This paper investigates compact large language model (LLM) deployment and world-model-assisted inference offloading in mobile edge computing (MEC) networks. We first propose an edge compact LLM deployment (ECLD) framework that jointly applies structured pruning, low-bit quantization, and knowledge distillation to construct edge-deployable LLM variants, and we evaluate these models using four complementary metrics: accessibility, energy consumption, hallucination rate, and generalization accuracy. Building on the resulting compact models, we formulate an MEC offloading optimization problem that minimizes the long-term average inference latency subject to per-device energy budgets and LLM-specific quality-of-service constraints on effective accuracy and hallucination. To solve this problem under unknown and time-varying network dynamics, we develop a world model-proximal policy optimization (PPO) algorithm, which augments an on-policy PPO algorithm with a learned recurrent world model that provides improved value targets and short imagination rollouts. Extensive experiments on Llama-3.1-8B, Qwen3-8B, and Mistral-12B show that ECLD compresses base models by about 70-80% in storage (i.e., from 15.3 GB to 3.3 GB for Llama-3.1-8B) and reduces per-query energy consumption by up to 50%, while largely preserving accuracy and often lowering hallucination compared with quantization-only or pruning-only baselines. Moreover, they also show that world model-PPO speeds up convergence by about 50%, improves the final reward by 15.8% over vanilla PPO, and reduces average inference latency by 12-30% across different user populations, while satisfying the accuracy and hallucination constraints and approaching the generation quality of always-offloading with much of the efficiency of local execution.

翻译：本文研究了移动边缘计算（MEC）网络中紧凑型大语言模型（LLM）的部署及基于世界模型的推理卸载问题。首先提出边缘紧凑型LLM部署（ECLD）框架，该框架联合应用结构化剪枝、低比特量化和知识蒸馏技术构建可部署于边缘的LLM变体，并通过四个互补指标评估这些模型：可访问性、能耗、幻觉率和泛化准确性。基于所得到的紧凑模型，我们构建了一个MEC卸载优化问题，在满足每设备能量预算以及有效准确率和幻觉等LLM特有的服务质量约束条件下，最小化长期平均推理延迟。为解决未知且时变网络动态下的该问题，我们开发了一种基于世界模型的近端策略优化（PPO）算法，该算法通过一个学习到的循环世界模型增强在线PPO算法，提供改进的价值目标与短时想象推演。在Llama-3.1-8B、Qwen3-8B和Mistral-12B上的大量实验表明，ECLD可将基础模型存储压缩约70–80%（例如Llama-3.1-8B从15.3 GB降至3.3 GB），并使单次查询能耗降低高达50%，同时与仅量化或仅剪枝的基线方法相比，在基本保持准确性的同时往往降低幻觉率。此外，实验还表明，世界模型-PPO算法比原始PPO算法收敛速度提升约50%，最终奖励提高15.8%，在不同用户群体中平均推理延迟降低12–30%，同时满足准确性和幻觉约束，并接近始终卸载方法的生成质量，兼具本地执行的高效率。