Reconciling the contrasting narratives on the environmental impact of large language models

The recent proliferation of large language models (LLMs) has led to divergent narratives about their environmental impacts. Some studies highlight the substantial carbon footprint of training and using LLMs, while others argue that LLMs can lead to more sustainable alternatives to current practices....

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Bibliographic Details
Published in:Scientific reports 2024-11, Vol.14 (1), p.26310-8, Article 26310
Main Authors: Ren, Shaolei, Tomlinson, Bill, Black, Rebecca W., Torrance, Andrew W.
Format: Article
Language:English
Subjects:
704/844/4066/4069
704/844/685
Artificial intelligence
Carbon
Carbon Footprint
Economic cost
Economic factors
Energy consumption
Environment
Environmental assessment
Environmental impact
Human work
Humanities and Social Sciences
Humans
Language
Large language model
Large language models
Models, Theoretical
multidisciplinary
Narration
Science
Science (multidisciplinary)
United States
Water use
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Summary:The recent proliferation of large language models (LLMs) has led to divergent narratives about their environmental impacts. Some studies highlight the substantial carbon footprint of training and using LLMs, while others argue that LLMs can lead to more sustainable alternatives to current practices. We reconcile these narratives by presenting a comparative assessment of the environmental impact of LLMs vs. human labor, examining their relative efficiency across energy consumption, carbon emissions, water usage, and cost. Our findings reveal that, while LLMs have substantial environmental impacts, their relative impacts can be dramatically lower than human labor in the U.S. for the same output, with human-to-LLM ratios ranging from 40 to 150 for a typical LLM (Llama-3-70B) and from 1200 to 4400 for a lightweight LLM (Gemma-2B-it). While the human-to-LLM ratios are smaller with regard to human labor in India, these ratios are still between 3.4 and 16 for a typical LLM and between 130 and 1100 for a lightweight LLM. Despite the potential benefit of switching from humans to LLMs, economic factors may cause widespread adoption to lead to a new combination of human and LLM-driven work, rather than a simple substitution. Moreover, the growing size of LLMs may substantially increase their energy consumption and lower the human-to-LLM ratios, highlighting the need for further research to ensure the sustainability and efficiency of LLMs.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-76682-6