The short version: Hugging Face, Ollama and LM Studio are the best places to download free AI models in 2026, and OpenAI's gpt-oss and Mistral's open-weight models are good open source LLMs to start with. The smaller gpt-oss model runs with 16 GB of memory, but every model has its own licence, and some come with conditions you need to read.
Running an AI model on your own computer used to be a job for developers. Now you can download a model, open an app and chat with it offline, which is handy if you work with client data you'd rather not send to a cloud service. The trick is knowing where to get the models and what you're allowed to do with them.
Five places to get free AI models
In no particular order, each one best for a different job:
- Hugging Face: best for the widest range of models, from chat to image and speech.
- Ollama: best for downloading and running a model with one command.
- LM Studio: best if you'd rather click than type commands.
- OpenAI gpt-oss: best for an open-weight reasoning model under a permissive licence.
- Mistral AI: best for open-weight models released under Apache 2.0.
Free AI model sources side by side
| Source | Best for | Worth knowing |
|---|---|---|
| Hugging Face | Finding any model | Licence sits in each model's card, so check it |
| Ollama | One-command setup | Library includes Llama, Gemma, Qwen, DeepSeek-R1 and Mistral |
| LM Studio | No-code local AI | Runs models such as gpt-oss, Llama and Qwen locally |
| gpt-oss | Reasoning on your own kit | Apache 2.0, the 20b model runs with 16 GB of memory |
| Mistral | Permissive licences | Lists several open-weight models under Apache 2.0 |
If you're new to this, start with LM Studio or Ollama and a small model. You can always move to a bigger one once you know your computer can handle it.
How each source works
Hugging Face describes its Model Hub as the place where its community hosts model checkpoints for storage, discovery and sharing. You can download models with its huggingface_hub library or with Transformers, and it says over 15 libraries are integrated. Ollama's library lists families such as Meta's Llama 3.1 in 8B, 70B and 405B sizes, Llama 3.2 at 1B and 3B, Qwen3, DeepSeek-R1, Google's Gemma models and Mistral's 7B model.
LM Studio's docs say you can download and run local LLMs such as gpt-oss, Llama and Qwen, chat with documents, and use a local API that works like OpenAI's. OpenAI says its gpt-oss-120b runs on a single 80 GB GPU, while gpt-oss-20b can run on edge devices with just 16 GB of memory, both under the Apache 2.0 licence. Mistral lists open-weight models such as Mistral Small 4 and Mistral Large 3 under Apache 2.0.
The licence rules that differ by model
This is the part most tutorials skip. Free to download doesn't mean free to use however you like. Hugging Face says licences are set in each repository's card and asks you to seek out and respect a project's licence before using it.
Apache 2.0, used by gpt-oss and many Mistral models, is one of the most permissive. Meta's Llama 3.1 Community License is different: if you share a product or service that contains Llama, you must provide a copy of the licence and prominently display Built with Llama on a related website, interface, blog post, about page or product documentation. It also says that if your products had more than 700 million monthly active users when that version was released, you must request a separate licence from Meta. Google's Gemma models come with their own Gemma Terms of Use, which cover how you use, modify and distribute them, including offering them as a hosted service.
For a small business running a model internally, most of these are fine. If you plan to build a model into something your customers use, read the licence first and keep a note of which model and version you chose.
Choosing a model size
Model names often include a number followed by B, such as 8B or 70B, which describes the model's size. Bigger models usually give better answers but need much more memory. OpenAI's own figures show the gap: gpt-oss-20b runs with 16 GB of memory, while gpt-oss-120b needs a single 80 GB GPU. Most laptops will be happiest with the smaller end of the range.
How to run your first free AI model
- Check how much memory your computer has.
- Install LM Studio or Ollama from its official site.
- Pick a small model from the library to test with.
- Read the model's licence on its card or terms page.
- Download it and ask it a few questions from your real work.
- Move to a larger model only if the small one can't keep up.
Getting more from local AI
If you want a step-by-step walkthrough, my guide on how to use Ollama covers installing and running models, and what Hugging Face is explains the hub in plain English. If the terminology is new, start with what a large language model is.
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Local models are slower than the big cloud chatbots on most laptops, and they won't always match them on complex tasks. Where they shine is privacy, working offline and testing ideas without worrying about usage limits. For drafting, summarising and sorting your own documents, a small model is often enough.
My verdict: start with LM Studio and gpt-oss-20b if your computer has the memory, use Hugging Face to explore, and read every licence before a model goes anywhere near a customer.
Run a free AI model library that belongs on this list? See how to get it featured.
Frequently asked questions
Where can I download free AI models?
Hugging Face, Ollama and LM Studio are the main places to find and download free AI models, including open source LLMs.
Can I run an AI model on my own computer?
Yes. OpenAI says gpt-oss-20b can run on devices with 16 GB of memory, and LM Studio and Ollama let you run models locally.
Which open AI models use the Apache 2.0 licence?
OpenAI's gpt-oss models are under Apache 2.0, and Mistral lists several open-weight models under Apache 2.0 too.
Do I need to credit Llama if I use it?
Meta's Llama 3.1 Community License says you must prominently display Built with Llama when you share a product or service that contains it.
Where do I find a Hugging Face model's licence?
Hugging Face says the licence is set in the model repository's card, in the card's metadata section.
