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Motherboard sales are now collapsing amid unprecedented shortages fueled by AI
(www.tomshardware.com)
This is a most excellent place for technology news and articles.
Self hosting an llm ain't the same thing as self hosting nextcloud for your docs and calendar. Yes there are small models but their output is laughable
Small models are improving and becoming more capable. The quality of local LLMs is basically unbounded. The context size of local LLMs is bounded by hardware. So local LLMs can be very capable for small, self-contained tasks.
qwen 3.6 35b running locally:
Single shot. No tool/internet use, so it didn't pull this script from elsewhere.
Output:
I tried to keep the size and scope within something that would reasonably fit in a comment. Looks pretty decent to me, but I can't write Python myself. Never learned. I double-checked the LAT & LON of Miami, and it's spot on.
It did take 47 seconds, while a cloud LLM would probably take 5 or less.
All I'm saying is local LLM isn't garbage and it is getting better all the time.
That's interesting.
How much ram did it use while running?
If you used a GPU, how much does it cost in today's prices?
It's a MacBook Pro. 36GB of ram. I am sure Macs have some kind of gpu and I understand it somehow combines GPU ram with system ram, but I don't really know Mac hardware very well.
It's beefy for a laptop, but the desktop I built for myself several years ago had 32 GB of ram and a GTX 1660, so I'm guessing they are similar in capability. I gave that to my daughter, so I can't run a comparison right now.
EDIT: After doing just a bit of research, I've learned the unified memory architecture that Macs use, while not ideal for many purposes, is actually a big advantage for running larger inference models. So it's possible that this particular model wouldn't run at all on my Linux box or would run much slower because the full model wouldn't fit in the 6GB of VRAM and create a lot of memory thrashing.
Yup, you want memory accessible to the GPU for local AI. AMD Strix Point and Mac devices are popular options. CPU can run LLMs but very slowly. I've got 32 GB of RAM and 8 VRAM and it's borderline useless for models that don't fit in the VRAM.