Architecting memory and storage in the AI era

AI inference workloads demand continuous, geographically distributed processing with strict latency, memory-bandwidth, storage-throughput, and networking requirements, turning data movement into the primary bottleneck. Organizations must redesign data pipelines and adopt purpose-built architectures that integrate memory and storage as core components, optimizing for performance per watt, cost, scalability, and environmental impact.