What does model quantization aim to achieve?

Model quantization primarily aims to compress the weights of a neural network model and reduce its inference latency. By converting the floating-point weights into lower precision formats, such as integers, model quantization significantly decreases the size of the model and the amount of memory required for computation. This reduction in size leads to faster inference times because lower precision operations require less computational power and bandwidth, which is especially beneficial for deploying models in resource-constrained environments like mobile devices or edge computing scenarios.

The focus of quantization is on enhancing efficiency without substantially compromising accuracy, making it a key technique in optimizing deep learning models for real-world applications.