Modern GPUs can crunch numbers much faster than standard memory can feed them. If the data transfer rate is too slow, an incredibly expensive GPU sits idle, waiting for information. High Bandwidth Memory (HBM) solves this by stacking DRAM chips vertically and connecting them directly to the processor via a microscopic highway, delivering terabytes of data per second. Without HBM, scaling frontier AI models is physically and economically impossible.

Memory is the bottleneck in AI right now. As AI models have grown exponentially—moving from training to continuous, high-context inference and multi-step agentic reasoning—the constraint has shifted from processing data (compute) to moving data (memory bandwidth).

And supply is short. Producing HBM requires roughly three times the wafer capacity of conventional memory, and building and equipping new fabrication plants takes years. As a result, the “Big Three” memory makers are effectively sold out of advanced HBM supply through the rest of 2026 and into 2027.

It is not just memory that is hot either. Adjacent storage—flash and hard disk—is also in high demand as AI needs more data to be stored and produces even more. There is a data explosion underway.