What Is WebGPU? AI in the Browser

01What Is WebGPU?

For many years, web pages relied mainly on the processor (CPU). WebGL existed for accessing the graphics card, but that technology was designed mainly for drawing graphics. The short answer to what WebGPU is is this: a next-generation web standard that opens the full power of the graphics card to the browser, for both graphics and computation.

WebGPU is a standard developed jointly by browser vendors under the W3C. Behind the scenes it uses the operating system's modern graphics interfaces: Direct3D 12 on Windows, Metal on macOS and iOS, and Vulkan on Linux and Android. The developer writes a single JavaScript interface and the browser translates it into the device's own language.

Its most important innovation is compute shader support. This means the graphics card can be used not just to paint pixels but to perform thousands of small calculations in parallel. Running AI models relies on exactly this kind of parallel matrix computation.

The small programs that run on the graphics card are called shaders. For these, WebGPU uses its own language called WGSL (WebGPU Shading Language). Most web developers don't deal with this language directly; graphics engines and AI libraries generate WGSL code behind the scenes. Still, knowing the basic logic helps in understanding performance issues: the processor describes the task, and the graphics card applies the same operation to thousands of data items at once.

02WebGPU vs. WebGL

WebGL is the web version of OpenGL ES, an older desktop graphics standard, and is still widely used. WebGPU, on the other hand, was designed from scratch around how today's graphics cards work. In short, the difference between WebGPU and WebGL is as follows:

03How Does AI Run in the Browser?

To run an AI model in the browser, the model file (the weights) is downloaded to the user's device and cached. JavaScript libraries then have the graphics card perform the model's calculations through WebGPU. The result is shown directly on the page without going to a server.

Developers don't need to write code from scratch for this. Open-source libraries such as ONNX Runtime Web, Transformers.js, TensorFlow.js and WebLLM offer WebGPU support. These libraries reduce running ready-made models in the browser to a few lines of code.

Another technology often mentioned alongside WebGPU is WebAssembly. WebAssembly runs code at near-native speed on the processor side, while WebGPU uses the graphics card. Many libraries use both together and fall back to WebAssembly on devices without WebGPU. For details, see our article on what WebAssembly is.

04Advantages of On-Device AI

On-device AI, or AI that runs on the device, means the model runs on the user's own device rather than on a server in the cloud. This approach has important advantages for businesses and users.

• Privacy: photos, audio or text never leave the device, which is a big plus for applications that handle sensitive data.
• Low latency: with no network round trip, responses are instant; critical for live camera effects and audio processing.
• Server cost: because the computation happens on the user's device, you don't pay for a server on every request.
• Offline use: once the model is downloaded, it can be used without an internet connection.
• Scalability: server load doesn't grow as the number of users grows, because each user uses their own device.

05Use Cases

AI in the browser doesn't yet replace large chat models entirely, but plenty can already be done with small, focused models.

Images and camera

Background removal, photo enhancement, object recognition and live camera effects can be done on the device. Experiences on an e-commerce site such as seeing a product in your own room or trying on glasses on your face rely on this technology.

Text and audio

Small language models can run in the browser for speech-to-text, translation, summarization and classification. Writing suggestions while filling out a form or a note-taking app that works offline are examples.

3D and visualization

Product configurators, architectural and real estate presentations, big data visualizations and browser games take advantage of WebGPU's graphics side. When these experiences are combined with good UI/UX design, the results are impressive.

06Browser Support and Feature Checks

WebGPU is widely available on desktop in Chromium-based browsers (Chrome, Edge), and support is arriving gradually in Safari and Firefox. The situation may differ on mobile devices and older hardware. Check the current support table in the MDN WebGPU documentation.

For this reason, every project that uses WebGPU should run a feature check and have a fallback path for unsupported devices. The code below checks whether the browser supports WebGPU.

• You can test the code quickly by running it in the browser's developer console.
• WebGPU can only be used in a secure context (HTTPS or localhost).
• On unsupported devices, switch to a WebGL- or WebAssembly-based alternative.

async function webgpuHazirMi() {
  if (!navigator.gpu) {
    console.log('Bu tarayici WebGPU desteklemiyor.');
    return null;
  }
  const adapter = await navigator.gpu.requestAdapter();
  if (!adapter) {
    console.log('Uygun GPU bulunamadi.');
    return null;
  }
  const device = await adapter.requestDevice();
  console.log('WebGPU hazir.');
  return device;
}

webgpuHazirMi();

07Limits and Things to Watch For

The biggest limit is model size. Large language models take up gigabytes; making every visitor download them is unrealistic in terms of both time and data allowance. Models that run in the browser need to be small, compressed (quantized) and focused on a specific task.

The second limit is device diversity. A model that runs smoothly on a powerful desktop graphics card can be slow on an entry-level phone or increase battery consumption. Check your analytics data to see which devices your target audience uses.

The third is page performance. Heavy model files and computation should not slow down the page's first load. Loading the model only when the user needs the relevant feature is the way to protect Core Web Vitals values (LCP ≤ 2.5 s, INP ≤ 200 ms). Striking this balance is a matter for speed and performance optimization.

08When Should You Use It in Web Projects?

WebGPU isn't necessary for every site. For a corporate presentation site or a standard e-commerce store, a fast, simple and accessible structure matters far more. WebGPU makes sense for AI tasks that require privacy, real-time image and audio processing, intensive 3D experiences, and computation jobs where you want to cut server costs.

When deciding, ask this question: what would I lose if this feature ran on the server? If the answer is privacy, latency or cost, WebGPU is a strong candidate. We covered the approach of putting AI at the center of a product in our article on AI-native applications, and the use of AI tools in the development process in our article on writing code with AI.

In web design projects and industry-specific software solutions, Aurora Yazılım chooses technology according to need, using a new standard only where it truly adds value. If you'd like to discuss your project, you can reach us through our contact page.