Unsloth Studio lets you run and train models locally.

Features • Quickstart • Notebooks • Documentation

⚡ Get started

macOS, Linux, WSL:

curl -fsSL https://unsloth.ai/install.sh | sh

Windows:

irm https://unsloth.ai/install.ps1 | iex

Community:

• Discord
• 𝕏 (Twitter)
• Reddit

⭐ Features

Unsloth Studio (Beta) lets you run and train text, audio, embedding, vision models on Windows, Linux and macOS.

Inference

• Search + download + run models including GGUF, LoRA adapters, safetensors
• Export models: Save or export models to GGUF, 16-bit safetensors and other formats.
• Tool calling: Support for self-healing tool calling and web search
• Code execution: lets LLMs test code in Claude artifacts and sandbox environments
• API inference endpoint: Deploy and run local LLMs in Claude Code, Codex tools with Unsloth
• Auto set inference settings and customize chat templates.
• We work directly with teams behind gpt-oss, Qwen3, Llama 4, Mistral, Gemma 1-3, and Phi-4, where we’ve fixed bugs that improve model accuracy.
• Chat with images, audio, PDFs, code, DOCX and more. Connect API providers (OpenAI, Anthropic) or servers (vLLM, Ollama).

Training

• Train and RL 500+ models up to 2x faster with up to 70% less VRAM, with no accuracy loss.
• Custom Triton and mathematical kernels. See some collabs we did with PyTorch and Hugging Face.
• Data Recipes: Auto-create datasets from PDF, CSV, DOCX etc. Edit data in a visual-node workflow.
• Reinforcement Learning (RL): The most efficient RL library, using 80% less VRAM for GRPO, FP8 etc.
• Supports full fine-tuning, RL, pretraining, 4-bit, 16-bit and, FP8 training.
• Observability: Monitor training live, track loss and GPU usage and customize graphs.
• Multi-GPU training is supported, with major improvements coming soon.

📥 Install

Unsloth can be used in two ways: through Unsloth Studio, the web UI, or through Unsloth Core, the code-based version. Each has different requirements.

Unsloth Studio (web UI)

Unsloth Studio (Beta) works on Windows, Linux, WSL and macOS.

• CPU: Supported for Chat and Data Recipes currently
• NVIDIA: Training works on RTX 30/40/50, Blackwell, DGX Spark, Station and more
• macOS: Training, MLX and GGUF inference are ALL supported.
• AMD: Chat + Data works. Train with Unsloth Core. Studio support is out soon.
• Multi-GPU: Available now, with a major upgrade on the way

macOS, Linux, WSL:

curl -fsSL https://unsloth.ai/install.sh | sh

Use the same command to update.

Windows:

irm https://unsloth.ai/install.ps1 | iex

Use the same command to update.

Launch

unsloth studio -p 8888

For cloud or global access, add -H 0.0.0.0. By default, Unsloth is accessible only locally.

For a secure HTTPS link instead of a raw network port, use unsloth studio --secure. Studio stays bound to localhost and is served only through a free Cloudflare HTTPS tunnel (it fails closed if the tunnel can't start, so the raw port is never exposed).

Docker

Use our Docker image unsloth/unsloth container. Run:

docker run -d -e JUPYTER_PASSWORD="mypassword" \
  -p 8888:8888 -p 8000:8000 -p 2222:22 \
  -v $(pwd)/work:/workspace/work \
  --gpus all \
  unsloth/unsloth
  ```

#### Developer, Nightly, Uninstall
To see developer, nightly and uninstallation etc. instructions, see [advanced installation](#-advanced-installation).

### Unsloth Core (code-based)
#### Linux, WSL:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv unsloth_env --python 3.13
source unsloth_env/bin/activate
uv pip install unsloth --torch-backend=auto

Windows:

winget install -e --id Python.Python.3.13
winget install --id=astral-sh.uv  -e
uv venv unsloth_env --python 3.13
.\unsloth_env\Scripts\activate
uv pip install unsloth --torch-backend=auto

For Windows, pip install unsloth works only if you have PyTorch installed. Read our Windows Guide. You can use the same Docker image as Unsloth Studio.

AMD, Intel:

For RTX 50x, B200, 6000 GPUs: uv pip install unsloth --torch-backend=auto. Read our guides for: Blackwell and DGX Spark.

To install Unsloth on AMD and Intel GPUs, follow our AMD Guide and Intel Guide.

📒 Free Notebooks

Train for free with our notebooks. You can use our new free Unsloth Studio notebook to run and train models for free in a web UI. Read our guide. Add dataset, run, then deploy your trained model.

• See all our notebooks for: Kaggle, GRPO, TTS, embedding & Vision
• See all our models and all our notebooks
• See detailed documentation for Unsloth here

🦥 Unsloth News

• Connections: Connect any API provider (OpenAI, Anthropic) or server (vLLM, Ollama). Guide
• MTP: Run Qwen3.6 MTP in Unsloth. MTP settings are autoset specific to your hardware. Guide
• API inference endpoint: Deploy and run local LLMs in Claude Code, Codex tools. Guide
• Qwen3.6: Qwen3.6-35B-A3B can now be trained and run in Unsloth Studio. Blog
• Gemma 4: Run and train Google’s new models directly in Unsloth. Blog
• Introducing Unsloth Studio: our new web UI for running and training LLMs. Blog
• Qwen3.5 - 0.8B, 2B, 4B, 9B, 27B, 35-A3B, 112B-A10B are now supported. Guide + notebooks
• Train MoE LLMs 12x faster with 35% less VRAM - DeepSeek, GLM, Qwen and gpt-oss. Blog
• Embedding models: Unsloth now supports ~1.8-3.3x faster embedding fine-tuning. Blog • Notebooks
• New 7x longer context RL vs. all other setups, via our new batching algorithms. Blog
• New RoPE & MLP Triton Kernels & Padding Free + Packing: 3x faster training & 30% less VRAM. Blog
• 500K Context: Training a 20B model with >500K context is now possible on an 80GB GPU. Blog
• FP8 & Vision RL: You can now do FP8 & VLM GRPO on consumer GPUs. FP8 Blog • Vision RL

📥 Advanced Installation

The below advanced instructions are for Unsloth Studio. For Unsloth Core advanced installation, view our docs.

Developer / Nightly / Experimental installs: macOS, Linux, WSL:

The developer install builds from the main branch, which is the latest (nightly) source.

git clone https://github.com/unslothai/unsloth
cd unsloth
./install.sh --local
unsloth studio -p 8888

To install into an isolated location (its own virtual env, auth/, studio.db, cache and llama.cpp build), set UNSLOTH_STUDIO_HOME and pass it again at launch:

UNSLOTH_STUDIO_HOME="$PWD/.studio" ./install.sh --local
UNSLOTH_STUDIO_HOME="$PWD/.studio" unsloth studio -p 8888

Then to update :

cd unsloth && git pull
./install.sh --local
unsloth studio -p 8888

Developer / Nightly / Experimental installs: Windows PowerShell:

The developer install builds from the main branch, which is the latest (nightly) source.

git clone https://github.com/unslothai/unsloth.git
cd unsloth
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\install.ps1 --local
unsloth studio -p 8888

To install into an isolated locat