
layout: blog_detail
title: 'Practical Quantization in PyTorch'
author: Suraj Subramanian, Mark Saroufim, Jerry Zhang
featured-img: ''

Quantization is a cheap and easy way to make your DNN run faster and with lower memory requirements. PyTorch offers a few different approaches to quantize your model. In this blog post, we'll lay a (quick) foundation of quantization in deep learning, and then take a look at how each technique looks like in practice. Finally we'll end with recommendations from the literature for using quantization in your workflows.



  Fig 1. PyTorch <3 Quantization

Contents
* TOC
Fundamentals of Quantization

If someone asks you what time it is, you don't respond "10:14:34:430705", but you might say "a quarter past 10".

Quantization has roots in information compression; in deep networks it refers to reducing the numerical precision of its weights and/or activations.