Локальные LLM
llama.cpp
LLM inference in C/C++
★ 130,2 тыс. звёздMIT
Смотреть руководство →Cactus — проект с открытым исходным кодом из категории «Локальные LLM» от cactus-compute (cactus-compute/cactus на GitHub). У него 6 090 звёзд на GitHub и 512 форков, он распространяется по лицензии Custom (check repo) и последний раз обновлялся на GitHub 26 сентября 2026.
Quantization, kernels, runtime and inference engine for mobiles, wearables, smart home and robots
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/cactus-compute/cactus.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 cactus-compute/cactus with 512 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
Alibaba's lightweight, blazing-fast inference engine for on-device LLMs and edge AI.
Lightweight inference engine for running LLMs efficiently on any device.
Offline inference engine for art.
Другие проекты из категории «Локальные LLM» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если Cactus 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 Cactus — продолжайте изучать полку по обе стороны от него.
DeepSeek-Coder-V2 — DeepSeek's open code model family that challenged closed-source code models.
⚡ Pure-Rust WebGPU inference engine — OpenAI-API compatible, GGUF native, runs on any GPU. No Python. No llama.cpp. Single binary.
vLLM-Omni — efficient inference for omni-modality models: image, audio, video, diffusion and policies.
Discover and run local AI apps, serving optimized LLMs from your own GPU and NPU.
Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU.
Google DeepMind's Gemma open-weight LLM library — install, load and chat with Gemma models in Python.
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.