Sustainability in Multi-Agent LLM System
Pawan Kumar Goel, Satya Prakash Yadav, Prashant Upadhyay · Advances in computational intelligence and robotics book series · 2025
The rapid growth of artificial intelligence (AI) has led to significant energy consumption and environmental impact in multi-agent large language model (LLM) systems. To ensure the long-term viability of AI advancements while minimizing their carbon footprint, research is focused on optimizing energy efficiency in these systems. This chapter proposes a novel framework that leverages adaptive agent collaboration and energy-aware scheduling algorithms to reduce energy usage without compromising system performance. The approach introduces a dynamic load-balancing mechanism that distributes tasks among agents based on real-time energy availability and computational demand. Experimental results show a 30-40% reduction in energy consumption compared to traditional systems, while maintaining comparable accuracy and response times. This breakthrough represents a significant advancement in sustainable AI.