AI workloads challenge traditional storage, requiring high-performance systems to prevent GPU idling and stalled training cycles. Legacy systems are inadequate, leading to reliability issues and costly inefficiencies. Modern architectures must incorporate fault tolerance, resilience, and efficient data management to meet AI demands and ensure ROI.
