The second section discusses and compares the main two quantization approaches in TensorFlow Lite (TFLite): Post-Training Quantization (PTQ) and Quantization Aware Training (QAT). We will look in more detail at these approaches in Quantization Aware Training vs Post-Training Quantization. Quantization Aware Training (QAT) performs quantization by retraining the model, while Post-Training Quantization (PTQ) applies quantization without retraining the model. As with PTQ, QAT brings clear benefits in terms of model compression by reducing model size by 4x (32-bit/8-bit). In this way the model learns parameters which are robust to quantization loss and models the accuracy of a quantized model.