AI workloads are driving massive growth in data center capacity needs, projected to nearly triple by 2030 with 70 percent of demand attributed to AI. Traditional storage systems, built for sequential and predictable workloads, are ill-equipped to handle the concurrent, random data access patterns required by modern AI training and inference processes. This mismatch results in underutilized compute resources and slowed progress on critical AI projects.
High-performance computing environments like life sciences and government sectors offer lessons in managing large, unstructured datasets with high availability and resilience requirements. Hybrid storage solutions combining high-performance tiers with cost-effective lower tiers enable better alignment with AI workload demands. Data durability remains a major challenge, with many AI projects failing or stalling due to poor data quality and unreliable storage infrastructures.
Operational and technical measures such as multi-level erasure coding, hybrid flash-and-disk configurations, modular architectures, automated integrity checks, and recovery drills are essential to maintain data integrity and ensure continuous AI development. Without addressing these storage and data management challenges, enterprises risk wasting significant investments and missing AI innovation targets.
👉 Pročitaj original: CIO Magazine
