LocalLLaMA
Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
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Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.
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I'd not use ollama, it's basically just a fancy wrapper around lama.cpp.
There's also modules/docker containers to hot swap models with lama.cpp
My model hosting setup is: Lama.cpp -> Open web UI
Lama.cpp is running in a local shell on my Mac Mini, since setting up GPU support with metal is (or was?) a pain. And open web UI sits in a docker with a local storage mounted so it have persistence when updating or moving the docker.
16gigs vram however ain't too much, you'll be fairly limited to fairly low quants. It will be reasonably fast tho. If you can use most of your system ram you could go and host f.e. qwen 3.6 bf8(~56gb) or bf4 (~30gb). It would be slower but you also gain a lot of usability from that.
Or you host two models a smaller one on the GPU and bigger one with system ram so you can switch between "knowledge" and speed.
Using lama.cpp you'll have to take a look at huggingface & use gguf models.