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1.58 Bits vs FP8: What Ternary Weights Actually Buy, and What They Break

The headline is always the same: a model that runs on a laptop, at a fraction of the energy, with almost no quality loss. The number behind it is bits per weight, pushed from sixteen down to eight, four, and now below two. The direction is right. The mechanism, however, is routinely misdescribed, a…

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  1. 2026-11-02 01:40 · DEV Community — Machine Learning
    1.58 Bits vs FP8: What Ternary Weights Actually Buy, and What They Break

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