WebLLM

WebLLM is an in-browser language model inference engine that runs AI locally inside web browsers.

WebLLM is a high-performance in-browser language model inference engine that uses WebGPU for hardware acceleration to execute AI operations directly within web browsers without server-side processing. It brings language models natively to browser tabs to reduce costs, enhance personalization, and protect privacy. The engine offers full OpenAI API compatibility, supporting features like JSON-mode, function-calling, and streaming. It natively supports a range of models including Llama, Phi, Gemma, Mistral, and Qwen, while also allowing custom model integration in MLC format. Developers can integrate WebLLM into projects using package managers like NPM and Yarn, or via CDN, and offload computations to web workers or service workers to optimize performance.

WebLLM at a glance

Best for: Software engineer
Runs on: Web, Chrome extension
Pricing: Free
Released: April 2023
Category: AI coding tool
Website: webllm.mlc.ai

Key features

In-browser inference with WebGPU hardware acceleration
Full OpenAI API compatibility with JSON-mode and function-calling
Extensive model support including Llama, Phi, Gemma, and Mistral
Custom model integration in MLC format
Plug-and-play integration via NPM, Yarn, or CDN
Streaming chat completions for real-time interactions
Web worker and service worker support for optimized performance
Chrome extension support for browser extensions

Use it for

Building personal AI assistants that run inside the browser
Running chat services without server-side inference infrastructure
Developing browser extensions powered by local language models
Deploying custom language models for client-side web applications

Alternatives to WebLLM

vLLM — Maximizes throughput with PagedAttention and continuous batching.
LightLLM — It delivers high-speed inference and serving performance for large language models.
AirLLM — Runs massive 70B+ parameter models on small 4GB consumer GPUs.
MLC LLM — Enables native optimization and deployment of AI models across platforms.
llm-d — Deploys production-grade LLM serving on existing hardware clusters.
OpenLLM — It simplifies infrastructure management and optimizes inference speed and cost.
LMDeploy — Accelerates inference with high-performance engines and quantization tools.
RubyLLM — It provides a unified API for 17 AI providers with native Ruby and Rails integration.
Mesh LLM — Run large local models across multiple machines through one OpenAI-compatible endpoint.
LiteLLM — One OpenAI-format API for 100+ LLM providers
LLM CLI — It enables command-line interaction with remote APIs and local machine models.
OptiLLM — Increases LLM reasoning accuracy by up to 10x without model training.

All WebLLM alternatives