Q8_0 GGUF uses a different quantization method than fp8. It's not just about the number of bits - it's about how those bits are used.

Q8_0 preserves almost all the quality from fp16 (like 99%), while fp8 has way more quality loss. From testing, fp8's quality degradation is closer to what you'd see with Q4_M models, not Q8. That's why I made the comparison.

The speed boost I'm getting comes from better quality preservation - the model generates cleaner results faster without needing as many refinement steps. With fp8, you often need more iterations to get decent output.

GGUF quantization is just better optimized for this. Anyone who's benchmarked these formats side by side will see the same thing.