I'm setting up a Frankenstein system at the moment. It's a Chinese DDR3 X99 motherboard with a 12 core Xeon v3, 32gb 1866MT/s ram, and a 1080 Ti.

I'm shoehorning it back in the Optiplex that donated the ram, so it's not ready to go at the moment, but when I had it running on top of the motherboard box as a test I ran the (9B?) gemma4:e4b-it-q4_K_M since it can fit entirely in the 11gb vram. It flew, more than 50tk/s. A model that small isn't useful for coding, but there could be uses. I'd love to figure out a Wake-on-Use and use it as my personal ChatGPT. I'm not sure how that would work... Maybe proxy the LLM thru a Pi with a script to Wake-on-LAN the PC? It'll be a fun weekend project someday.

My always-on LLM is the dense Gemma4:31b that's not quite half in GPU on a 12gb 2060. It's really slow, but the quality is great and my use case is an automated queue so I'm not sitting there watching the output. I have another 2060 but unfortunately the PC won't POST with both installed for some reason.

Speaking of llama and local compute, there was a tweet from Georgi Gerganov (llama.cpp author) a couple of days ago saying that he is currently using Qwen3.6 27B, running locally on a Mac M2 Ultra or RTX 5090, to assist with llama.cpp development.