Abstract: With the growing scale of artificial intelligence (AI) models and the increasing heterogeneity of processor architectures, the AI inference infrastructure faces challenges such as limited cross-platform portability, low hardware utilization, and highly dynamic runtime behavior. This article systematically analyzes its optimization paths, including unified abstraction, multi-layer fusion, adaptive mechanisms, and deployment scenario differentiation, and explores the key technical directions and future opportunities.