Introducing Unsloth Studio | Unsloth Documentation

Today, we’re launching Unsloth Studio (Beta): an open-source, no-code web UI for training, running and exporting open models in one unified local interface.

QuickstartFeaturesGithub

• Run GGUF and safetensor models locally on Mac, Windows, Linux.

• Train 500+ models 2x faster with 70% less VRAM (no accuracy loss)

• Run and train text, vision, TTS audio, embedding models

• MacOS and CPU work for Chat GGUF inference and Data Recipes. MLX training coming soon.

Execute code + heal Tool calling

Unsloth Studio lets LLMs run Bash and Python, not just JavaScript. It also sandboxes programs like Claude Artifacts so models can test code, generate files, and verify answers with real computation.

E.g. Qwen3.5-4B searched 20+ websites and cited sources, with web search happening inside its thinking trace.

Unsloth as an API endpoint

You can now use local LLMs via tools like Claude Code and Codex by connecting it to Unsloth's API endpoint. This means you'll be able to directly run Qwen and Gemma models in those tools with Unsloth's inference which includes features like self-healing tool-calling, websearch etc.

Upload PDF, CSV, JSON docs, or YAML configs and start training instantly on NVIDIA. Unsloth’s kernels optimize LoRA, FP8, FFT, PT across 500+ text, vision, TTS/audio and embedding models.

Fine-tune the latest LLMs like Qwen3.5 and NVIDIA Nemotron 3. Multi-GPU works automatically, with a new version coming.

Data Recipes transforms your docs into useable / synthetic datasets via graph-node workflow. Upload unstructured or structured files like PDFs, CSV and JSON. Unsloth Data Recipes, powered by NVIDIA Nemo Data Designer, auto turns documents into your desired formats.

Gain complete visibility into and control over your training runs. Track training loss, gradient norms, and GPU utilization in real time, and customize to your liking.

You can even view the training progress on other devices like your phone.

Export any model, including your fine-tuned models, to safetensors, or GGUF for use with llama.cpp, vLLM, Ollama, LM Studio, and more.

Stores your training history, so you can revisit runs, export again and experiment.

Chat with and compare 2 different models, such as a base model and a fine-tuned one, to see how their outputs differ.

Just load your first GGUF/model, then the second, and voilà! Inference will firstly load for one model, then the second one.

Unsloth Studio can be used 100% offline and locally on your computer. Its token-based authentication, including encrypted password and JWT access / refresh flows keeps your data secure.

You can use pre-exisiting / old models or GGUFs that previously downloaded from HF etc. Read instructions here.

Unsloth Studio works on Windows, Linux, WSL and MacOS (chat only currently).

• CPU: Unsloth still works without a GPU, but only for Chat inference and Data Recipes.

• Training: Works on NVIDIA: RTX 30, 40, 50, Blackwell, DGX Spark/Station etc. + Intel GPUs

• Mac: Like CPU - Chat and Data Recipes only works for now. MLX training coming very soon.

• AMD: Chat works. Train with Unsloth Core. Studio support is coming soon.

• Coming soon: Training support for Apple MLX and AMD.

• Multi-GPU: Works already, with a major upgrade on the way.

Use the same install commands below to update:

Use our official Docker image: unsloth/unsloth which currently works for Windows, WSL and Linux. MacOS support coming soon.

For more details about install and uninstallation please visit the Unsloth Studio Install section.

Google Colab notebook

We’ve created a free Google Colab notebook so you can explore all of Unsloth’s features on Colab’s T4 GPUs. You can train and run most models up to 22B parameters, and switch to a larger GPU for bigger models. Just Click 'Run all' and the UI should pop up after installation.

Once installation is complete, scroll to Start Unsloth Studio and click Open Unsloth Studio in the white box shown on the left:

Scroll further down, to see the actual UI.

Here is a usual workflow of Unsloth Studio to get you started:

2. Load a model from local files or a supported integration.

3. Import training data from PDFs, CSVs, or JSONL files, or build a dataset from scratch.

5. Start training with recommended presets or customize the config yourself.

6. Chat with the trained model and compare its outputs against the base model.

You can read our individual deep dives into each section of Unsloth Studio:

Does Unsloth collect or store data? Unsloth does not collect usage telemetry. Unsloth only collects the minimal hardware information required for compatibility, such as GPU type and device (e.g. Mac). Unsloth Studio runs 100% offline and locally.

How do I use an old / exisiting model that I downloaded previously from Hugging Face? Yes, you can use pre-exisiting/old models or GGUFs that you previously downloaded from Hugging Face etc. They should be now be automatically detected by Unsloth otherwise read our instructions here.

Why is inference sometimes slower in Unsloth? Unsloth, like other local inference apps, are powered by llama.cpp, so speeds should be mostly the same. Sometimes Unsloth might be because you turned on web-search, code execution, self-healing tool-calling on. All these features may make your inference slower. If the speed difference is still slower with all features turned off, please make a GitHub issue!

Does Unsloth Studio support OpenAI-compatible APIs? Yes, see our API endpoint guide here.

Is Unsloth now licensed under AGPL-3.0? Unsloth uses a dual-licensing model of Apache 2.0 and AGPL-3.0. The core Unsloth package remains licensed under Apache 2.0, while certain optional components, such as the Unsloth Studio UI are licensed AGPL-3.0.

This structure helps support ongoing Unsloth development while keeping the project open source and enabling the broader ecosystem to continue growing.

Does Studio only support LLMs? No. Studio supports a range of supported transformers compatible model families, including text, multimodal models, text-to-speech, audio, embeddings, and BERT-style models.

Can I use my own training config? Yes. Import a YAML config and Studio will pre-fill the relevant settings.

How can I adjust my context length? Context length adjustment is no longer necessary with llama.cpp’s smart auto context, which uses only the context you need without loading anything extra. However, soon we will still add the feature incase you want to use it.

Do you need to train models to use the UI? No, you can just download any GGUF or model without fine-tuning any model.

We're working hard to make open-source AI as accessible as possible. Coming next for Unsloth and Unsloth Studio, we're releasing official support for: multi-GPU, Apple Silicon/MLX and AMD. Reminder this is the BETA version of Unsloth Studio so expect a lot of announcements and improvements in the coming weeks. We’re also working closely with NVIDIA on multi-GPU support to deliver the best and simplest experience possible.

A huge thank you to NVIDIA and Hugging Face for being part of our launch. Also thanks to all of our early beta testers for Unsloth Studio, we truly appreciate your time and feedback. We’d also like to thank llama.cpp, PyTorch and open model labs for providing the infrastructure that made Unsloth Studio possible.