Overview
The rapid growth of AI applications demanding low latency, data privacy, and real-time responsiveness is driving a shift from centralized cloud computing toward edge-hosted AI. This transformation mirrors earlier trends in content delivery networks and is particularly relevant for telecom and cable operators, whose distributed infrastructure is well-positioned to support this evolution.
This paper explores the deployment of large language models (LLMs), vision-language models (VLMs), and other AI workloads at the network edge. By placing compute closer to users and data sources, operators can reduce latency, preserve data locality, meet regulatory requirements, and unlock new operational efficiencies and revenue streams.
