
Trillion-dollar investments in artificial intelligence often stall because the most expensive components in the modern data center, the high-performance GPUs, are frequently left idling while waiting for training data to arrive across congested network links. This paradox creates a significant operational data problem where raw compute power significantly outpaces the ability of traditional storage architectures to deliver large-scale datasets at










