Essentially all AI training is done with 32-bit floating point. But doing AI inference with 32-bit floating point is expensive, power-hungry and slow. And quantizing models for 8-bit-integer, which is ...

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 ...

Sponsored Feature: Training an AI model takes an enormous amount of compute capacity coupled with high bandwidth memory. Because the model training can be parallelized, with data chopped up into ...