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        <title>Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit View</title>
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        <namePart type="given">Yanran</namePart>
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            <title>Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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            <namePart type="given">Wanxiang</namePart>
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            <namePart type="given">Mohammad</namePart>
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    <abstract>Large language models (LLMs) offer powerful capabilities but come with significant environmental impact, particularly in carbon emissions. Existing studies benchmark carbon emissions but lack a standardized basis for comparison across different model configurations. To address this, we introduce the concept of functional unit (FU) as a standardized basis and develop FUEL, the first FU-based framework for evaluating LLM serving’s environmental impact. Through three case studies, we uncover key insights and trade-offs in reducing carbon emissions by optimizing model size, quantization strategy, and hardware choice, paving the way for more sustainable LLM serving. The code is available at https://github.com/jojacola/FUEL.</abstract>
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%0 Conference Proceedings
%T Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit View
%A Wu, Yanran
%A Hua, Inez
%A Ding, Yi
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F wu-etal-2025-unveiling
%X Large language models (LLMs) offer powerful capabilities but come with significant environmental impact, particularly in carbon emissions. Existing studies benchmark carbon emissions but lack a standardized basis for comparison across different model configurations. To address this, we introduce the concept of functional unit (FU) as a standardized basis and develop FUEL, the first FU-based framework for evaluating LLM serving’s environmental impact. Through three case studies, we uncover key insights and trade-offs in reducing carbon emissions by optimizing model size, quantization strategy, and hardware choice, paving the way for more sustainable LLM serving. The code is available at https://github.com/jojacola/FUEL.
%R 10.18653/v1/2025.acl-long.519
%U https://aclanthology.org/2025.acl-long.519/
%U https://doi.org/10.18653/v1/2025.acl-long.519
%P 10560-10576

Markdown (Informal)

[Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit View](https://aclanthology.org/2025.acl-long.519/) (Wu et al., ACL 2025)

• Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit View (Wu et al., ACL 2025)

ACL

• Yanran Wu, Inez Hua, and Yi Ding. 2025. Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit View. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10560–10576, Vienna, Austria. Association for Computational Linguistics.