How to Run a Local LLM in Your Browser with WebGPU
Team 5 min read
#webgpu
#llm
#in-browser
#gpu
#frontend
Introduction
Running large language models locally in the browser is no longer science fiction. With WebGPU, you can tap into a machine’s GPU directly from JavaScript, enabling offline or privacy-conscious inference without sending prompts to a remote server. This post walks through the core ideas, practical considerations, and a step-by-step path to getting a small-to-mid size LLM running entirely in your browser.
What you will build
By the end, you’ll have a minimal in-browser UI that loads a quantized LLM model and generates text using WebGPU acceleration. The setup focuses on accessibility and safety: start with small models, host everything locally, and iterate on UX for a smooth experience.
Prerequisites
- A WebGPU-capable browser (Chrome/Edge canary builds with WebGPU enabled, or Safari with appropriate flags).
- A local static server to host the UI (examples: Python’s http.server, Node’s http-server, or a simple Vite/Parcel dev server).
- A small, quantized LLM model suitable for in-browser inference (for example, 4-bit GGML/GGUF-formatted models around 3–8 GB, depending on your device).
- Basic command line familiarity and a willingness to experiment with model formats and quantization.
Choosing a model and format
- Model size: start with 3–7B parameter models that are quantized to 4-bit or 2-bit precision for in-browser memory constraints.
- Format: prefer ggml/gguf variants which are designed for CPU/GPU inference in browsers via WebGPU backends.
- Model availability: ensure the model you choose is legally eligible for local, non-cloud use and that you have the rights to run it locally.
How WebGPU helps
- WebGPU provides low-latency GPU access from JavaScript, enabling parallelized matrix operations that power transformer models.
- In-browser inference with WebGPU reduces data transfer overhead and preserves privacy since prompts and completions stay on the device (assuming the model is loaded locally).
- The main trade-offs are memory usage and energy consumption, which improve with quantization and careful memory planning.
Step-by-step guide to running a local LLM in the browser
- Gather a browser-ready WebGPU bundle
- Find a small, browser-focused bundle or demo that wires a WebGPU-backed LLM to a web UI. Many projects provide a minimal loader and a UI that can be hosted locally.
- Ensure you have a compatible model file in a supported format (ggml/gguf) and that the bundle expects that format.
- Serve the UI locally
- Create a simple directory for your project and place the UI assets there.
- Start a local server to serve the files:
# Python 3
python -m http.server 8000
# Or with Node (if you have http-server installed)
npx http-server -p 8000- Load the model in the browser
- Place your model file in a models/ directory within the project.
- Use the provided loader script to initialize the WebGPU backend and point it at the model file, for example:
<script type="module">
import { LLMWebGPU } from './llm-webgpu-loader.js';
const llm = new LLMWebGPU({
modelPath: '/models/your-model.ggml',
maxTokens: 256
});
document.getElementById('startBtn').addEventListener('click', async () => {
const prompt = document.getElementById('prompt').value;
const resp = await llm.infer(prompt);
document.getElementById('output').textContent = resp;
});
</script>- Try a simple prompt
- Open http://localhost:8000 in your browser.
- Enter a short prompt and press the run button to see a generated response.
- Fine-tune for performance
- Reduce the number of tokens generated per request (maxTokens) during experimentation.
- Try lower precision quantization if supported (e.g., 4-bit) and ensure your device’s GPU drivers are up to date.
- Be mindful of memory limits: smaller models will fit more readily on mainstream laptops.
A minimal working example (structure and code)
Directory structure:
- index.html
- script.js
- models/
- your-model.ggml
index.html (skeleton):
<!doctype html>
<html>
<head>
<meta charset="utf-8" />
<title>WebGPU LLM Demo</title>
</head>
<body>
<h1>Local LLM in the Browser with WebGPU</h1>
<textarea id="prompt" placeholder="Ask your question..."></textarea>
<button id="startBtn">Run</button>
<pre id="output"></pre>
<script type="module" src="./script.js"></script>
</body>
</html>script.js (skeleton):
import { LLMWebGPU } from './llm-webgpu-loader.js';
const llm = new LLMWebGPU({
modelPath: '/models/your-model.ggml',
maxTokens: 256
});
document.getElementById('startBtn').addEventListener('click', async () => {
const prompt = document.getElementById('prompt').value;
const resp = await llm.infer(prompt);
document.getElementById('output').textContent = resp;
});Note: The actual loader and API names depend on the specific WebGPU-enabled LLM bundle you choose. Adapt paths and APIs according to the project you use.
Troubleshooting
- WebGPU not available: update your browser or enable WebGPU flags in chrome://flags (or equivalent in your browser).
- Model won’t load: verify the model format matches what the loader expects (ggml vs gguf) and that the file path is correct.
- Out of memory: try a smaller model, reduce maxTokens, or adjust quantization settings if supported.
- Performance seems slow: ensure you are running on hardware with a capable GPU and that the browser has access to sufficient VRAM.
Performance tips
- Start with quantized models (4-bit or 2-bit) to minimize memory footprint.
- Prefer streamable inference if the loader supports it to improve perceived latency.
- Disable nonessential browser extensions that might compete for GPU resources.
- Use a wired network and minimize background processes when benchmarking.
Next steps
- Explore more advanced UIs that provide streaming outputs, conversation history, and session management.
- Experiment with different quantization formats and model sizes to find the sweet spot for your hardware.
- Follow community projects for WebGPU-based LLMs to stay up to date with new backends and performance improvements.