{"data":{"event":{"id":"3d3361e8-8209-4a46-b3bd-f049dab18c71","slug":"carbon-aware-routing-for-function-calling-in-edge-cloud-llm-systems-6616620623","title":"Carbon-Aware Routing for Function Calling in Edge-Cloud LLM Systems","short_summary":"arXiv:2609.13559v1 Announce Type: new \nAbstract: 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 unifie","full_description":"arXiv:2609.13559v1 Announce Type: new \nAbstract: 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.\n\nUpdate (2026-09-16): OpenSpec – A lightweight and configurable AI spec framework. 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Nevertheless, current deployments typically route inferences to powerful cloud-based models, incurring significant energy use and carbon emissions. 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