Unsloth Studio Installation

Unsloth Studio works on Windows, Linux, WSL and MacOS. You should use the same installation process on every device, although the system requirements may differ by device.

WindowsMacOSLinux & WSLDocker

• Training: Supported on NVIDIA GPUs: RTX 3090, Blackwell 50-series, DGX Spark etc.

• Mac: Like CPU - Chat only works for now. MLX training coming very soon.

• CPU: Unsloth still works without a GPU, but only for Chat inference.

• Coming soon: Support for Apple MLX, AMD, and Intel.

Install Instructions

Remember install instructions are the same across every device:

1

Install Unsloth

Firstly, install Unsloth with just one command:

Copy

pip install unsloth

Or install the latest code from source via pip with:

Copy

pip install git+https://github.com/unslothai/unsloth

Or you can download Studio directly from source:

2

Setup Unsloth Studio

Copy

unsloth studio setup

Setup automatically installs Node.js (via nvm), builds the frontend, installs all Python dependencies, and builds llama.cpp with CUDA support.

First install may take 5-10 minutes. This is normal as llama.cpp needs to compile binaries. Do not cancel it. We're working on precompiled binaries so next time it won't take so long.

WSL users: you will be prompted for your sudo password to install build dependencies (cmake, git, libcurl4-openssl-dev).

3

Launch

Launch Unsloth Studio via:

Copy

unsloth studio -H 0.0.0.0 -p 8888

Then open http://localhost:8888 in your browser.

4

Onboarding

On first launch you will need to create a password to secure your account and sign in again later. You’ll then see a brief onboarding wizard to choose a model, dataset, and basic settings. You can skip it at any time.

5

Start training and running

Start fine-tuning and building datasets immediately after launching. See our step-by-step guide to get started with Unsloth Studio:

Get Started

System Requirements

Windows

Unsloth Studio works directly on Windows without WSL. To train models, make sure your system satisfies these requirements:

Requirements

• Windows 10 or Windows 11 (64-bit)

• NVIDIA GPU with drivers installed

• App Installer (includes winget): here

• Git: winget install --id Git.Git -e --source winget

• Python: version 3.11 up to, but not including, 3.14

• Work inside a Python environment such as uv, venv, or conda/mamba

MacOS

Unsloth Studio works on Mac devices for Chat for GGUF models. MLX training coming soon!

• macOS 12 Monterey or newer (Intel or Apple Silicon)

• Install Homebrew: here

• Git: brew install git

• cmake: brew install cmake

• openssl: brew install openssl

• Python: version 3.11 up to, but not including, 3.14

• Work inside a Python environment such as uv, venv, or conda/mamba

Linux & WSL

• Ubuntu 20.04+ or similar distro (64-bit)

• NVIDIA GPU with drivers installed

• CUDA toolkit (12.4+ recommended, 12.8+ for blackwell)

• Git: sudo apt install git

• Python: version 3.11 up to, but not including, 3.14

• Work inside a Python environment such as uv, venv, or conda/mamba

Docker

• Pull our latest Unsloth container image: docker pull unsloth/unsloth

• Run the container via:

For more information, see here.

• Access your studio instance at http://localhost:8000 or external ip address http://external_ip_address:8000/

CPU only

Unsloth Studio only supports CPU devices for Chat for GGUF models.

• Same as the ones mentioned above for Linux (except for NVIDIA GPU drivers) and MacOS.

Google Colab

We’ve created a free Google Colab notebook so you can explore all of Unsloth’s features on Colab’s T4 GPUs. You can train and run most models up to 22B parameters, and switch to a larger GPU for bigger models. Just Click 'Run all' and the UI should pop up after installation.

Google Colabcolab.research.google.com

Once installation is complete, scroll to Start Unsloth Studio and click Open Unsloth Studio in the white box shown on the left:

Troubleshooting