140
If open weight models are the future, U.S. AI companies are going to have a hard time
(www.fastcompany.com)
this post was submitted on 26 Jul 2026
140 points (98.6% liked)
Technology
86628 readers
3478 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related news or articles.
- Be excellent to each other!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, this includes using AI responses and summaries. To ask if your bot can be added please contact a mod.
- Check for duplicates before posting, duplicates may be removed
- Accounts 7 days and younger will have their posts automatically removed.
Approved Bots
founded 3 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
I just meant that you have to be cognizant of what went into the quantization.
As an example, a “Q4_K_M” could be too much quantization to be usable on one model, and an inefficient waste of space on the other. Two Q4_K_Ms of the exact same model could be completely different, one totally borked. Or one particular Q4_K_M could excel in one task, but be totally useless for another, even with the exact same settings, when a slightly different sized or type of quantization would excel.
It’s a deep rabbit hole. It’s not random either; there are distinct technical reasons behind every case mentioned above.
And that’s not even at the cutting edge quantization anymore, though what’s “cutting edge” completely depends on your particular hardware and use case.
I’m trying to make this sound daunting on purpose.
Many people have really horrible experience with a default “ollama run” for this exact reason, because the defaults are terrible and the customization is critical to getting coherent, performant output.
Unquantized LLMs, on the other hand, are basically always run the same way: vllm docker image on a big server, official weights. There’s less to “go wrong” trying to squeeze it on hardware with unofficial runtimes and compressors.