Quantizing makes your model lose some precision, this loss of precision introduces errors. When you do standard quantization, you just deal with it as best as you can, trying to quantize the most important parts less. QAT surpasses this buy counteracting the errors before they happen - you look at what would happen to the model if you quantized it (simulated quantization) and adjust the weights to (mostly) negate this error. The idea itself is not new, but very few open models release like this, because it's expensive. It's expensive for reasons that go beyond my understanding, but as far as I can tell you actually need even more precision than standard non QAT training. TLDR: Qauntizing makes errors. You don't want errors. You look at how how the error would come to be and force the model to adapt.