SanDisk’s HBF Halves GPU Requirements for AI Inference by Outperforming HBM Capacity
SanDisk is advancing High Bandwidth Flash (HBF) memory, which stacks NAND flash silicon dies, as a high-capacity, lower-cost, and lower-power alternative to High Bandwidth Memory (HBM) for AI systems.
AsiaAI Publisher
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September 10, 2026 ·
2 min read · Source: PC Watch (Impress) · Issue #92
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This story ran in Issue #92, alongside three other stories.
Semiconductors & Hardware
SanDisk is advancing High Bandwidth Flash (HBF) memory, which stacks NAND flash silicon dies, as a high-capacity, lower-cost, and lower-power alternative to High Bandwidth Memory (HBM) for AI systems. At recent industry events, SanDisk demonstrated that HBF-equipped systems can achieve comparable AI inference performance to HBM systems using half the number of GPUs, citing its 10-100x higher capacity per die.
The demand for massive, high-speed memory in AI inference systems, particularly for large language models (LLMs), is pushing HBM to its limits in terms of capacity. HBF, leveraging the higher density of NAND flash, offers a potential solution for overcoming this capacity bottleneck and reducing the hardware footprint of AI infrastructure.
