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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Rules:
Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.
Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.
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>.
Rule 4 - No implying that models are devoid of purpose or potential for enriching peoples lives.
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OpenWebUI works with plain llama.cpp
16 is a bit small so try a MoE (e.g. QWEN 3.6 35BA3B) model and put experts on the CPU (although DDR4 may be underwhelming) which you can do with llama ( with offloading and drafting for T/s) but not ollama (spitting noise). Here's a good starting point. You'll likely get 60+T/s on say a 6 bit quant.
You can use a container approach, but llama.cpp is a bit of a moving target, with new cool features coming along regularly to support new models. I build it in a distrobox and running it is a simple call. When it doesn't want to build anymore because dependencies have changed too much, I just spin up a new distrobox and leave the old one there for older models. I find it a good balance between flexibility and ease of maintenance, and technically it's also a container approach. Take notes so you know how to set up the new one.