AI Workloads Are Turning The Data Center Network Into A Combined Memory And Storage Fabric

AI inference workloads are transforming data center architecture by integrating the network into a combined memory and storage fabric. This shift is driven by the increasing dominance of inference workloads over traditional microservices and client-server interactions. The classic data center design is evolving to accommodate the structured, server-server communication patterns of AI training and the sustained memory and storage traffic of inference workloads. As AI inference becomes the primary workload, network performance will be crucial for efficient access to distributed memory and storage resources. The data center network is no longer just a communication layer but a critical component defining AI performance.