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Train Neural Networks without Draining your Pocket: Mixed Precision in PyTorch using Lightning

4 min readAug 4, 2026

A quick guide to demonstrate model training with Mixed precision in PyTorch using Lightning. Straightforward and easy to implement!

In the last two blogs we covered,

Precision Optimization in Model Training: The concept of precision optimization and how it impacts model training
Mixed Precision and Pixel Precision in TensorFlow: The code for implementing mixed precision and pixel precision in TensorFlow

Today we’ll discuss how to implement mixed precision and pixel precision in PyTorch Lightning.

Why two parts for PyTorch?

I’ve divided the mixed precision blog into two parts for PyTorch to make it easier to follow. Unlike TensorFlow where mixed precision implementation just needs a few small changes, native PyTorch version can seem a tad complicated. If you use Lightning for training the process is very similar to TensorFlow — a few changes here and there and you’re set to go. If you choose native PyTorch your code may seem more complicated (it’s not once you get the hang of it!) but it comes with the added advantage of flexibility. The second point is that the next few parts will cover custom precision and to go through that you have to be familiar with the…

mixed precision is easy to enable, but calling it cheaper hides the real cost. on unsupported gpus, the speedup can be tiny, while overflow and unstable gradients waste entire runs. tutorials should show memory, throughput, and convergence numbers before selling one config flag as savings

Mohana Roy Chowdhury

Train Neural Networks without Draining your Pocket: Mixed Precision in PyTorch using Lightning

Written by Mohana Roy Chowdhury

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