Balancing Sustainability And Performance: The Role Of Small-Scale Llms In Agentic Artificial Intelligence Systems
This addresses sustainability challenges for developers of AI systems using large language models, though it appears incremental in nature.
This study investigated whether smaller-scale language models could reduce energy consumption in agentic AI systems without compromising performance, finding that smaller open-weights models can lower energy usage while preserving task quality.
As large language models become integral to agentic artificial intelligence systems, their energy demands during inference may pose significant sustainability challenges. This study investigates whether deploying smaller-scale language models can reduce energy consumption without compromising responsiveness and output quality in a multi-agent, real-world environments. We conduct a comparative analysis across language models of varying scales to quantify trade-offs between efficiency and performance. Results show that smaller open-weights models can lower energy usage while preserving task quality. Building on these findings, we propose practical guidelines for sustainable artificial intelligence design, including optimal batch size configuration and computation resource allocation. These insights offer actionable strategies for developing scalable, environmentally responsible artificial intelligence systems.