HBF Could Ease AI’s Memory Wall: What It Means for SNDK

Posted on by Eben@CANSLIM Research

In a report examining whether high-bandwidth flash can solve the AI memory bottleneck, the analysis argues that AI’s shift from training to advanced inference and agentic workloads is moving infrastructure from compute-centric to memory-centric designs. Larger models and expanding context push data beyond HBM into the wider memory and storage hierarchy, while caching creates an economic incentive to retain rather than recompute data. The result, the report says, is broadening demand across conventional DRAM, NAND and HDD, not just GPUs and HBM.

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