nvfp4

Both formats store the same eight 4-bit values per element. The difference is all in the scale, and the answer shows which of the two scale choices buys more accuracy and what it costs in bits.

QAT never makes a model faster than PTQ at the same format; both produce identical tensors for the kernel. The answer reframes what QAT buys, prices a QAT run in GPU hours against the fleet it could shrink, and names the cases where a serving team should not touch it.

Calibration data reaches each expert only through the router, so the experts that matter for rare domains see almost none of it. The answer counts the tokens, shows the point where a Hessian-based quantizer becomes worse than plain rounding, and builds a recipe that does not depend on luck.

Even a format with the same name differs between the two generations: block-scaled FP8 uses FP32 scales on Hopper and power-of-two scales on Blackwell. The answer designs formats, fallbacks and gates so a user routed to either pool gets a model the team has actually tested.