Getting Started - Docs - Changelog - Bug reports - Discord

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

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.

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

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

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

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

• 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

Contributions are welcome! Please read the CONTRIBUTING.md file

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

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.

3. (Optional) Run the API server without frontend

   yarn dev:server

# 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.

• 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

• 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.

Jan builds on top of other open-source projects:

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

• 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

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

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