提出标准化评估框架，量化大模型服务碳排放
Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit View
- 以功能单元为基准，统一比较不同模型配置的环境影响
- 发现模型规模、量化策略和硬件选择对碳排放有显著影响
- 适合关注绿色AI的开发者与决策者参考
大语言模型虽能力强大，但碳排放问题突出。现有研究缺乏统一基准来比较不同模型配置的碳排放。为此，本文提出功能单元（Functional Unit）作为标准化基准，构建首个基于功能单元的评估框架FUEL，用于衡量大模型服务的环境影响。通过三个案例研究，揭示了优化模型大小、量化策略与硬件选型在降低碳排放中的关键作用与权衡关系，为更可持续的大模型服务提供支持。代码已开源：https://github.com/jojacola/FUEL。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。