AI Inference Demands a Holistic Rethink of Memory and Storage Infrastructure

As AI inference becomes the dominant enterprise workload, organizations must move beyond optimizing compute alone and instead coordinate memory, storage, and networking to meet latency and efficiency demands. The shift requires purpose-built architectures that balance performance per watt and scalability, as legacy systems create bottlenecks that limit AI's real-world impact. Decision-makers are urged to prioritize infrastructure that removes memory and storage constraints before they hinder growth and operational costs.