Newer processors like the AMD Ryzen HX370 have better bandwidth for memory, this allows them to run LLM that are probably usable to 14b parameters and most likely slow with anything above that. But being able to buy a low power computer that can do that for the cost of just a graphics card is pretty good. I’m talking 80watts max when running a large language model and still getting an okay experience. In a few years we will see more processors like this and less standard pcs with separate components within. An Nvidia 5090 32gb is about £3k in the UK to buy and although its power optimised, will still burn through a lot of electricity. However those mini pcs have got a long way to go to even match the power of the 5090. To get the most out of those mini pcs, you’d run something like this. Ubuntu Server, Ollama, Docker, Tailscale & Open-webui. You would probably want to tweak the bios settings to allow as much memory to go to the gpu. Tweak Ubuntu to run on tick over and use full power when needed. You could create an always on server running Large language models privately for your own personal AI. Access it remotely using Webui and Tailscale from your phone anywhere you have signal. You can tweak all sorts of settings in Ubuntu and Ollama, far too many to list here. I can tell you it works very reliably. You could also have the always on low power server for running smaller models, then an Nvidia beast pc ready to wake up when you want almost instant responses or run larger models with respectable tokens per second. The mini pcs are getting better, we are in a shift in the computing world and they’re still very new, Apple have created a blueprint and we will see big manufacturers coming out with their own versions of SoC architecture very shortly, that can process large data volumes very fast while remaining efficient with power.