---
id: unsloth
version: "2026.8.18"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# unsloth — 2-5X faster training, reinforcement learning & finetuning

License: permissive · Maintenance: active · Downloads: 2.3M/mo

## What it is and what it does

Unsloth is a PyTorch-based framework for accelerating the training and fine-tuning of large language models, diffusion models, and embeddings on consumer-grade hardware. It combines quantization techniques, memory optimization, and hardware-specific kernels to reduce VRAM requirements and training time. The package integrates with the Hugging Face ecosystem—transformers, datasets, peft, trl, accelerate—and supports reinforcement learning workflows.

The project offers three interfaces: a native desktop app, a web UI called Unsloth Studio, and the PyPI package for programmatic use. The PyPI install targets developers who want to integrate optimized training into Python scripts or notebooks; it requires Python 3.9–3.14 and a compatible GPU or CPU backend. No known security vulnerabilities are recorded.

Use it for:

- Fine-tune open-source LLMs on your own hardware without renting cloud GPUs
- Train diffusion models for image or video generation with reduced VRAM footprint
- Build reinforcement learning pipelines with lower memory overhead
- Integrate optimized training into data science workflows via transformers and peft
- Deploy locally trained models via OpenAI-compatible API or export to GGUF for inference

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Unsloth accelerates training and fine-tuning of large language models and diffusion models on consumer hardware, reducing memory usage and training time through optimized PyTorch integration.

Yes, if you are training or fine-tuning models and have a compatible GPU (NVIDIA, AMD, Intel, or macOS). The permissive license, active maintenance, low install friction, and zero known vulnerabilities make it a sound choice. The 30-package dependency stack is heavy but standard for ML work. Not necessary if you only run inference or work exclusively with cloud-based training.

## Install

pip install unsloth
uv add unsloth
poetry add unsloth

## Installing unsloth

Before you install: Low friction installation via wheel; active maintenance with recent releases. Depends on 30 runtime packages including torch, transformers, and bitsandbytes—a heavy stack typical of ML frameworks. Installation succeeds easily, but the dependency footprint is substantial.

License in practice: Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions, making it suitable for both open-source and proprietary projects.

Quickstart:

pip install unsloth

from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/model",
    load_in_4bit=True,
)

# Fine-tune with your dataset
trainer.train()

Requires a compatible GPU (NVIDIA CUDA, AMD ROCm, Intel, or macOS Metal) or CPU; Python 3.9–3.14; torch and transformers pre-installed or installed as dependencies.

Verify before relying:

- Whether the 2× faster training and 70% VRAM reduction claims apply to all model types or specific configurations only
- Compatibility matrix across NVIDIA, AMD, Intel, and macOS GPU backends in practice
- Whether the desktop app and PyPI package share the same core training engine or differ significantly

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 2.3M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

llm fine-tuning acceleration, model training memory optimization, faster pytorch training, lora quantization training, gpu efficient model training, model-training, gpu-acceleration, quantization

[View on SkillFed](https://skillfed.io/packages/unsloth) · [View on PyPI](https://pypi.org/project/unsloth/)