提出可量化大模型推理碳排放的框架，助力绿色AI决策。
SEALing the Gap: A Reference Framework for LLM Inference Carbon Estimation via Multi-Benchmark Driven Embodiment
- 基于多基准测试构建推理阶段碳排放估算框架
- 首次实现按提示词粒度的碳排放精准测量
- 适合关注AI可持续性的开发者与研究者使用
大语言模型在软件工程中迅速普及，但其日益增长的碳足迹引发严重可持续性担忧。尽管训练阶段排放巨大，但由于处理的请求量庞大，推理阶段的碳排放很快超过训练阶段。这一转变凸显了在推理过程中进行精确、提示级碳排放测量的迫切需求，以支持可持续性导向的决策。为克服现有方法的局限，本文提出一种新型参考框架的设计原则，旨在指导未来工具开发，并为该领域的可持续性研究提供系统基础。我们还介绍了该原则的早期实现——SEAL，其采用多基准驱动的方法实现每条提示的碳排放估算。初步验证显示其结果具有潜力，使SEAL成为大模型生态系统中标准化可持续性评估的基石。
原文摘要 · Abstract (English)
Large Language Models are rapidly gaining traction in software engineering, yet their growing carbon footprint raises pressing sustainability concerns. While training emissions are substantial, inference quickly surpasses them due to the sheer volume of prompts processed. This shift underscores the urgent need for accurate, prompt-level carbon measurement during inference to enable informed, sustainability-focused decision-making. To address the limitations of existing approaches, in this paper, we outline the guiding principles for a novel reference framework for LLM inference carbon estimation that can guide the design of future tools and provide a systematic foundation for advancing sustainability research in this domain. We also introduce SEAL, an early embodiment of these principles that leverages a multi-benchmark-driven approach for per-prompt carbon estimation. Its initial validation shows promising results, positioning SEAL as a foundation for standardized sustainability assessment across the LLM ecosystem.
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。