What is WebLLM — In-Browser LLM Inference Engine?

Run large language models entirely in the browser with WebGPU acceleration and no server required.

Is WebLLM — In-Browser LLM Inference Engine free to use?

Yes. WebLLM — In-Browser LLM Inference Engine is freely available on TokRepo. Check the Source & Thanks section on the asset page for the specific open-source license.

How do I install WebLLM — In-Browser LLM Inference Engine?

Visit the asset page on TokRepo and click "Copy for agent" to get the installation instructions. Most assets can be installed with a single command.

WebLLM — In-Browser LLM Inference Engine

Introduction

WebLLM brings large language model inference directly into the browser using WebGPU for hardware acceleration. It eliminates the need for server-side computation, enabling fully private, offline-capable AI chat and text generation on any modern browser that supports WebGPU.

What WebLLM Does

Runs LLMs (Llama, Mistral, Phi, Gemma, Qwen) entirely client-side in the browser
Leverages WebGPU for near-native GPU performance without plugins or extensions
Provides an OpenAI-compatible chat completions API for drop-in integration
Supports streaming responses, JSON mode, and function calling
Caches model weights in browser storage for instant subsequent loads

Architecture Overview

WebLLM compiles models through Apache TVM's machine learning compiler stack into WebGPU-optimized shaders. At runtime, a lightweight JavaScript engine loads quantized model weights into GPU memory via the WebGPU API, executes transformer attention and feed-forward layers as compute shaders, and exposes an OpenAI-compatible interface. A service worker mode allows background inference without blocking the main UI thread.

Self-Hosting & Configuration

Install via npm or load from a CDN script tag in any web project
Choose from dozens of pre-quantized models hosted on Hugging Face
Configure quantization level (q4f16, q4f32) to balance quality vs. VRAM usage
Set context window size and generation parameters (temperature, top-p) per request
Use the service worker engine variant for multi-tab or PWA deployments

Key Features

Zero server dependency means complete data privacy for end users
OpenAI-compatible API makes migration from cloud LLMs straightforward
Supports structured output via JSON mode and grammar-guided decoding
Pre-built model library covers instruction-tuned and code-generation models
Works on Chrome, Edge, and any browser with WebGPU support

Comparison with Similar Tools

Ollama — runs models locally on desktop but requires a native binary; WebLLM runs purely in-browser
llama.cpp (WASM) — compiles to WebAssembly with CPU-only execution; WebLLM uses WebGPU for GPU acceleration
Transformers.js — targets smaller encoder models via ONNX Runtime; WebLLM handles full-size decoder LLMs
LM Studio — desktop GUI application requiring installation; WebLLM needs only a web page
PrivateGPT — server-side Python stack; WebLLM is a client-side JavaScript library

FAQ

Q: What browsers support WebLLM? A: Any browser with WebGPU enabled, including Chrome 113+, Edge 113+, and recent builds of Firefox and Safari.

Q: How much VRAM do I need? A: A 4-bit quantized 8B model requires roughly 4-5 GB of GPU memory. Smaller 1-3B models work with 2 GB.

Q: Can I fine-tune or add my own models? A: WebLLM consumes pre-compiled model libraries. You can compile custom models using the MLC-LLM toolchain and host them for WebLLM to load.

Q: Does it work on mobile devices? A: WebGPU support on mobile is still emerging. Android Chrome has partial support; iOS Safari support is in development.

WebLLM — In-Browser LLM Inference Engine

Agent 可直接安装

Introduction

What WebLLM Does

Architecture Overview

Self-Hosting & Configuration

Key Features

Comparison with Similar Tools

FAQ

Sources

讨论

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