ACL 2025long0 citations
Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit View
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.
BibTeX
@inproceedings{wu-etal-2025-unveiling,
title = "Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit View",
author = "Wu, Yanran and
Hua, Inez and
Ding, Yi",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.519/",
doi = "10.18653/v1/2025.acl-long.519",
pages = "10560--10576",
ISBN = "979-8-89176-251-0"
}