Carbon-Aware Routing for Function Calling in Edge-Cloud LLM Systems

The paper presents a carbon‑aware routing framework for function‑calling in large language models that distributes queries across a three‑tier edge‑cloud architecture. A lightweight k‑NN predictor estimates accuracy, delay, and power for each edge tier, and real‑time grid carbon intensity is used to route queries to the lowest‑emission tier that can execute them. Experiments on state‑of‑the‑art benchmarks show the framework matches cloud‑level accuracy while cutting operational carbon emissions by an average of four times.

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• llms
• rag
• agents
• benchmarks