A technique that introduces additional steps during training that prepare your model to be deployed in 8-bit. If deployment in 8-bit is not your plan, QAT is an unnecessary complication; but, otherwise it can be a very effective approach.
The name speaks for itself: training is performed with awareness that the inference will be done in INT8. It results in a much faster model with uncompromised accuracy.
