This research introduces a framework for analyzing and optimizing carbon emissions in wireless large language model services, addressing a critical environmental challenge.

• Proposes a comprehensive model for measuring carbon impact across both computation and network infrastructure
• Develops novel optimization algorithms that reduce carbon footprint while maintaining quality of service
• Demonstrates significant potential for carbon reduction in real-world LLM deployments

For engineering teams, this work provides actionable strategies to balance performance requirements with environmental sustainability goals when deploying LLM services at scale.

AOLO: Analysis and Optimization For Low-Carbon Oriented Wireless Large Language Model Services