Custom CUDA kernels give your models a serious performance edge, but building them for the real world can feel daunting. How do you move beyond a simple GPU function to create a robust, scalable system without getting bogged down by endless build times and dependency nightmares?

We created the kernel-builder library for this purpose. You can develop a custom kernel locally, and then build it for multiple architectures and make it available for the world to use.

In this guide we'll show you how to build a complete, modern CUDA kernel from the ground up. Then, we’ll tackle the tough production and deployment challenges, drawing on real-world engineering strategies to show you how to build systems that are not just fast, but also efficient and maintainable.

When you're done, other developers will be able to use your kernels directly from the hub like this:

Rather watch a video? Check out the YouTube video that accompanies this guide.

Let's build a practical kernel that converts an image from RGB to grayscale. This example uses PyTorch's modern C++ API to register our function as a first-class, native operator.

A clean, predictable structure is the foundation of a good project. The Hugging Face Kernel Builder expects your files to be organized like this:

This file orchestrates the entire build. It tells the kernel-builder what to compile and how everything connects.

nix file. It locks the exact version of the kernel-builder and its dependencies, eliminating "it works on my machine" issues.

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