Unsloth
Verified Oct 2, 2026 ActiveOpen-source app and library for running, fine-tuning and reinforcement-learning open LLMs locally with far less GPU memory.
Listed on EffectHub ·
Pricing
- Pricing model
- 100% Free
The open-source app and library are free under Apache 2.0, and you supply your own hardware. The vendor's pricing page is blocked by a bot check, so any paid Pro or Enterprise tier could not be confirmed and no price is recorded.
Use tool ↗About Unsloth
Unsloth started as a Python library that speeds up LLM fine-tuning and cuts memory use, and now also ships a desktop app and a web UI called Studio. You can chat with local models, serve them through an OpenAI-compatible API, and train or fine-tune language, vision, speech, embedding and diffusion models with LoRA, QLoRA, full fine-tuning and reinforcement-learning methods such as GRPO. It runs on Windows, Linux and macOS, with NVIDIA, AMD, Intel and CPU support, and exports models to GGUF and other formats. The code is Apache 2.0 licensed and the company is a San Francisco start-up.
- API
- Yes
- Last verified
- Oct 2, 2026
Verified sources
- Open-source repository: desktop app, Studio web UI, fine-tuning, RL, GGUF export, Apache 2.0 licencegithub.com ↗
- Documentation describing running and training LLMs locally on macOS, Linux and Windowsunsloth.ai ↗
- Founded 2023 by Daniel Han and Michael Han, YC Summer 2024, San Franciscoycombinator.com ↗
Is Unsloth legit?
We don’t label tools safe or unsafe. Below are the records we checked ourselves, each with the date we read it.
- Last checked
- Oct 2, 2026The site answered
- On the market since
- 2023The vendor's own material, read by us
- Operated by
- Unsloth AI, United StatesOur research, card verified Oct 2, 2026
Pros & cons of Unsloth
The go-to open-source route for people who want to fine-tune or run open models on one machine without paying for managed training. It is a good fit if you are comfortable choosing a base model and a dataset yourself. If you want a hosted training service with a support contract, you will have to ask the vendor about paid tiers.
Pros
- Apache 2.0 licence with an active public repository
- Claims faster training with lower VRAM use than standard setups
- Desktop app, web UI and code library for different comfort levels
- Exports to GGUF and other deployment formats
Cons
- Speed and memory figures are the vendor's own claims
- Fast-moving project, so docs and features change often
- Paid tiers and support terms are not visible without passing the vendor's bot check
Best for
- Developers fine-tuning open models on a single consumer or workstation GPU
- Researchers running reinforcement-learning recipes such as GRPO
- Hobbyists who want a local chat UI plus an OpenAI-compatible endpoint
Not for
- Teams that need a fully managed cloud training service
- Anyone without a capable GPU or enough RAM for the models they want to run
Watch out: The official site sits behind a bot check that blocks plain fetches, so pricing and any paid-tier limits should be confirmed in a normal browser before you plan around them.
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