Локальные LLM
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Discover and run local AI apps, serving optimized LLMs from your own GPU and NPU.
★ 5,8 тыс. звёздApache-2.0
Смотреть руководство →GPT4All — проект с открытым исходным кодом из категории «Локальные LLM» от nomic-ai (nomic-ai/gpt4all на GitHub). У него 77 360 звёзд на GitHub и 8 262 форка, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 27 мая 2025.
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
Download the free installer for Windows, macOS or Linux from nomic.ai/gpt4all.
In the app, open the model browser and pick one that fits your RAM — the catalogue shows sizes and requirements; downloads are a few GB each.
Start chatting fully offline, or add a folder under LocalDocs to ask questions about your own files with local retrieval.
Quantised GGUF models run usefully on ordinary laptops — no GPU required, which is the whole point.
Model browser, chat UI and settings in one free app; nothing to configure in a terminal.
Point it at document folders and chat with their contents using retrieval that never leaves the machine.
Python and Node bindings, plus an OpenAI-style local server, for building on the same models.
The route most people take — one download, then pick a model in-app.
https://nomic.ai/gpt4allFrom the README: install the bindings and load a model in a few lines.
pip install gpt4all
from gpt4all import GPT4All
model = GPT4All("Meta-Llama-3-8B-Instruct.Q4_0.gguf")
with model.chat_session():
print(model.generate("How can I run LLMs efficiently on my laptop?", max_tokens=1024))The desktop app can serve an OpenAI-compatible API on your machine for other tools to use.
Enable "Enable Local Server" in GPT4All settingsNomic AI (nomic-ai/gpt4all on GitHub), the company behind the Atlas data tool.
Yes — the app, SDKs and ecosystem are MIT licensed and free for commercial use. The models you download carry their own licences — check each one.
No. GPT4All is built for CPU inference with quantised models; a GPU speeds things up where supported, but a modern laptop is enough.
Against Ollama, GPT4All gives non-technical users a full graphical app and document chat out of the box, while Ollama is CLI-first and stronger as a developer server. Against LM Studio, the difference is licensing and openness: GPT4All is MIT open source, LM Studio is a polished free-but-closed app.
Install the desktop app, download a recommended model from the in-app browser, and chat — offline from the first message.
Discover and run local AI apps, serving optimized LLMs from your own GPU and NPU.
Elegant AI chat client with workspaces, plugins, and local-first sync.
Find the best local models for your hardware — and how to run them.
Google DeepMind's Gemma open-weight LLM library — install, load and chat with Gemma models in Python.
Lightweight inference engine for running LLMs efficiently on any device.
Run local LLMs like Llama and DeepSeek right inside your web browser.
Другие проекты из категории «Локальные LLM» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если GPT4All 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 GPT4All — продолжайте изучать полку по обе стороны от него.
🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team.
Run and fine-tune LLMs and diffusion models locally — faster training, smaller memory footprint.
A high-throughput and memory-efficient inference and serving engine for LLMs
The fastest, litest AI Gateway. Rust core with Python SDK. Call 100+ LLM APIs in OpenAI (or native) format with cost tracking, guardrails, load balancing, an…
LLM inference in C/C++
Run frontier AI locally.
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.