Project Title

web-llm — High-Performance In-Browser LLM Inference Engine for AI Assistants

Overview

WebLLM is a high-performance in-browser LLM inference engine that enables language model inference directly within web browsers, utilizing hardware acceleration via WebGPU. It is fully compatible with the OpenAI API, allowing developers to use open-source models locally with features like streaming and JSON-mode.

Key Features

In-Browser Inference: Leverages WebGPU for hardware acceleration, enabling powerful LLM operations directly within web browsers without server-side processing.
Full OpenAI API Compatibility: Seamlessly integrate your app with WebLLM using OpenAI API functionalities such as streaming, JSON-mode, and logit-level control.
Structured JSON Generation: Supports state-of-the-art JSON mode structured generation, implemented in WebAssembly for optimal performance.

Use Cases

AI Assistants: Build AI assistants for web applications with real-time interaction capabilities.
Privacy-Focused Applications: Enable privacy by processing AI operations locally without server-side involvement.
Custom Model Deployment: Integrate and deploy custom models in MLC format for specific needs and scenarios.

Advantages

Enhanced Performance: Utilizes WebGPU for hardware acceleration, improving the performance of LLM operations in the browser.
Open Source Flexibility: Compatible with any open-source models, providing flexibility in model choice and customization.
Real-Time Interactions: Supports streaming chat completions, enhancing interactive applications like chatbots.

Limitations / Considerations

Browser Compatibility: May require modern browsers that support WebGPU for optimal performance.
Model Size and Complexity: Handling very large or complex models may be limited by browser resources and performance.

TensorFlow.js: A JavaScript library for training and deploying machine learning models in the browser, differing in its broader scope beyond LLMs.
ONNX Runtime: An open-source scoring engine for Open Neural Network Exchange (ONNX) models, which is more focused on general neural network models rather than LLMs specifically.
Hugging Face Transformers: A library of pre-trained models for Natural Language Processing (NLP), offering a different approach with a focus on model training and fine-tuning rather than in-browser inference.

Basic Information

GitHub: https://github.com/mlc-ai/web-llm
Stars: 16,441
License: Unknown
Last Commit: 2025-09-15

📊 Project Information

Project Name: web-llm
GitHub URL: https://github.com/mlc-ai/web-llm
Programming Language: TypeScript
⭐ Stars: 16,441
🍴 Forks: 1,096
📅 Created: 2023-04-13
🔄 Last Updated: 2025-09-15

🏷️ Project Topics

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