Edge computing significantly enhances AI inference by reducing latency and cost while improving user experience, particularly in tokenization and Retrieval-Augmented Generation (RAG) processes. Traditionally, AI systems are centralized in distant data centers, leading to increased network latency and server strain when handling numerous simultaneous requests. By distributing computation to edge nodes—numerous points of presence globally operated by CDNs and ISPs—AI workloads can run closer to end users, mitigating these issues. For instance, offloading tokenization to the edge can decrease latency by approximately 20 milliseconds and reduce payload size by about 35%, while RAG's integration at the edge shows substantial latency improvements, especially for users distant from centralized servers. This strategic shift not only relieves main servers but also allows additional checks and optimizations, promising a more efficient and responsive AI system, with ongoing research exploring further use cases and enhancements.