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GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.
| Date | Stars |
|---|---|
| 2026-07-31 | 77402 |
| 2026-08-01 | 77402 |
| 2026-08-05 | 77408 |
| 2026-08-06 | 77408 |
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<h1 align="center">GPT4All</h1>
<p align="center">
Now with support for DeepSeek R1 Distillations
</p>
<p align="center">
<a href="https://www.nomic.ai/gpt4all">Website</a> • <a href="https://docs.gpt4all.io">Documentation</a> • <a href="https://discord.gg/mGZE39AS3e">Discord</a> • <a href="https://www.youtube.com/watch?v=gQcZDXRVJok">YouTube Tutorial</a>
</p>
<p align="center">
GPT4All runs large language models (LLMs) privately on everyday desktops & laptops.
</p>
<p align="center">
No API calls or GPUs required - you can just download the application and <a href="https://docs.gpt4all.io/gpt4all_desktop/quickstart.html#quickstart">get started</a>.
</p>
<p align="center">
Read about what's new in <a href="https://www.nomic.ai/blog/tag/gpt4all">our blog</a>.
</p>
<p align="center">
<a href="https://nomic.ai/gpt4all/#newsletter-form">Subscribe to the newsletter</a>
</p>
https://github.com/nomic-ai/gpt4all/assets/70534565/513a0f15-4964-4109-89e4-4f9a9011f311
<p align="center">
GPT4All is made possible by our compute partner <a href="https://www.paperspace.com/">Paperspace</a>.
</p>
## Download Links
<p>
— <a href="https://gpt4all.io/installers/gpt4all-installer-win64.exe">
<img src="gpt4all-bindings/python/docs/assets/windows.png" style="height: 1em; width: auto" /> Windows Installer
</a> —
</p>
<p>
— <a href="https://gpt4all.io/installers/gpt4all-installer-win64-arm.exe">
<img src="gpt4all-bindings/python/docs/assets/windows.png" style="height: 1em; width: auto" /> Windows ARM Installer
</a> —
</p>
<p>
— <a href="https://gpt4all.io/installers/gpt4all-installer-darwin.dmg">
<img src="gpt4all-bindings/python/docs/assets/mac.png" style="height: 1em; width: auto" /> macOS Installer
</a> —
</p>
<p>
— <a href="https://gpt4all.io/installers/gpt4all-installer-linux.run">
<img src="gpt4all-bindings/python/docs/assets/ubuntu.svg" style="height: 1em; width: auto" /> Ubuntu Installer
</a> —
</p>
<p>
The Windows and Linux builds require Intel Core i3 2nd Gen / AMD Bulldozer, or better.
</p>
<p>
The Windows ARM build supports Qualcomm Snapdragon and Microsoft SQ1/SQ2 processors.
</p>
<p>
The Linux build is x86-64 only (no ARM).
</p>
<p>
The macOS build requires Monterey 12.6 or newer. Best results with Apple Silicon M-series processors.
</p>
See the full [System Requirements](gpt4all-chat/system_requirements.md) for more details.
<br/>
<br/>
<p>
<a href='https://flathub.org/apps/io.gpt4all.gpt4all'>
<img style="height: 2em; width: auto" alt='Get it on Flathub' src='https://flathub.org/api/badge'><br/>
Flathub (community maintained)
</a>
</p>
## Install GPT4All Python
`gpt4all` gives you access to LLMs with our Python client around [`llama.cpp`](https://github.com/ggerganov/llama.cpp) implementations.
Nomic contributes to open source software like [`llama.cpp`](https://github.com/ggerganov/llama.cpp) to make LLMs accessible and efficient **for all**.
```bash
pip install gpt4all
```
```python
from 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))
```
## Integrations
:parrot::link: [Langchain](https://python.langchain.com/v0.2/docs/integrations/providers/gpt4all/)
:card_file_box: [Weaviate Vector Database](https://github.com/weaviate/weaviate) - [module docs](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-gpt4all)
:telescope: [OpenLIT (OTel-native Monitoring)](https://github.com/openlit/openlit) - [Docs](https://docs.openlit.io/latest/integrations/gpt4all)
## Release History
- **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 SupportExcerpt of 5,925 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e223f676a05c07a8, topic:llm-inference