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Convequity on X

Inference repeatedly reads the same model weights, with far fewer writes than training. 1. In training, weights are constantly updated, so the memory sees heavy read and write traffic. 2. In inference, the dominant pattern is repeated reading of the same weights, and the key-value cache alongside them, with far fewer writes. That is the case for putting AI model weights on flash. Flash has traditionally been weaker than DRAM on write endurance and write speed. A read-heavy, write-light workload plays to its strengths and gets around exactly those weaknesses. Today AI chips sit beside HBM built from stacked DRAM, and it is the expensive part of the memory bill. High-Bandwidth Flash (HBF) is the product built on the read-heavy observation. It aims to deliver HBM-like read bandwidth at far higher capacity and lower cost by using NAND flash instead of DRAM. Two caveats. These are aims, not measured specifications. As of our early-August note, the first prototype from Sandisk ($SNDK), developed with SK Hynix, was expected later in the year. HBF is also more specialised than commodity NAND, where Chinese producers are expanding capacity aggressively.

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British American Tobacco: Harvest

British American Tobacco’s U.S. reset revives combustibles and rebuilds its nicotine-pouch portfolio after legacy-brand writedowns and Velo…

BTI $120B
Invariant 5 clicks

Practical Parametric Yield

Cerebras’s WSE-3 faces parametric-yield constraints that could restrict supply, making power and clock-speed tradeoffs central to its next…

CBRS $39B
Irrational Analysis 3 clicks

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