Qumulo and Cisco target AI data bottlenecks with distributed storage access

StorageReview reported that Qumulo's Cloud AI Accelerator is designed to improve enterprise AI infrastructure efficiency by reducing data-movement bottlenecks. The compact payload says the platform can present distributed datasets to GPU resources across regions, clouds, and hybrid environments in real time without replication or staging. For RamTrend, the story is mostly about storage architecture rather than DRAM pricing. It highlights the pressure AI workloads place on data placement and high-performance storage systems, where enterprises often co-locate flash storage with GPU clusters. If architectures like this gain traction, they could shift some demand from duplicated local storage toward data-fabric and shared-storage deployments, but the payload does not provide a direct NAND or DRAM supply signal.