Spatiotemporal Resource Orchestration for LLM Inference in Vehicular-Edge Networks

Abstract

Large Language Models (LLMs) have been increasingly applied to intelligent vehicular systems for tasks such as scene understanding, intent reasoning, and natural language interaction. However, their inference demands exceed onboard processing capabilities, making low-latency on-vehicle inference impractical. Although edge computing can partially offload computation, the prolonged nature of LLM inference often causes execution to exceed the residence time of vehicles within edge coverage areas, leading to frequent service interruption. To address these challenges, we propose a collaborative spatiotemporal resource orchestration architecture for LLM inference in vehicular-edge networks (CoInfer). CoInfer exploits the intrinsic decomposability of LLM inference by modeling each request as a Directed Acyclic Graph (DAG) of interdependent subtasks, which are then scheduled, migrated, and aggregated along the road network to preserve end-to-end inference continuity. To improve latency and resource efficiency, CoInfer integrates multi-agent reinforcement learning for coarse-grained task orchestration with a reactive scheduler for fine-grained resource adaptation, forming a closed-loop service optimization under dynamic resource conditions. The simulation results demonstrate that CoInfer achieves a task success ratio of up to 96.0% and reduces the end-to-end inference latency by 35.7% compared to representative baselines.