Local LLM
llama.cpp
LLM inference in C/C++
★ 130.2k starsMIT
View guide →High-performance In-browser LLM Inference Engine
Web LLM is an open-source local llm project by mlc-ai (mlc-ai/web-llm on GitHub). It has 19,219 GitHub stars and 1,388 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 3 October 2026.
One-command installers exist for every major OS; servers and engines use Docker or simple binaries.
Pull from a built-in library or point at any GGUF/compatible weights you already have.
Use the bundled chat UI, or point your apps and coding agents at the local OpenAI-style endpoint.
Pick model sizes that fit your RAM/VRAM; quantization options trade a little quality for a lot of speed.
Prompts never leave your machine — no accounts, no logging, no training on your data.
Once weights are downloaded, everything runs with the network cable unplugged.
Local endpoints your existing tools and code can call with minimal changes.
Llama, Qwen, Gemma, DeepSeek, Mistral and more, in sizes from phone-friendly to workstation-class.
Coding assistants and automation tools plug straight into the local server.
Run bigger models on smaller hardware via GGUF and friends.
Download the source and move into the project folder.
git clone https://github.com/mlc-ai/web-llm.git
cd web-llmUse the package manager the project specifies (check for pnpm or yarn lockfiles).
npm installLaunch the dev server or build, per the README.
npm run devRoughly the model size in GB plus headroom — 8B models fit comfortably on 16GB machines with quantization.
Local wins on privacy and cost; cloud wins on raw frontier quality. Many people use both.
It is an open-source project at mlc-ai/web-llm with 1,388 forks. Check the repo's contributors and recent commits to see how active maintenance is.
A recent mid-size open model in the library's recommendations — start small and scale up.
Yes — the local API plus client libraries make it a popular backend for indie apps.
Yes — several of these are optimized for it, and unified memory helps a lot.
LLM inference in C/C++
A high-throughput and memory-efficient inference and serving engine for LLMs
Open-source desktop app for local LLMs. Text, vision, tool-calling, OpenAI/Anthropic-compatible API. 100% private
TokenSpeed — a speed-of-light LLM inference engine designed for agentic workloads, with TensorRT-LLM-level performance and vLLM-level usability
Run a 70B LLM on a single 4GB GPU — layer-by-layer inference with almost no memory tricks skipped
Compile and run LLMs anywhere - phones, laptops, browsers and GPUs - with one engine
The most-starred other Local LLM projects in the LibreAI catalogue — the realistic shortlist if Web LLM is not the right fit.
The simplest way to run open language models on your own machine.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
LLM inference in C/C++
A high-throughput and memory-efficient inference and serving engine for LLMs
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Free and open — grab the source, star the repo, and start creating.