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Why is fp8 training hard, and how did DeepSeek-V3 make it work?

Separate FP8 input quantization from accumulation error. A small representable-value example shows when a shared scale loses small values, while the DeepSeek-V3 report supplies one carefully scoped training recipe.

Updated Sep 2026 · Learn how AI infrastructure works through explanations, worked examples and diagrams, then practise applying it to interview questions.

Separate FP8 input quantization from accumulation error. A small representable-value example shows when a shared scale loses small values, while the DeepSeek-V3 report supplies one carefully scoped training recipe.

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Work through the numbers

Change the assumptions in a free calculator, then explain what the result establishes and what still needs measurement.

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FEDITOR'S NOTE

Derive the effect of scaling without treating floating point as a uniform integer grid. Attribute the DeepSeek-V3 recipe and results precisely, and keep numerical validation separate from hypothetical speedup arithmetic.

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