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.

  • partial_accumen@lemmy.world
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    1 day ago

    Kind of like “compressed”. It takes longer/more effort to run them, to produce the same result as a the same model that has not been quantized, where that non-quantized version would consume significantly more RAM but produce the result faster. You would typically only run the quantized model when you’re starved for RAM, which most of us are running LLMs locally.

    Think like zipping a file with file compression. It takes less space, but has to be unzipped for you to have usable files again.