Локальные LLM
llama.cpp
LLM inference in C/C++
★ 130,2 тыс. звёздMIT
Смотреть руководство →whatcanirun — проект с открытым исходным кодом из категории «Локальные LLM» от fiveoutofnine (fiveoutofnine/whatcanirun на GitHub). У него 247 звёзд на GitHub и 23 форка, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 26 августа 2026.
Find the best local models for your hardware — and how to run them.
Clone the repo or visit the official site to get started.
The README documents install requirements and configuration.
Connect your own API keys where the project requires them.
Launch the app and start using it.
Recommends models that fit your RAM and chip — no VRAM guesswork.
Focused on the two stacks that run best on consumer hardware.
Get the exact run command for each recommended model.
Tuned for M-series Macs where most local inference happens.
Sets expectations on tokens-per-second before you download gigabytes.
Free and open — fork it or self-host the picker.
Download the source and move into the project folder.
git clone https://github.com/fiveoutofnine/whatcanirun.git
cd whatcanirunEvery project documents its own setup; start with install requirements.
cat README.md | head -60Follow the project's launch command — Docker, pip, npm, or a binary release.
# see README for the exact launch commandYes — MIT licensed and free to use.
Built primarily around Apple Silicon Macs and llama.cpp-compatible setups.
It recommends and gives commands; you run them yourself.
MLX is fastest on Apple Silicon; llama.cpp runs almost anywhere. The tool covers both.
It is open source — check the repo for contribution notes.
LLM inference in C/C++
The simplest way to run open language models on your own machine.
Jan is an open source alternative to ChatGPT that runs 100% offline on your computer
Hundreds of models & providers. One command to find what runs on your hardware.
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
Noise-robust subtitle generator powered by Whisper and local ASR models.
Другие проекты из категории «Локальные LLM» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если whatcanirun 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 whatcanirun — продолжайте изучать полку по обе стороны от него.
Token efficient translation utility built on ChatGPT or any OpenAI compatible LLM.
The simplest way to run open language models on your own machine.
Self-hosted control plane: approve or block rogue agent calls.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
Pentest Harness — Heaven for Hackers.
LLM inference in C/C++
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.