The AI infrastructure race has spent the past few years fixated on high-bandwidth memory as one of the most indispensable building blocks of accelerated compute. Yet as model sizes, context windows and inference workloads continue to expand, the bottleneck is increasingly shifting from how much memory can be stacked toward how efficiently data can move between memory and compute.