LLM local
llama.cpp
LLM inference in C/C++
★ 130,2k estrellasMIT
Ver la guía →Open Webui es un proyecto de LLM local de código abierto de open-webui (open-webui/open-webui en GitHub). Tiene 153.872 estrellas en GitHub y 22.494 bifurcaciones, se publica bajo la licencia Custom (check repo) y se actualizó por última vez en GitHub el 3 de octubre de 2026.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
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/open-webui/open-webui.git
cd open-webuiIsolate dependencies so nothing clashes with your other projects.
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activateInstall the requirements, then follow the README's launch command (it varies per project).
pip install -r requirements.txtThe same local stack drives Pentest Harness, a penetration-testing harness that keeps its AI work on local models. The Local LLM shelf also holds entroly — Cut AI context cost with reversible, byte-exact compression. Readers working through the Local LLM shelf also stop at skales, personal AI agent that takes goals and works alone across your devices.
Roughly 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 open-webui/open-webui with 22,494 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
High-performance In-browser LLM Inference Engine
Open-source desktop app for local LLMs. Text, vision, tool-calling, OpenAI/Anthropic-compatible API. 100% private
Clean web interface for chatting with your local Ollama models.
Maid is a free and open source application for interfacing with llama.cpp models locally, and with Anthropic, DeepSeek, Ollama, Mistral and OpenAI models rem…
Los demás proyectos de LLM local con más estrellas del catálogo de LibreAI: la lista realista de alternativas si Open Webui no es lo que buscas.
The simplest way to run open language models on your own machine.
LLM inference in C/C++
A high-throughput and memory-efficient inference and serving engine for LLMs
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GPT4All — run local LLMs on any device: a free desktop chat client and Python SDK that run open models entirely on your CPU, no GPU or cloud required
Proyectos de LLM local cercanos en el catálogo, los más próximos en estrellas a Open Webui: sigue explorando a ambos lados.
The simplest way to run open language models on your own machine.
LLM inference in C/C++
Find the best local models for your hardware — and how to run them.
A high-throughput and memory-efficient inference and serving engine for LLMs
Token efficient translation utility built on ChatGPT or any OpenAI compatible LLM.
🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team.
Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.