Large Language Models (LLMs) enable real-time function calling in edge AI systems but introduce significant computational overhead, leading to high power consumption and carbon emissions. Existing methods optimize for performance while neglecting sustainability, making them inefficient for energy-constrained environments. We introduce CarbonCall, a sustainability-aware function-calling framework that integrates dynamic tool selection, carbon-aware execution, and quantized LLM adaptation. CarbonCall adjusts power thresholds based on real-time carbon intensity forecasts and switches between model variants to sustain high tokens-per-second throughput under power constraints. Experiments on an NVIDIA Jetson AGX Orin show that CarbonCall reduces carbon emissions by up to 52%, power consumption by 30%, and execution time by 30%, while maintaining high efficiency.

本研究针对大型语言模型在边缘AI系统中的高能耗和碳排放问题，提出了CarbonCall，一个可持续性意识的函数调用框架。该框架通过实时碳强度预测动态调整功率阈值，并切换模型变体，以减少碳排放和能耗，同时保持高效能。实验结果表明，CarbonCall能将碳排放减少52%、功耗降低30%和执行时间缩短30%。

CarbonCall：面向边缘设备的大型语言模型的可持续性函数调用