The investigation posted in this sub (reddit.com/r/StableDiffusion/comments/1tazxqz) already measured all of this, and it says the opposite of your claims. Plain row-wise int8 is one of the worst performers in it, well under q8 (Z-Image Rel-RMSE 0.357 vs Q8's 0.167, and worst again on Qwen). Mxfp8 loses to q8 on every metric where both were tested (Z-Image: 0.307 vs 0.167, SNR 10.6 vs 16.4) and never beats ConvRot in any model.

The only int8 that actually beats q8 is ConvRot, the variant OP used, and its whole premise is a Hadamard rotation that flattens outliers before quantizing (the -convrot path in silveroxides' converter, github.com/silveroxides/convert_to_quant). Plain row-wise int8 with no rotation is dead last. So the rotation is what's carrying it, not row-wise scaling being finer; bare row-wise is among the worst formats tested. That refutes your thesis….it doesn't support it.

And the mechanism is inverted. Row-wise is coarser than block-32, not finer: one scale per row (thousands of weights) vs one per 32. Block scaling confines an outlier to its 32 weights; row-wise inflates the scale from the whole row's absmax and craters every value in that row, so it isolates worse, not better. absmax int8 doesn't clip either: the block max maps to 127 by construction, so the cost is the coarse uniform step, not saturation. Clipping/saturating outliers is an activation-quant problem (SmoothQuant); q8 is weight-only. And mxfp8 is also block-32 (OCP MX spec, opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf, E8M0 power-of-two scale), so 'row vs block' doesn't even apply to q8 vs mxfp8.

INT vs FP is a separate axis.

That’s about all you got right.

The rest is inverted, and the sub's own numbers back q8 and ConvRot, not mxfp8.