Local Model Explorer

Local Model Explorer

Shows which GGUF quantized models fit your graphics card, Mac or CPU, for people who run language models on their own machine and want to pick a file that works.

Made by LocalLLaMA on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: Local Model Explorer is a small web page that helps you see which GGUF quantized models fit the hardware you own. It covers a GPU, a Mac or a plain CPU. The Space is built with static HTML and names a companion dataset, local-model-explorer-data, as its data.

Who it suits: People who run language models on their own computer and want to know which quantized file suits their machine before they download it.

Industry | Who uses it | How they use it | Example
Hobby AI | Home users of local models | Check which quants suit their graphics card | Checking a Mac before saving a large model file
Software | Developers adding a local model to an app | Compare quant options against the target machine | Picking a file that runs on a laptop CPU
Research | People who test open models | Look up which quants suit a single GPU | Choosing a quant for a lab workstation

If you only use hosted chat services and never run a model yourself, you can skip it.

Where to use it:

• A web browser, straight from the Space's page on Hugging Face
• Windows, macOS or Linux, by opening a downloaded copy of index.html in a browser

Use case | Who | What they get back
Pick a quant for a GPU | Local model users | A view of which GGUF quants fit that card
Pick a quant for a Mac | Mac owners | A view of which GGUF quants fit that Mac
Pick a quant for a CPU | People without a graphics card | A view of which GGUF quants fit a CPU

How to set it up: You can use the page online with nothing to install. A local copy needs a little more.

• A web browser
• Python and the pip tool, only if you want the files on your own computer

1. Install the Hugging Face hub package with pip.
2. Download the Space's files into a folder with the hf command.
3. Move into that folder.
4. Open index.html in a web browser.

pip install -U huggingface_hub
hf download LocalLLaMA/local-model-explorer --repo-type space --local-dir local-model-explorer
cd local-model-explorer

How to use it: Open the Space's page on Hugging Face and it runs in the browser. With a local copy, open index.html instead. Then look for the GGUF quants that fit your GPU, Mac or CPU. The download command pulls the whole Space into a folder.

hf download LocalLLaMA/local-model-explorer --repo-type space --local-dir local-model-explorer

Example: You own a Mac and want a quantized model that fits it. You open the page in your browser and check which GGUF quants suit a Mac. The page is built to show you the ones that fit, so you can download one that will load.

For newcomers and experts: GGUF is a file format for language models that run on your own computer, and a quant is a smaller version of a model that needs less memory. To start, open the Space's page in your browser and look at what it lists for your kind of computer.

Under the hood this is static HTML with a style sheet, so there is no server to run and nothing to log in to. The trade-off is that the page is only as good as the dataset it names, and the source does not say how that data is updated.

Open the page in a browser
Static HTML loads with its data
Check which GGUF quants fit your hardware
Pick a file that suits your machine

Good to know:

• License: MIT
• Cost to run: none named
• Data it reaches: a dataset on Hugging Face named local-model-explorer-data, and no account is named
• Not for: the source names no forbidden uses

On Hugging Face, 13 November 2026

• Likes: 34
• Built with: static
• License: mit

Links

• Space: https://huggingface.co/spaces/LocalLLaMA/local-model-explorer
• Files: https://huggingface.co/spaces/LocalLLaMA/local-model-explorer/tree/main

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.