Quantization reduces the numerical precision of a model's weights (for example from 16 bits to 4 or 8 bits) to cut memory and speed up inference. It causes a loss of quality, often small but to be verified on the target task.
Example
A model quantized to 4 bits takes up about a quarter of the memory of its 16-bit version.