A severe memory and chip shortage called 'RAMageddon' is bottlenecking AI infrastructure deployment globally, with shortages expected to persist through 2027.
The technology industry is grappling with what insiders are calling "RAMageddon" — a severe shortage of memory chips and semiconductors that is creating critical bottlenecks for AI infrastructure deployment worldwide. The crisis is affecting companies from startups to tech giants.
Memory and chip suppliers are struggling to keep pace with the explosive demand driven by AI model training and inference workloads. High-bandwidth memory (HBM) used in AI accelerators is particularly scarce, with lead times extending to 12-18 months for some components.
The shortage is driving up costs across the AI ecosystem. Companies building AI data centers are finding that even with billions in capital, they cannot secure enough hardware to meet their deployment timelines. This has contributed to the urgency behind projects like SpaceX's Terafab.
Cloud providers are rationing [GPU](https://lambdalabs.com/service/gpu-cloud) access, and smaller AI companies report being unable to secure the compute resources needed to train competitive models. The bottleneck is shifting competitive dynamics in the AI industry, favoring companies with existing hardware commitments.
Analysts predict the shortage could persist through 2027, as new fabrication capacity takes years to come online. The situation is particularly acute for the latest-generation chips designed specifically for AI workloads, where demand has far outstripped even the most optimistic supply projections.
The crisis underscores a fundamental challenge for the AI industry: while software capabilities are advancing rapidly, the physical infrastructure needed to support those advances is constrained by the realities of semiconductor manufacturing.
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This article was originally published by CNET and has been enhanced and curated by AInewsnow AI.
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