Large language models (LLMs) offer significant potential for intelligent mobile services but are computationally intensive for resource-constrained devices. Mobile edge computing (MEC) allows such devices to offload inference tasks to edge servers (ESs), yet introduces latency due to communication and serverside queuing, especially in multi-user environments. In this work, we propose an uncertainty-aware offloading framework that dynamically decides whether to perform inference locally or offload it to the ES, based on token-level uncertainty and resource constraints. We define a margin-based token-level uncertainty metric and demonstrate its correlation with model accuracy. Leveraging this metric, we design a greedy offloading algorithm (GOA) that minimizes delay while maintaining accuracy by prioritizing offloading for highuncertainty queries. Our experiments show that GOA consistently achieves a favorable trade-off, outperforming baseline strategies in both accuracy and latency across varying user densities, and operates with practical computation time. These results establish GOA as a scalable and effective solution for LLM inference in MEC environments.

翻译：大型语言模型（LLM）为智能移动服务提供了巨大潜力，但其计算密集型特性对资源受限设备构成了挑战。移动边缘计算（MEC）允许此类设备将推理任务卸载至边缘服务器（ES），然而由于通信及服务器端排队（尤其在多用户环境中）会引入延迟。本文提出一种基于不确定性的卸载框架，该框架根据词元级不确定性与资源约束，动态决定在本地执行推理或将其卸载至ES。我们定义了一种基于间隔的词元级不确定性度量，并论证了其与模型精度的相关性。利用该度量，我们设计了一种贪心卸载算法（GOA），该算法通过优先卸载高不确定性查询，在保持精度的同时最小化延迟。实验表明，GOA在不同用户密度下均能持续实现有利的权衡，在精度与延迟方面均优于基线策略，且具有实际可行的计算时间。这些结果确立了GOA作为MEC环境中LLM推理的可扩展且有效的解决方案。