cycle assessment, incorporating manufacturing and HPC infrastructure.
- · Environmental consulting firms
- · Sustainable AI developers
- · Researchers in LCA methodologies
- · LLM developers ignoring environmental impact
- · Data centers with high embodied emissions
- · HPC infrastructure with poor sustainability metrics
Increased focus on embodied emissions and water usage in AI infrastructure procurement and design.
Development of new metrics and standards for sustainable AI, influencing investment in 'green' AI technologies.
Potential for regulatory frameworks that mandate full life cycle assessments for large-scale AI training, impacting hardware choices and geographical placement of data centers.
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Read at arXiv cs.LG