Introducing @huggingface/kernels: 200+ WebGPU Kernels for Local AI

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Today, we are releasing the first layer of that effort: @huggingface/kernels, a minimal library for loading and running optimized WebGPU kernels from the Hugging Face Hub, together with an initial collection of 207 kernels at huggingface.co/webgpu-kernels.

The collection covers operations used across a wide variety of machine learning architectures and workloads. More importantly, each kernel is published as a complete, versioned package: its interface, shader templates, correctness cases, benchmark cases, and usage instructions all live together on the Hub.

We are also launching Fleet, an in-browser GPU benchmarking and testing suite that runs and scores the kernels on your hardware. Beyond the results for your own machine, Fleet gives the community a way to contribute performance and correctness evidence from devices we could never cover in a conventional test lab. With your consent, every run adds private evidence that can help us find failures (incorrect results, pathologically slow cases, etc.), improve kernel variants, and make better optimization decisions across real-world hardware.

A model running in the browser eventually becomes a sequence of GPU operations: matrix multiplications, normalizations, convolutions, attention primitives, quantization operations, data-layout transformations, and many more. WebGPU makes these operations available across modern browsers through a portable API, while WGSL provides a common language for the shaders that execute them.

Portability, however, does not automatically mean performance. Two shaders can implement the same operation and produce the same output while behaving completely differently across different accelerators. Workgroup sizes, memory access patterns, vectorization, data types, and fusion strategies can all affect performance. The best choice can also change with the input shape, device, browser, and available WebGPU features.

This is why kernels form a foundational layer of fast browser inference. Higher-level runtimes can only be as efficient as the operations they dispatch. By making those operations individually discoverable, testable, benchmarkable, and versioned, we can improve the foundation independently while keeping a stable contract for the layers above it.

Each kernel in the collection has its own repository and kernel card. The card documents the operation's semantics, inputs, outputs, attributes, supported data types, source files, and a ready-to-run @huggingface/kernels example.

For example, ai.onnx.Add implements elementwise addition with multidirectional broadcasting. It is one of the simplest operations in a neural network, used everywhere from residual connections to adding a bias. Its card documents the two inputs, the broadcasted output shape, supported data types, and the variants available for different shapes and devices.

Behind the card, the repository contains the artifacts needed to understand and evaluate the implementation:

This structure turns a shader into a reusable software artifact. The interface is inspectable without reading WGSL, correctness and performance cases travel with the