Organizations deploying GPU infrastructure for AI training and inference are facing a new set of networking challenges. Unlike traditional enterprise applications, AI workloads generate unique traffic patterns, including large-scale data ingestion before training, high-bandwidth model checkpoint transfers to distributed storage, continuous inference requests from thousands of users, and ongoing management traffic that crosses cluster boundaries.