Optimizing Latency and Energy Efficiency in Edge-Native Large Language Models (LLMs) for Autonomous Mobile Agents

Abstract

Large language models (LLMs) are becoming more and more important for perception, reasoning, navigation, and human-machine interaction in autonomous mobile agents like delivery robots, drones, and intelligent cars. However, the rigorous real-time latency requirements, energy limitations, and restricted processing resources make it difficult to implement LLMs directly on edge devices. Therefore, this study introduces an edge-native framework for optimizing latency and energy efficiency in LLM-enabled autonomous mobile agents. To obtain effective on-device intelligence while preserving effective performance, the suggested method combines lightweight model architectures, adaptive inference scheduling, dynamic job offloading, and hardware-aware optimization techniques. While edge–cloud collaboration allows for the selective execution of computationally demanding activities, quantization, pruning, and knowledge distillation are used to lower model complexity and memory consumption. Furthermore, an energy-aware resource management technique constantly modifies processor workloads according to task urgency, network conditions, and battery levels. When compared to traditional cloud-dependent LLM deployments, experimental evaluations on representative mobile robotic platforms show notable improvements in inference latency and battery consumption. The findings show decreased communication overhead, increased operational continuity, and faster response times without significantly lowering language comprehension or decision-making precision.