How AI is solving the memory crunch it created

Memory has replaced compute as a primary constraint for modern tech teams. A perfect storm of hardware architecture limitations, semiconductor supply chain uncertainty, and changing software licensing models has left enterprises confronting increasingly memory-constrained environments. All while high-bandwidth AI workloads overload the production chain’s ability to provide sufficient memory — and when your AI token bill is starting to cost more than your salary bill.

Over the past year, the cost of high-bandwidth memory (HBM) and dynamic random access memory (DRAM) has increased by an unprecedented 170%, with some virtualization subscriptions more than doubling in price.

All this adds up to a demand for enterprises to shift from the previous buy-all-you-can mindset to a data-driven optimization strategy.

Fortunately, AI isn’t only part of the problem. When AI is applied to memory economics in modern virtualization, it becomes a vital part of the solution. 

Bharath Ram, director of product management at Hewlett-Packard Enterprise (HPE), explains it to The New Stack this way: “There’s a component shortage. Today the prices have increased. So customers are looking at ways to save and optimize the existing footprint, so that they can run their workloads on whatever and not have to procure anything new.”

By switching focus from gobbling up every bit of memory your organization can grab to optimizing your workloads and their placement…