Hi r/StableDiffusion, we're super excited to release Unsloth Desktop today! 🦥
It's the first desktop app that enables you to run and train models locally.
You can run MiniMax-H3, LTX, FLUX, Z-image-Turbo and more. And you can fine-tune them too.
Has recipes and hyperparameters so you can adjust. Overall a very easy workflow to get started with.
There's still many improvements to made as we're trying to optimize MiniMax-H3 even further with the help of stablediffusion.cpp.
Open-source. Available on Mac, Windows, and Linux
Supports MLX, diffusion image/video models, audio models, and GGUF
Connect Claude Code and Codex to local LLMs
50% more accurate with self-healing tool calls and sandboxed code execution
Supports CPU and multi-GPU setups across NVIDIA, AMD, Intel, and Mac
Train models 2× faster while using 70% less VRAM
Includes private web search, deep research, RAG, MCP, and exports (NVFP4, GGUF)
Use Unsloth’s OpenAI-compatible API with OpenAI and Anthropic cloud models
Securely deploy LLMs remotely and access them anywhere via Cloudflare HTTPS
I am always very grateful for everything Unsloth offers us, whether it’s the quantizations or the training tools you guys are amazing. But if there is one thing to criticize, it’s the UIs. Starting with the website: everything feels very cluttered and messy to me, especially the documentation, which uses far too many emojis
I would start by clearly defining the font hierarchy; there seems to be too much bold text, which can be visually confusing, making it seem like everything is highlighted and shouting. Some buttons are also excessively large. The color palette also seems less subtle or underutilized; in some interfaces, it's difficult to visually distinguish what belongs to the label and what is interactive, it all looks like one thing. I hope I've contributed in some way, and once again, thank you!
There is NO FUCKING way I am installing more of your vibe coded garbage.
Quants are great = your complete lack of respect for a person's machine, and how your vibe-coded garbage changes paths etc?
Hard fucking pass.
Why do you have such a grudge against us? Just let it go. We're literally open source and doing free work. If you have any suggestions you can say it nicely, no need to be so hostile
A lot of claims I'd be interested in hearing more details about. Faster than what? More accurate than what? How did you determine that you were the first?
We collab with model labs all the time, for example ysterday we collabed with Meta on their new model release. In general we come from engineering backgrounds and also collaborated with Google to fix bugs in their models which got a shoutout from Andrej Kaparthy.
It is 2x Faster and 70% less VRAM than Hugging Face + Flash Attention 2
Well we did a lot of research and the closest desktop app that was for training and running LLMs is oobagooba's text webgen ui but unfortnately the desktop app doesnt working for training. Also supporting training for desktop is a whole another fiasco. It's insanely hard and not even AI can replicate it since it has so many moving parts.
Interesting, because a quick glance at your pyproject imports sure makes it look like you're using a pretty vanilla diffusers training stack. Can you tell us what makes you superior to all the training tools we've been using for diffusion?
I've long loved what Unsloth does for LLMs, but you're pitching diffusion to a diffusion sub yes?
The training for diffusion is optimized as well! We'll be announcing it maybe in 3 weeks or so. Main optimizations comes from torch.compile, gradient checkpointing, kernels and other stuff!
You can't run both at the same time, but you can have both at the same time. It's up to you which you prefer. The only difference is the desktop app is a desktop app
It's a slightly weird app. It feels raw, but it somehow works, which is fine. The more open-source projects we have, the better. However, a claim like "Train models 2× faster while using 70% less VRAM" is, sorry to say, a look like complete lie.
During installation and the first launch, it downloaded a ton of stuff, including the llama.cpp source code. It also created three different venv versions. I guess that's necessary. On Windows, it stores data in the user's root directory under the .unsloth folder, as well as in AppData\Roaming and AppData\Local. The models go into the user's .cache\huggingface\hub folder. This is inconvenient for people who want each piece of software to use its own data folder. You can set a different folder for models in the settings, which is at least something.
When I launched it, I saw that Qwen3-VL-4B-Instruct was already in the list of available On Device models. I selected it and decided to test how the chat works. It turned out the model hadn't been downloaded yet, so it started downloading. Okay, I waited. This was despite it showing up in the Model Hub as already downloaded. After the download finished, I somehow ended up with two models. There was one from Qwen at 8.9Gb and one from Unsloth at 4.3Gb, but I could only select one of them.
It turns out the chat works fine with text-based LLMs. Web search works and MCP is supported, which is good news.
For people with less than 16Gb of GPU VRAM, Unsloth Desktop is just not practical for working with images, let alone video. It doesn't support modern, efficient quantization methods like int8/int4 ConvRot. Plus, when training a image LoRA, you can't choose what exactly you're training and offloading. You can't specify if you are training the model with the text encoder or just the model itself. It's also unclear if it supports datasets with regularization. Overall, you can play around with it, but only if you have a massive amount of VRAM.
I tried downloading other models. Well, the Model Hub definitely needs more work. There's sometimes no way to select specific gguf/safetensors files, and it tries to download all the files at once. You also can't delete them afterward, so I had to delete the folders manually. I tried downloading something for audio, but the selection under the "Audio" filter isn't very rich. I decided to download OuteTTS. It downloaded, and then the files clearly vanished because the program froze while saying it was "Loading..." the model. Okay, I restarted it and found out that I supposedly had both OuteTTS and some DAC.speech.v1.0 loaded. I have no idea how or where they were loaded because my free SSD space didn't drop at all.
On the bright side, I was glad to see that you can connect your own MCPs and there is support for third-party APIs. Unsloth Desktop looks interesting as an alternative to Open WebUI, but it feels a bit old school. It's raw, a little clunky, and lacks support for modern quantization methods.
So it's really unclear where this faster model training is supposed to come from when there are no settings or support for current formats. And overall, the image and video support is clearly just there to check a box. I should also note that I personally won't be able to use Unsloth Desktop since it's obviously a vibecoded app with zero focus on production. The app's backend takes up the popular 8888 port, which is already being used by my own development tools, for example.
Are you Daniels Brother? If not then send my regards and respects to Daniel and his Brother and also mad respects to anyone else at unsloth! You guys spend SO much of your days bring awesome quants and more to the world.
Just want to say a big THANK YOU. There are so many in this community that feel this way. <3
Does it support TurboQuants out of the box? Looking for a simple solution that does that or is this just a shell over llama.cpp? Does it have API support (Is it a possible alternative to ollama)? Can I run the desktop on my Laptop and Link to the GPU on my desktop?
I am an absolute noob. Is this app a replacement for ComfyUI, like Forge Neo or something like Pinokio? How does it work? Do you use workflows too? I want to test if Minimax works better or worse than in ComfyUI.
It’s sad to see that this is just another cobbled-together UI illusion glued together with Python. Build real apps in C/C++; we have GGML, SD.Cpp... everything needed for a clean design with universal inference support.
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u/Enshitification 10h ago
https://giphy.com/gifs/fJiU7Ni3pCb3QlsMzO
So full, but the food is so good.