English(EN) Carbon-Aware Routing for Function Calling in Edge-Cloud LLM Systems

新框架通过边缘-云路由将LLM碳排放量减少4倍

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一个新颖的碳感知路由框架，旨在优化具有函数调用能力的大型语言模型（LLM）的能源消耗。该系统将查询分布在三层边缘-云架构中，利用k-NN预测器估算每个查询的准确性、延迟和功耗。通过整合实时电网碳强度数据，该框架将查询路由到能够成功执行的最节能的层。评估表明，这种方法在保持云级准确性的同时，可以将运营碳排放量平均减少四倍。 AI

影响该路由框架可以显著减少AI系统的环境足迹，使LLM部署更具可持续性。

排序理由该集群包含一篇详细介绍LLM基础设施新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Aikaterini Maria Panteleaki, Varatheepan Paramanayakam, Spyros Tragoudas, Iraklis Anagnostopoulos · 2026-09-15 04:00

面向边缘-云LLM系统的碳感知函数调用路由

arXiv:2609.13559v1 Announce Type: new Abstract: Large Language Models (LLMs) with function-calling capabilities are becoming critical for modern agentic AI systems. Nevertheless, current deployments typically route inferences to powerful cloud-based models, incurring significant …

本文如何被排名

报道来源 [1]

面向边缘-云LLM系统的碳感知函数调用路由

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