LATIDIA · Investigación

Enrutamiento consciente de carbono para llamadas de función en sistemas LLM Edge-Cloud

arXiv: 2609.13559v1Tipo de anuncio: nuevo Resumen: los modelos de lenguaje grandes (LLM) con capacidades de llamada a funciones se están volviendo críticos para los sistemas modernos de IA agéntica. Sin embargo, las implementaciones actuales generalmente enrutan

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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 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