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High Bandwidth Flash offers capacity but limited speed for AI workloads

High Bandwidth Flash (HBF), a new memory format presented at Hot Chips 2026, offers significantly more capacity than traditional High Bandwidth Memory (HBM) at a comparable cost. However, HBF's lower bandwidth makes it unsuitable for many AI workloads where speed is critical. OXMIQ Labs demonstrated that while HBF can drastically reduce the number of GPUs needed to store large models like Kimi-K2, HBM remains superior for maximizing inference throughput and delivering a lower cost per token when bandwidth is the limiting factor. The consensus suggests HBF may serve as a specialized memory tier for large, less frequently accessed datasets rather than a direct replacement for HBM. AI

IMPACT High Bandwidth Flash offers a potential solution for accommodating extremely large AI models, but its performance limitations may restrict its adoption for high-throughput inference tasks.

RANK_REASON New memory technology presented at a research conference. [lever_c_demoted from research: ic=1 ai=0.7]

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High Bandwidth Flash offers capacity but limited speed for AI workloads

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New memory technology presented at a research conference. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. Tom's Hardware TIER_1 English(EN) · Anton Shilov ·

    Hot Chips 2026: High Bandwidth Flash promises massive bandwidth and capacity, but its usability is extremely limited — new memory format strikes a balance between HBM and NAND flash

    OXMIQ presented its High Bandwidth Flash (HBF) use-case scenarios at Hot Chips 2026, which dramatically narrow the circumstances under which HBF makes sense.