Installation

Mac

Pre-requisites

Ensure that your MacOS version is 13 or higher to run Jan.

Stable Releases

To download stable releases, go to Jan.ai > select Download for Mac.

The download should be available as a .dmg.

Nightly Releases

We provide the Nightly Release so that you can test new features and see what might be coming in a future stable release. Please be aware that there might be bugs!

You can download it from Jan's Discord in the #nightly-builds channel.

Experimental Model

To enable the experimental mode, go to Settings > Advanced Settings and toggle the Experimental Mode

warning

If you are stuck in a broken build, go to the Broken Build section of Common Errors.

Windows

Pre-requisites

Ensure that your system meets the following requirements:

• Windows 10 or higher is required to run Jan.

To enable GPU support, you will need:

• NVIDIA GPU with CUDA Toolkit 11.7 or higher
• NVIDIA driver 470.63.01 or higher

Stable Releases

To download stable releases, go to Jan.ai > select Download for Windows.

The download should be available as a .exe file.

Nightly Releases

We provide the Nightly Release so that you can test new features and see what might be coming in a future stable release. Please be aware that there might be bugs!

You can download it from Jan's Discord in the #nightly-builds channel.

Experimental Model

To enable the experimental mode, go to Settings > Advanced Settings and toggle the Experimental Mode

Default Installation Directory

By default, Jan is installed in the following directory:

# Default installation directory
C:\Users\{username}\AppData\Local\Programs\Jan

warning

If you are stuck in a broken build, go to the Broken Build section of Common Errors.

Linux

Pre-requisites

Ensure that your system meets the following requirements:

• 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, you will need:

• NVIDIA GPU with CUDA Toolkit 11.7 or higher
• NVIDIA driver 470.63.01 or higher

Stable Releases

To download stable releases, go to Jan.ai > select Download for Linux.

The download should be available as a .AppImage file or a .deb file.

Nightly Releases

We provide the Nightly Release so that you can test new features and see what might be coming in a future stable release. Please be aware that there might be bugs!

You can download it from Jan's Discord in the #nightly-builds channel.

Experimental Model

To enable the experimental mode, go to Settings > Advanced Settings and toggle the Experimental Mode

Linux

To install Jan, you should use your package manager's install or dpkg.

Debian / Ubuntu

To install Jan, run the following command:

# Install Jan using dpkg
sudo dpkg -i jan-linux-amd64-{version}.deb

# Install Jan using apt-get
sudo apt-get install ./jan-linux-amd64-{version}.deb
# where jan-linux-amd64-{version}.deb is path to the Jan package

Others

To install Jan, run the following commands:

# Install Jan using AppImage
chmod +x jan-linux-x86_64-{version}.AppImage
./jan-linux-x86_64-{version}.AppImage
# where jan-linux-x86_64-{version}.AppImage is path to the Jan package

warning

If you are stuck in a broken build, go to the Broken Build section of Common Errors.

Docker

Pre-requisites

Ensure that your system meets the following requirements:

• Linux or WSL2 Docker
• Latest Docker Engine and Docker Compose

To enable GPU support, you will need:

• nvidia-driver
• nvidia-docker2

note

• If you have not installed Docker, follow the instructions here.
• If you have not installed the required file for GPU support, follow the instructions here.

Run Jan in Docker

You can run Jan in Docker with two methods:

1. Run Jan in CPU mode
2. Run Jan in GPU mode

CPU

To run Jan in Docker CPU mode, by using the following code:

# 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

GPU

To run Jan in Docker CPU mode, follow the steps below:

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

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      |
|=======================================================================================|

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)
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)
4. Run Jan in GPU mode by using the following command:

# 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

Docker Compose Profile and Environment

The available Docker Compose profile and the environment variables listed below:

Docker Compose Profile

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 Variables

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

warning

If you are stuck in a broken build, go to the Broken Build section of Common Errors.