A data science team runs iterative training experiments on an on-premises DGX system. Each experiment repeatedly reads the same multi-terabyte dataset, and they complain that GPU utilization drops during the initial epochs while data loads. Their entire dataset currently sits on a network-attached object store connected over standard Ethernet. Which storage change would most directly improve GPU utilization for these repeated-read workloads?