Quantization-Aware Training (Qat)

Quantization-Aware Training (QAT) trains models with quantization effects simulated, yielding better low-precision accuracy than PTQ. Mechanism: Insert fake quantization nodes during training, forward pass simulates quantized behavior, gradients computed through straight-through estimator (STE), model learns to be robust to quantization noise. Why better than PTQ: Model adapts weights to quantization-friendly distributions, learns to avoid outlier activations, can recover accuracy lost in PTQ especially at very low precision (INT4, INT2). Training process: Start from pretrained FP model, add quantization simulation, fine-tune for additional epochs, export quantized model. Computational cost: 2-3x training overhead due to quantization simulation, requires representative training data, more complex training pipeline. When to use: Target precision is INT4 or lower, PTQ results unacceptable, have training infrastructure and data, accuracy is critical. Tools: PyTorch FX quantization, TensorFlow Model Optimization Toolkit, Brevitas. Trade-offs: Better accuracy than PTQ but requires training, best when combined with other compression techniques (pruning, distillation).