From Zero to GPU: A Guide to Building and Scaling Production-Ready CUDA Kernels

Back to Articles From Zero to GPU: A Guide to Building and Scaling Production-Ready CUDA Kernels Published August 18, 2025 Update on GitHub Upvote 91 +85 David Holtz drbh Follow Daniël de Kok danieldk Follow 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. What You’ll Learn When you're done, other developers will be able to use your kernels directly from the hub like this: import torch from kernels import get_kernel # Download custom kernel from the Hugging Face Hub optimized_kernel = get_kernel("your-username/optimized-kernel") # A sample input tensor some_input = torch.randn((10, 10), device="cuda") # Run the kernel out = optimized_kernel.my_kernel_function(some_input) print(out) Rather watch a video? Check out the YouTube video that accompanies this guide. Let's Get S...