# Unsloth

> Train and fine-tune LLMs faster with less GPU memory

- Category: Training & Fine-Tuning / Fine-Tuning Frameworks
- Language: Python
- Repository: https://github.com/unslothai/unsloth
- Website: https://unsloth.ai
- Canonical page: https://ai-tldr.dev/tools/unsloth/
- Updated: 2026-08-19

## In short

Unsloth is a library for fine-tuning and running large language models with lower memory use and shorter training times. It focuses on LoRA, QLoRA, and other parameter-efficient methods, along with full fine-tuning, reinforcement learning, and 4-bit, 16-bit, and FP8 training. It sits in the Fine-Tuning Frameworks category of the Training & Fine-Tuning tools and is open-source, written in Python.

## Overview

Unsloth is a library for fine-tuning and running large language models with lower memory use and shorter training times. It focuses on LoRA, QLoRA, and other parameter-efficient methods, along with full fine-tuning, reinforcement learning, and 4-bit, 16-bit, and FP8 training. According to the project, it can train and run RL on 500+ models up to 2x faster while using up to 70% less VRAM, with no accuracy loss.

It comes in two forms. Unsloth Studio is a web UI that runs on Windows, Linux, WSL, and macOS, letting you search, download, run, and train text, vision, audio, and embedding models, and export them to formats like GGUF and 16-bit safetensors. Unsloth Core is the code-based version installed with pip or uv, aimed at people who want to script training and inference directly.

It fits the fine-tuning frameworks category by lowering the hardware bar for training. With its custom Triton kernels and memory savings, you can fine-tune many open models on a single consumer GPU instead of a multi-GPU cluster.

## What it does

- Trains and runs RL on 500+ models up to 2x faster with up to 70% less VRAM, per the project
- Supports LoRA, QLoRA, full fine-tuning, RL, pretraining, and 4-bit, 16-bit, and FP8 training
- Custom Triton and math kernels, including collaborations with PyTorch and Hugging Face
- Unsloth Studio web UI to search, download, run, train, and export models on Windows, Linux, WSL, and macOS
- Exports trained models to GGUF, 16-bit safetensors, and other formats
- Reinforcement Learning support using up to 80% less VRAM for methods like GRPO

## Getting started

You can use Unsloth either through the Studio web UI or as a code-based install. The commands below come straight from the README.

1. Install Unsloth Studio (macOS, Linux, WSL)

   Run the install script. Use the same command later to update.

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

2. Install Unsloth Studio on Windows

   Use the PowerShell install command instead.

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

3. Launch the Studio

   Start the web UI on a port. It is local-only by default; add -H 0.0.0.0 for cloud or global access.

```bash
unsloth studio -p 8888
```

4. Or install Unsloth Core (code-based)

   For the Python library, install with uv. On Windows, pip install unsloth also works if PyTorch is already installed.

```bash
uv venv unsloth_env --python 3.13
source unsloth_env/bin/activate
uv pip install unsloth --torch-backend=auto
```

## When to use it

- Fine-tune an open LLM with LoRA or QLoRA on a single consumer GPU when VRAM is limited
- Run reinforcement learning (for example GRPO) on a model with reduced memory use
- Train and export a custom model to GGUF or safetensors for local inference
- Search, download, and run text, vision, audio, or embedding models locally through the Studio web UI

## Version history

- 2026-08-19 Dynamic 3.0: Unsloth Dynamic 3.0 is a new GGUF quantization recipe that Unsloth says keeps over 10% more top-1% accuracy at the same file size than any other provider; the first full set covers Qwen3.8-27B.