AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices

This work investigates the time, token, and energy overhead of locally deployed LLM-based AI agents on consumer hardware. It reveals that while local agents address privacy and cost concerns, their iterative reasoning and tool use substantially increase resource consumption, leading to higher GPU power draw and battery drain.