Hacker News Reader

Obviously going to depend on your definition of "decent". My impression so far is that you will need between 90GB to 100GB of memory to run medium sized (31B dense or ~110B MoE) models with some quantization enabled.

View on HN

I’m running Gemma4 31B (Q8) on my 2 4090s (48GB) with no problem.

I have the same setup but tried paperclip ai with it and it seems to me that either i'm unable to setup it properly or multiply agents struggle with this setup. Especially as it seems that paperclip ai and opencode (used for connection) is blowing up the context to 20-30k

Any tips around your setup running this?

I use lmstudio with default settings and prioritization instead of split.

I asked AI for help setting it up. I use 128k context for 31B and 256k context for 26B4A. Ollama worked out of the box for me but I wanted more control with llama.cpp.

My command for llama-server:

llama-server -m /models/gemma-4-26B-A4B-it-UD-Q8_K_XL.gguf -ngl 99 -sm layer -ts 10,12 --jinja --flash-attn on --cont-batching -np 1 -c 262144 -b 4096 -ub 512 -ctk q8_0 -ctv q8_0 --host 0.0.0.0 --port 8080 --timeout 18000

Reply on news.ycombinator.com