I usually don’t even understand the lingo they use. “Open-weighted” is the most recent one, then it usually goes down to specific “models” that everybody is supposed to know about.
These are my thoughts (I will stick to the vague “it” for now, but of course therein lies another question: “and how does all this apply to various specialised AIs”):
- Is it really feasible to run it 100% locally? I know there’s plenty of people with very powerful rigs indeed, but still. Or are 99% of these people really saying “it would, in theory, be possible to run that locally, therefore your concerns are invalid”?
- If yes to the previous: the software doesn’t come from nowhere and ultimately still relies on gas-turbine-powered datacenters and stolen IP and stolen personal data, no?
If what I wrote above is true, what exactly are people arguing when they say it’s still possible to use LLMs ethically or true to FOSS philosophy, because … ???
edit
Thanks to all who answered.
I guess it’s my fault for asking several questions in one, but this thread has attracted exactly the type of people I’m writing about; several even used the term “open-weighted models” without explaining it.
Asking to get arguments explained, I got more arguments instead.


Ok seeing your edit, some basic explanations:
A Token is a small unit used by LLMs. Mostly not a full word, more of a word fragment of a few characters.
A Dataset is an accumulation of different data (mostly real-life data to avoid problems) which is used to train a Model.
A Model is basically a huge file, full of Tokens that are interconnected with each other.
Open Weights means that the numbers which describe the relationship between Tokens (this is all basically a ton of matrix multiplication, which is the reason this is using so much power) are available.
If you want to really understand, 3Blue1Brown has a complete series about neural networks!