Jan - Turn your computer into an AI computer

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[!Warning] Jan is currently in Development: Expect breaking changes and bugs!

Jan is an open-source ChatGPT alternative that runs 100% offline on your computer.

Jan runs on any hardware. From PCs to multi-GPU clusters, Jan supports universal architectures:

[x] NVIDIA GPUs (fast)
[x] Apple M-series (fast)
[x] Apple Intel
[x] Linux Debian
[x] Windows x64

Download

Version Type	Windows	MacOS		Linux
Stable (Recommended)	jan.exe	Intel	M1/M2/M3/M4	jan.deb	jan.AppImage
Experimental (Nightly Build)	jan.exe	Intel	M1/M2/M3/M4	jan.deb	jan.AppImage

Download the latest version of Jan at https://jan.ai/ or visit the GitHub Releases to download any previous release.

Demo

Realtime Video: Jan v0.4.3-nightly on a Mac M1, 16GB Sonoma 14

Quicklinks

Jan

Nitro

Nitro is a high-efficiency C++ inference engine for edge computing. It is lightweight and embeddable, and can be used on its own within your own projects.

Troubleshooting

As Jan is in development mode, you might get stuck on a broken build.

To reset your installation:

Use the following commands to remove any dangling backend processes:
```
ps aux | grep nitro
```
Look for processes like "nitro" and "nitro_arm_64," and kill them one by one with:
```
kill -9 <PID>
```
Remove Jan from your Applications folder and Cache folder
```
make clean
```
This will remove all build artifacts and cached files:
- Delete Jan extension from your ~/jan/extensions folder
- Delete all node_modules in current folder
- Clear Application cache in ~/Library/Caches/jan

Requirements for running Jan

MacOS: 13 or higher
Windows:
- Windows 10 or higher
- To enable GPU support:
- Nvidia GPU with CUDA Toolkit 11.7 or higher
- Nvidia driver 470.63.01 or higher
Linux:
- glibc 2.27 or higher (check with ldd --version)
- gcc 11, g++ 11, cpp 11 or higher, refer to this link for more information
- To enable GPU support:
- Nvidia GPU with CUDA Toolkit 11.7 or higher
- Nvidia driver 470.63.01 or higher

Contributing

Contributions are welcome! Please read the CONTRIBUTING.md file

Pre-requisites

node >= 20.0.0
yarn >= 1.22.0
make >= 3.81

Instructions

Clone the repository and prepare:

git clone https://github.com/janhq/jan
cd jan
git checkout -b DESIRED_BRANCH

Run development and use Jan Desktop
```
make dev
```

This will start the development server and open the desktop app.

(Optional) Run the API server without frontend
```
yarn dev:server
```

For production build

# Do steps 1 and 2 in the previous section
# Build the app
make build

This will build the app MacOS m1/m2 for production (with code signing already done) and put the result in dist folder.

Docker mode

Supported OS: Linux, WSL2 Docker
Pre-requisites:
- Docker Engine and Docker Compose are required to run Jan in Docker mode. Follow the instructions below to get started with Docker Engine on Ubuntu.
```
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh ./get-docker.sh --dry-run
```
- If you intend to run Jan in GPU mode, you need to install nvidia-driver and nvidia-docker2. Follow the instruction here for installation.
Run Jan in Docker mode

User can choose between docker-compose.yml with latest prebuilt docker image or docker-compose-dev.yml with local docker build

Docker compose Profile	Description
`cpu-fs`	Run Jan in CPU mode with default file system
`cpu-s3fs`	Run Jan in CPU mode with S3 file system
`gpu-fs`	Run Jan in GPU mode with default file system
`gpu-s3fs`	Run Jan in GPU mode with S3 file system

Environment Variable	Description
`S3_BUCKET_NAME`	S3 bucket name - leave blank for default file system
`AWS_ACCESS_KEY_ID`	AWS access key ID - leave blank for default file system
`AWS_SECRET_ACCESS_KEY`	AWS secret access key - leave blank for default file system
`AWS_ENDPOINT`	AWS endpoint URL - leave blank for default file system
`AWS_REGION`	AWS region - leave blank for default file system
`API_BASE_URL`	Jan Server URL, please modify it as your public ip address or domain name default http://localhost:1377

Option 1: Run Jan in CPU mode

# cpu mode with default file system
docker compose --profile cpu-fs up -d

# cpu mode with S3 file system
docker compose --profile cpu-s3fs up -d

Option 2: Run Jan in GPU mode

Step 1: Check CUDA compatibility with your NVIDIA driver by running nvidia-smi and check the CUDA version in the output

nvidia-smi

# Output
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 531.18                 Driver Version: 531.18       CUDA Version: 12.1     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                      TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf            Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 4070 Ti    WDDM | 00000000:01:00.0  On |                  N/A |
|  0%   44C    P8               16W / 285W|   1481MiB / 12282MiB |      2%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
|   1  NVIDIA GeForce GTX 1660 Ti    WDDM | 00000000:02:00.0 Off |                  N/A |
|  0%   49C    P8               14W / 120W|      0MiB /  6144MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
|   2  NVIDIA GeForce GTX 1660 Ti    WDDM | 00000000:05:00.0 Off |                  N/A |
| 29%   38C    P8               11W / 120W|      0MiB /  6144MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|

Step 2: Visit NVIDIA NGC Catalog and find the smallest minor version of image tag that matches your CUDA version (e.g., 12.1 -> 12.1.0)
Step 3: Update the Dockerfile.gpu line number 5 with the latest minor version of the image tag from step 2 (e.g. change FROM nvidia/cuda:12.2.0-runtime-ubuntu22.04 AS base to FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04 AS base)
Step 4: Run command to start Jan in GPU mode

# GPU mode with default file system
docker compose --profile gpu-fs up -d

# GPU mode with S3 file system
docker compose --profile gpu-s3fs up -d

This will start the web server and you can access Jan at http://localhost:3000.

Note: RAG feature is not supported in Docker mode with s3fs yet.

Acknowledgements

Jan builds on top of other open-source projects:

Contact

Bugs & requests: file a GitHub ticket
For discussion: join our Discord here
For business inquiries: email hello@jan.ai
For jobs: please email hr@jan.ai

Trust & Safety

Beware of scams.

We will never ask you for personal info
We are a free product; there's no paid version
We don't have a token or ICO
We are not actively fundraising or seeking donations

License

Jan is free and open source, under the AGPLv3 license.

janhq / jan

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