this post was submitted on 05 Nov 2025
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They can ALL be run on RAM, theoretically. I bought 128GB so I can run GLM 4.5 with the experts offloaded to CPU, with a custom trellis/K quant mix; but this is a 'personal use' tinkerer setup basically no one but hobbyists will touch.
Qwen Next is good at that because its very low active parameter.
...But they aren't actually deployed that way. They're basically always deployed on cloud GPU boxes that serve dozens/hundreds of people at once, in parallel.
AFAIK the only major model actually developed for CPU inference is one of the esoteric Gemma releases, aimed at mobile. And the bitnet experiments, which aren't very big so far.
(In case it's not obvious, this is my special interest, and I'm happy to ramble on about how to set up 'niche gaming rig hybrid models' for anyone interested).
I for one would enjoy triggering your unskippable cutscenes in setting up local CPU based AI if it can work on Linux with an older amd card.
Don't have funds for anything fancy, but would be interesting in playing around with it. Been wanting to get something like that setup for home assistant.
Plenty of folks do AMD. A popular homelabsetup is 32GB AMD MI50 GPUs, which are quite cheap on eBay. Even Intel is fine these days!
But what's your setup, precisely? CPU, RAM, and GPU.
I have a MI50/7900xtx gaming/ai setup at homr which in i use for learning and to test out different models. Happy to answer questions
You can use Vulkan fairly easily as long as you have 8G vram
https://blog.linux-ng.de/2025/09/27/running-llms-with-llama-cpp-using-vulkan/
The key is which model, and how.
For the really sparse MoEs, you might be better off trying ik_llama.cpp, especially if you are targeting a 'small' quant. But the dense Gemma models (as good as they are) are probably not the best choice for 8G RAM these days.
If you just want an easy way to setup AI on Windows or Linux, KoboldCPP is my recommendation for your backend. It supports the GGUF format, which allows you to use both RAM and VRAM simultaneously. It won't be the fastest thing, but it is easy enough to setup, with a bundled GUI for prep and actual usage. Through the IP address it gives, you can hook the backend into a frontend of choice.
KoboldCPP
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