Jan - Turn your computer into an AI computer

Getting Started - Docs - Changelog - Bug reports - Discord

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:

• NVIDIA GPUs (fast)
• Apple M-series (fast)
• Apple Intel
• Linux Debian
• 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

• Jan website
• Jan GitHub
• User Guides
• Developer docs
• API reference
• Specs

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.

• Nitro Website
• Nitro GitHub
• Documentation
• API Reference

Troubleshooting

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

To reset your installation:

1. 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>

2. 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

1. Clone the repository and prepare:

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

2. Run development and use Jan Desktop

   make dev

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

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:

• llama.cpp
• LangChain
• TensorRT
• TensorRT-LLM

Contact

• Bugs & requests: file a GitHub ticket
• For discussion: join our Discord here
• For business inquiries: email [email protected]
• For jobs: please email [email protected]

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.