Github · GitHub Repository Radar
nomic-ai gpt4all
GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.
Stars
77,384
Forks
8,292
Watchers: 77,384
Language
License: MIT License
Repository Radar Score
48 / 100
Growth
- 7d
- +0
- 30d
- +0
- %
- 0.0%
Not enough metric snapshots yet to chart growth for this repository.
Score breakdown
- popularity 90
- growth 0
- activity 15
- freshness 100
- community 82
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Website • Documentation • Discord • YouTube Tutorial
GPT4All runs large language models (LLMs) privately on everyday desktops & laptops.
No API calls or GPUs required - you can just download the application and get started.
Read about what's new in our blog.
gpt4all_2.mp4
GPT4All is made possible by our compute partner Paperspace.
Windows Installer
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macOS Installer
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Ubuntu Installer
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The Windows and Linux builds require Intel Core i3 2nd Gen / AMD Bulldozer, or better.
The Windows ARM build supports Qualcomm Snapdragon and Microsoft SQ1/SQ2 processors.
The Linux build is x86-64 only (no ARM).
The macOS build requires Monterey 12.6 or newer. Best results with Apple Silicon M-series processors.
See the full System Requirements for more details.
Flathub (community maintained)
gpt4all gives you access to LLMs with our Python client around llama.cpp implementations.
Nomic contributes to open source software like llama.cpp to make LLMs accessible and efficient for all.
pip install gpt4allfrom gpt4all import GPT4All
model = GPT4All("Meta-Llama-3-8B-Instruct.Q4_0.gguf") # downloads / loads a 4.66GB LLM
with model.chat_session():
print(model.generate("How can I run LLMs efficiently on my laptop?", max_tokens=1024))🦜🔗 Langchain 🗃️ Weaviate Vector Database - module docs 🔭 OpenLIT (OTel-native Monitoring) - Docs
- July 2nd, 2024: V3.0.0 Release
- Fresh redesign of the chat application UI
- Improved user workflow for LocalDocs
- Expanded access to more model architectures
- October 19th, 2023: GGUF Support Launches with Support for:
- Mistral 7b base model, an updated model gallery on our website, several new local code models including Rift Coder v1.5
- Nomic Vulkan support for Q4_0 and Q4_1 quantizations in GGUF.
- Offline build support for running old versions of the GPT4All Local LLM Chat Client.
- September 18th, 2023: Nomic Vulkan launches supporting local LLM inference on NVIDIA and AMD GPUs.
- July 2023: Stable support for LocalDocs, a feature that allows you to privately and locally chat with your data.
- June 28th, 2023: Docker-based API server launches allowing inference of local LLMs from an OpenAI-compatible HTTP endpoint.
GPT4All welcomes contributions, involvement, and discussion from the open source community! Please see CONTRIBUTING.md and follow the issues, bug reports, and PR markdown templates.
Check project discord, with project owners, or through existing issues/PRs to avoid duplicate work.
Please make sure to tag all of the above with relevant project identifiers or your contribution could potentially get lost.
Example tags: backend, bindings, python-bindings, documentation, etc.
If you utilize this repository, models or data in a downstream project, please consider citing it with:
@misc{gpt4all,
author = {Yuvanesh Anand and Zach Nussbaum and Brandon Duderstadt and Benjamin Schmidt and Andriy Mulyar},
title = {GPT4All: Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/nomic-ai/gpt4all}},
}
Languages
Share of the codebase by language, based on repository metadata from the host.
- C++ 51.9%
- QML 30.3%
- Python 7.6%
- CMake 5.4%
- JavaScript 3.2%
- C 0.9%
- Qt Script 0.3%
- Shell 0.2%
- Makefile 0.1%
- Batchfile 0.1%
- PowerShell 0.0%
- Objective-C++ 0.0%
- CSS 0.0%
Repository Radar analysis
Deterministic insights derived from public metadata and our observations — not personal testing or reviews.
Why this repository is interesting
- High absolute popularity (77,384 stars) signals broad adoption.
Who should use it
- Developers working primarily with C++
- Teams exploring AI tooling, agents, or ML infrastructure
Potential use cases
- Reference or evaluate C++ open-source approaches in this domain
- Prototype AI/agent workflows or study reference architectures
Strengths
- README present in our index
- Declared license: MIT License
- Substantial fork count (8,292) suggests reuse and contribution interest
Limitations / considerations
- Insights are derived from public metadata and our observations — not a substitute for code review
What to watch
- Re-check last push, issues, and releases on GitHub before production adoption
Strong signals: Strong community interest
Source: GitHub (public metadata) + Repository Radar analysis. We do not claim ownership of third-party repositories.