I recently did some toying around with (very) small language models (<200k). I used QAT from the beginning and was surprised that I got byte identical results from the int8 model vs the float32 one.

If that scales with parameters I don't see why QAT shouldn't be the norm.

(As I was only comparing the QAT f32 model vs the quantization its possible that a not QAT f32 would have performed even better)

Do you think QAT isn't the norm yet?

I expected it to be the norm since GPT-OSS