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.

To reach Studio over HTTPS, use unsloth studio --secure. Studio stays bound to localhost and is reached only through a free Cloudflare tunnel, which publishes it at a public https://*.trycloudflare.com URL (it fails closed if the tunnel can't start, so the raw port is never exposed). This makes Studio reachable from the internet, so anyone with the link and API key can use it and run code: keep your API key private (see Remote access below).

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. ```powershell git clon