PyTorch

Anyone who wants to build an AI model writes a program to do so. Nobody starts from scratch. Instead, one relies on a collection of ready-made building blocks that other people have prepared. Such a collection is called a library, and PyTorch is the best-known one. It provides the computational operations from which a learning program is assembled, and it takes care of the elaborate mathematics behind it. It was developed starting in 2016 at Facebook, is now maintained by an independent foundation, and anyone may use it free of charge.

Why almost every new model is built with it

PyTorch has established itself as the standard in research. When a university publishes a new method, the example code is usually available in PyTorch. This creates a cycle: because everyone uses it, it pays off for newcomers to learn it as well. Estimates suggest that well over 80 percent of scientific AI work is done with this tool.

The main competitor is called TensorFlow and comes from Google. It was the market leader for years but was considered unwieldy and hard to understand. PyTorch, by contrast, felt like ordinary programming and was easier to debug, meaning to search for errors. TensorFlow has never made up this lead again.

This is economically relevant because PyTorch supports Nvidia graphics cards particularly well. Anyone who writes a model in PyTorch is highly likely to also buy Nvidia hardware. Competitors such as AMD therefore have to invest a great deal of work to make PyTorch run smoothly on their chips too.

Tensors, gradients, and the blueprint in the background

The basic unit in PyTorch is called a tensor. This is simply a table of numbers that may also have more than two dimensions. A photo, for example, becomes a tensor of brightness values, a sentence becomes a tensor of numeric codes. At its core, everything an AI model does is computing with such number tables.

The second core building block is automatic differentiation. During training, a model needs to know in which direction to shift its internal numerical values in order to make fewer errors. This direction is called the gradient and is determined through a specific mathematical calculation. PyTorch keeps track of every computational operation and derives the gradient automatically. So one only describes the forward path, and the library handles the way back.

A particularly useful feature is that PyTorch only builds the computation path during execution. This means you can insert loops or conditions in the middle of the program and inspect intermediate results along the way. Earlier tools required the complete blueprint to be fixed in advance. This very flexibility was the reason for PyTorch’s success.

From university exercise to data center

PyTorch is never directly visible to ordinary users. But it lies beneath the surface of many well-known products. Meta's language models, large image generators, and numerous models on the Hugging Face platform are built with it. Anyone who downloads a model from there typically gets a PyTorch file.

In the news, the name mainly comes up in disputes about hardware and dependencies. When a chip manufacturer announces that its new accelerator natively supports PyTorch, that is an economic piece of news, not a technical footnote. Without this support, a chip is practically worthless to most developers.

A common misconception is that PyTorch is itself an AI. That is not true. It is a pure tool, comparable to a workbench along with its machines. What is created with it depends entirely on the data and the design of the developer. Beginners can try out PyTorch with basic knowledge of the Python programming language, often right in the browser without their own graphics card.