Локальные LLM
llama.cpp
LLM inference in C/C++
★ 130,2 тыс. звёздMIT
Смотреть руководство →Koboldcpp — проект с открытым исходным кодом из категории «Локальные LLM» от LostRuins (LostRuins/koboldcpp на GitHub). У него 11 931 звезда на GitHub и 787 форков, он распространяется по лицензии AGPL-3.0 и последний раз обновлялся на GitHub 3 октября 2026.
KoboldCpp - Run GGUF AI models locally with a KoboldAI UI. One File. Zero Install
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.
Clone the repository or download the latest release for your OS.
git clone https://github.com/LostRuins/koboldcpp.gitEvery project documents its own setup — the README's install section is the source of truth.
Start the app or server as documented and open the interface it provides.
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 LostRuins/koboldcpp with 787 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
Run controllable pre-trained AI models locally, written in Rust.
Run and fine-tune LLMs and diffusion models locally — faster training, smaller memory footprint.
Run AI models locally on your machine with node.js bindings for llama.cpp. Enforce a JSON schema on the model output on the generation level
Другие проекты из категории «Локальные LLM» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если Koboldcpp вам не подходит.
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
🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team.
Соседние проекты из категории «Локальные LLM» в каталоге, ближайшие по числу звёзд к Koboldcpp — продолжайте изучать полку по обе стороны от него.
Typescript/React Library for AI Chat 💬🚀
Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU.
Access large language models from the command-line
vLLM-Omni — efficient inference for omni-modality models: image, audio, video, diffusion and policies.
Open bilingual (Chinese-English) 6B chat LLM with 32K-context variant and low-memory INT4 inference.
DeepSeek-Coder-V2 — DeepSeek's open code model family that challenged closed-source code models.
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.