Model quantization represents model weights, activations, or cache values with fewer bits to reduce memory traffic, storage, energy, and often inference latency. This guide explains the mechanism, ...

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

The general definition of quantization states that it is the process of mapping continuous infinite values to a smaller set of discrete finite values. In this blog, we will talk about quantization in ...