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Unsloth AI ✓

Open-source desktop app for running and training models.

Source rating 4.3 (6) 3k views 308.6K/mo traffic 54 saves 2 comments Free
Agent AI Inference API
More categories (4)
Diffusion Image Generation Large Language Models Models
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Verdict

A quick read before you dig into the full page.

Best if
You are aI Developers and need: More accurate and advanced voice cloning
Skip if
Limited to language learning models
Pick this when
Beginner-friendly

Considering ZenMux instead? Compare side by side

Overview

Unsloth Desktop (Beta) is a free, open-source app for running and training AI models on your own local hardware.
Available for macOS, Windows, and Linux.
Unsloth lets you run, train and deploy LLMs, diffusion image/video, MLX, GGUF and audio models.
  • Train Model
  • Large Language Models
  • Llm (large Language Support) Integrated. Convert Your Lyrics To Descriptive Prompts For Images And Video. Export Then Directly To Your Favorite Plugin
  • Llm
  • ● Local-first: All Captured Information Is Stored Locally On Your Own Device, Ensuring Privacy And Control Over Your Data.
  • Ai Inference
  • Local Stable Diffusion (nvidia / Cuda, Also Cpu), Video Generations Provided By Online Service Providers
  • Text And Image To Image
  • Image Gen
  • Text To Video Generation
Unsloth AI website

Best for

Short scenarios where Unsloth AI tends to shine.

  • More accurate and advanced voice cloning

    Best for AI Developers

  • Customized models for personal chatbots, characters, personalities etc.

    Best for Machine Learning Engineers

  • RL for domain specific use-cases like law, medicine, finance

    Best for Data Scientists

Example outputs 1

Sample generations and before/after results for Unsloth AI.

Unsloth AI screenshot
Unsloth AI screenshot Unsloth AI website

Access & connections

Where you can use Unsloth AI, and what it plugs into.

Platforms

Web macOS Desktop

Integrations

MCP

API

Public API available

Browse other tools with an API

Capabilities

What this tool accepts, produces, and is strongest at.

Input

  • API
  • Code
  • Tabular
  • Image
  • Text
  • Video
  • Audio

Output

  • Code
  • Audio
  • Video
  • Tabular
  • API
  • Text
  • Image

Highlights

  • SOTA
  • Agents
  • API
  • MCP
  • Local
  • Open Source
  • No Signup

Supported features

  • Agents
  • API
  • MCP
  • Runs Locally
  • Open Source
  • No Signup
  • Fine-tuning

Models

Text

Qwen 3.8 Max LTX 2.3

Image

Qwen 3.8 Max MiniMax H3

Audio

MiniMax H3 FLUX 3

Video

Qwen 3.8 Max MiniMax H3

Pros & cons

Pros

  • Beginner-friendly
  • Open-source
  • Supports multiple LLMs
  • Speeds up training process
  • Manual derivation of mathematics
  • Handwriting GPU kernels
  • Compatibility with NVIDIA, AMD, Intel GPUs
  • Adaptable across multiple GPUs
Show 33 more
  • Supports Google Colab, Kaggle Notebooks
  • Free fine-tuning on NVIDIA GPU
  • Offers Freeware version
  • Pro, Enterprise variants available
  • Reduced VRAM usage
  • Performance improvement features
  • Focused on increasing inference speed
  • Used by industry leaders
  • Aims to overcome hardware challenges
  • Supported by large tech corporations
  • LLMs training more smartly
  • Improved speed in training
  • Handles compute-heavy maths steps
  • Works without altering hardware
  • No hardware change required
  • Allows custom kernel handwriting
  • Portable across different GPUs
  • Boosts GPU utilization efficiency
  • Cut down time of fine-tuning
  • Facilitates reinforcement learning training
  • Efficient with single GPU usage
  • Scales well across multiple GPUs
  • Offers variations for different needs
  • Extensive adaptability with GPUs
  • Reduces hardware resource use
  • Pro Version supports MultiGPU
  • Pro version uses less VRAM
  • Enterprise version has multi-node support
  • Enterprise version offers faster training
  • Enterprise version enhances accuracy
  • Offers 30x faster training
  • Reduces training time significantly
  • Ideal for fine-tuning LLMs

Cons

  • Limited to language learning models
  • Manual derivation of maths steps
  • May require GPU knowledge
  • Only free with one NVIDIA GPU
  • Reduced VRAM usage in paid version
  • Greener claim not substantiated
  • Inference speed improvement underworks
  • Limited to NVIDIA, AMD, Intel GPUs
Show 1 more
  • Requires sign up for news

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Releases 1

Initial release July 7, 2025 Current
Initial release of Unsloth AI.

In the news

Launch coverage and community mentions for Unsloth AI.

Imported reviews 2

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Robert Jul 10, 2025 v@ initial release

Amazing project

Unsloth AI Jul 8, 2025 v@ initial release

Hi there everyone! You might know us if you're familiar in the open-source space, but at Unsloth we do a lot of things. Mainly we help you fine-tune LLMs locally and help you run LLMs more efficiently and accurately.

All of this is 100% local and of course open-source. We DO NOT have paid plans currently and everything is free and open-source.

We also have a sub Reddit r/unsloth and very active Discord server so if you have any questions please do ask! Thank you! :)

FAQ 12

What is Unsloth AI?
Unsloth AI is an open-source tool specialized in the fine-tuning and reinforcement learning for various language learning models (LLMs). Its core functionality aims to simplify and expedite the training process of these models.
Can I use Unsloth AI even if I'm a beginner?
Absolutely. Unsloth AI has been designed specifically to be beginner-friendly. Its seamless interface and simplified processes make it accessible for beginners embarking on their AI training journey.
Which types of language learning models can I fine-tune using Unsloth AI?
Unsloth AI supports fine-tuning for a diverse range of language learning models. These include but are not limited to Llama 4, DeepSeek-R1, Qwen3, Gemma 3, and Mistral.
How does Unsloth AI manage to shorten the training process of LLMs?
Unsloth AI enables users to manually derive compute-heavy maths steps and handwrite GPU kernels. These advanced techniques substantially reduce the training timeline, making it significantly faster in comparison to traditional models.
Does Unsloth AI require any hardware alterations?
No, Unsloth AI does not require any hardware alterations. The process makes the training faster without necessitating any modifications to the existing hardware setup.
What GPUs are compatible with the Unsloth AI system?
Unsloth AI system is compatible with a wide range of GPUs, including but not limited to NVIDIA GPUs from Tesla T4 to H100. Furthermore, it is portable to AMD and Intel GPUs, enhancing its adaptability and usage scope.
Can I fine-tune LLMs using a single NVIDIA GPU on platforms like Google Colab or Kaggle Notebooks?
Yes, you can indeed fine-tune LLMs using a single NVIDIA GPU on widely used platforms such as Google Colab and Kaggle Notebooks. Unsloth AI offers free access to this facility.
What's the difference between the freeware, Pro, and Enterprise versions of Unsloth?
The free version of Unsloth offers standard fine-tuning capabilities. The Pro version, on the other hand, offers faster training and lesser VRAM usage. The Enterprise variant offers the fastest training, multi-node support, along with a significant increase in accuracy.
How does the Unsloth AI Pro version save on VRAM usage?
The Pro version of Unsloth AI has been designed to maximize efficiency. It offers a 20% reduction in VRAM usage, thus saving resources while ensuring effective training.
How can Unsloth AI help in overcoming hardware cost increase and performance stagnation?
Unsloth AI aims to counter hardware cost increase and performance stagnation by optimizing how AI training and LLMs operate. By delivering training methods that are significantly faster and smarter, it enables users to overcome the challenges posed by increased hardware costs and stagnating performance.
How does Unsloth AI plan on improving inference speed?
Unsloth AI is steadily working on improving inference speed to enhance overall performance. However, the specific strategies involved in this endeavor aren't explicitly specified.
Who are the notable users of Unsloth AI?
Unsloth AI is utilized by several big names in various industries. Notable users include Microsoft, Nvidia, Facebook, NASA, among others.
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