Локальные LLM
llama.cpp
LLM inference in C/C++
★ 130,2 тыс. звёздMIT
Смотреть руководство →Web LLM — проект с открытым исходным кодом из категории «Локальные LLM» от mlc-ai (mlc-ai/web-llm на GitHub). У него 19 219 звёзд на GitHub и 1 388 форков, он распространяется по лицензии Apache-2.0 и последний раз обновлялся на GitHub 3 октября 2026.
High-performance In-browser LLM Inference Engine
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
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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
Другие проекты из категории «Локальные LLM» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если Web LLM вам не подходит.
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LLM inference in C/C++
A high-throughput and memory-efficient inference and serving engine for LLMs
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Соседние проекты из категории «Локальные LLM» в каталоге, ближайшие по числу звёзд к Web LLM — продолжайте изучать полку по обе стороны от него.
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Compile and run LLMs anywhere - phones, laptops, browsers and GPUs - with one engine
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Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.