Issue 58, Sep 2, 2026
The FP8 Quantization Tax
Senior ML Systems Engineer interview at Meta, and the interviewer asks:
“You moved your matmuls to FP8 on H100s expecting a 2× speedup. You got 25%. Is FP8 overhyped, or did you do it wrong?”
Don’t say: “FP8 halves the bytes, so the tensor cores should be twice as fast. Maybe the kernel isn’t optimized.”
Why doubling Tensor Core throughput silently gets eaten by memory-bound casts and Amdahl's Law — and how elite teams split E4M3 and E5M2 across the backward pass.
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The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.