A model optimization technique that reduces the numerical precision of a neural network's weights and activations - for example, converting 32-bit floating-point values to 8-bit integers (INT8). Quantization significantly reduces model size, memory usage, and inference latency, making it essential for deploying models on edge devices and mobile hardware.