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

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 energy use and carbon emissions. We address this sustainability challenge with a carbon-aware routing framework that distributes function-calling queries across a three-tier edge-cloud architecture, combining edge and cloud LLMs on heterogeneous hardware. At its core, a lightweight k-NN predictor operating in a unified semantic-lexical embedding space estimates query-specific accuracy, delay, and power consumption on each edge tier. These predictions are then combined with real-time grid carbon intensity to route every query to the lowest-emission tier capable of executing it successfully. Evaluated on state-of-the-art function-calling benchmarks and LLM families, our framework matches cloud-level accuracy while reducing operational carbon emissions by $4\times$ on average.

Artificial Intelligence (cs.AI)

arXiv:2609.13559 [cs.AI]

(or arXiv:2609.13559v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.13559 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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