Which open-weight model can you actually run, and what are you legally allowed to do with it?

"Open" tells you one thing: you can download the file. It doesn't tell you whether your machine can hold it or whether your business may use it. Those are two separate checks, and they often point in opposite directions, because the biggest models need the most hardware and tend to carry the most conditions.

This page compares models as of November 2, 2026, using their model cards, licence files and Hugging Face file sizes. For the basics of open versus closed, see our licence explainer.

Three numbers to read on any card

Total parameters are all the learned numbers in the file, and they largely determine the memory you need. Active parameters are those used per token (a small piece of text) in a mixture-of-experts model, where only some sections do the work at each step. They largely determine the compute, and so the speed and running cost.

The third is bits per parameter, set by quantization, which stores numbers more compactly. A trillion parameters takes about 2 TB at 16 bits, 1 TB at 8 bits, and roughly 500 GB at 4 bits. That is why a card's parameter count never tells you memory on its own. Working memory for your context comes on top of the file size too, and a million-token session can add a lot.

Models one person can realistically run

These fit a single good computer, or one graphics card in the larger cases. Sizes come from the Ollama library or Hugging Face.

Note how friendly the licences are at this end. For most small projects, the question is quality on your tasks, not legal risk.

Models for one server

These need data-centre GPUs or very large unified memory. Sizes are file totals on Hugging Face, in the most compact official format listed.

Sparse designs show their value here. Beam's download is modest for 501 billion parameters because it comes in 4-bit, and few parameters are working on each token.

Models that need a cluster, or a provider

Muse Spark Open stands out as the only one here with a real desktop route. Even so, two top-end Mac Studios are a serious purchase.

For almost everyone, these models are something you rent per token. But having the weights still has value: if a provider changes its terms or vanishes, someone else can host the same model.

What you are legally allowed to do

This table summarizes the licence files as of this date. It isn't legal advice, and licences can change with each model, as they did for GLM and Kimi. Copyright, privacy and trade laws still apply whatever the licence says. In Canada, a business handling personal information still has its privacy duties, including under Québec's Law 25, wherever the model runs.

How do you choose in ten minutes?

Minute 1: where can your data go? If it may go to a provider, hosted access opens up every model above. If it must stay on your machines, your hardware sets the limit.

Minutes 2 to 3: what memory do you have? Under 16 GB, begin with Gemma 4 E2B or E4B. At 24 to 32 GB, try Qwen3.8-27B, Muse Glimmer or gpt-oss-20b. With one 80 GB data-centre GPU, gpt-oss-120b fits, and at 160 GB or more you can run Kolibri-1 or Super VL. Beyond that, plan a server.

Minutes 4 to 6: what will you do with it? For your own work, or a product with the model built in, most licences are fine. If you plan to sell access to the model itself, cross off Mistral Large 4 unless you are ready to talk to Mistral, and check thresholds on the others.

Minutes 7 to 8: do you need something specific? For French, test Mistral's models and Gemma's multilingual support. For German, try Kolibri. For audio, look at Gemma 4's smaller sizes or MiMo. For image input, avoid Beam, which handles only text. For full transparency about training data, look at Nemotron or Switzerland's public Apertus.

Minutes 9 to 10: test. Benchmark tables are mostly self-reported, and each lab picks the setup that suits it. Run five real tasks on your two best candidates through a host, score them, and only then buy hardware.