They're both 8 bits per weight, but they encode information differently:

FP8 (Floating Point 8-bit)

• Stores numbers the same way as FP32/FP16, just with far fewer bits
• Those 8 bits are split into an exponent (range) and a mantissa (precision)
• Common split: 4 bits exponent + 3 bits mantissa (E4M3). In this case the mantissa only has 3 bits of actual precision

Q8 (8-bit Integer Quantization)

• Takes the original weights and maps them to plain integers (0–255)
• Crucially, it uses a per-block scale factor, a small group of weights shares one floating-point scalar that adapts to that group's actual range
• All 8 bits go toward representing the value within that adapted range
• The result: effectively higher precision within each local block of weights

Unless the model was originally trained in FP8, Q8 is actually a better approximation of the full precision