Memory has become the limiting factor in AI and IT systems, according to an analysis by The Next Platform.
The shift reflects growing demands of machine learning, generative AI, and agentic AI, which require large amounts of memory and storage. Memory and storage were previously secondary considerations in high-end computing but have become the critical bottleneck.
Gartner data cited in the report shows memory revenues nearly doubled between 2025 and 2026, though growth is expected to decelerate between 2026 and 2027. Demand is driven by DRAM main memory, HBM stacked memory, and flash storage essential for AI workloads.
Prices for memory have surged since the GenAI boom began in 2022. DDR4 server memory has become two to three times more expensive, DDR5 server memory increased by nine to 13 times, and enterprise SSDs have seen a six to seven times price hike. Nearline disk drives cost about 2.5 times more over the same period.
HBM memory, produced by Samsung, SK Hynix, and Micron Technology, has seen modest price increases. The report said HBM4 is rumored to cost around $16 per GB, with end-user costs significantly higher. This figure is flagged as speculative and unconfirmed in the source.
Nvidia has developed NVHBM technology that integrates the memory controller into the HBM stack, delivering up to 30% greater memory bandwidth and 15% lower power consumption, according to the report. This frees up to 25% more area on the compute die. These figures are Nvidia's own claims and have not been independently verified.
The IT and AI industries are in a massive upgrade cycle, competing for memory resources as traditional IT sectors compete with tech companies for flash and DRAM memory.
The analysis by The Next Platform attributes the shift to the rapid evolution of AI technologies, which have made memory a critical component in system design and performance.