this post was submitted on 21 Jun 2026
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For those who want to know more, rough setup:
We get 70-100tok/s generation. Four slots with 256k context length each.
We use a smaller Board with "only" 64GB of shared LPDDR5X. Bottleneck is memory speed, rocmfp4 quants help a lot.
As soon as I get my imatrix calibration right, I will publish the quantized versions.
Most existing quantized models are broken. The authors did some not supported stuff (like using a already quantized model and requantize it) that you may get issues with coherence or sudden Chinese words in the output.
That is not an issue with rocmfp4 but with vibe coders and agent psychosis.
Thank you so so much for pointing out ROCmFP4. I have been tinkering with my RDNA 3 framework on llama. I was struggling with ROCm llama.cpp and have been using vulcan in the meantime. I know there’s some issues on the llama.cpp github to try and fix my issue (UMA stuff), but haven’t come across this specific project. Gonna try it out
Do you have a walk through for setup?
I'm on the strix halo 128 gb variant and while I got ollama working fine, i haven't gotten any of these multi headed setups working
I am on Gentoo for it, but everything with a decent rocm should work.
Have a look for llama-swap, that handles multi head endpoints.
Also, as you are on a big board, you can quantize yourself, as the BF16 version of qwen has only 72gb.
I will try and post a full writeup next days. But feel free to dm me, if you need some guidance on quantize or more.
I am using this fork currently: https://github.com/charlie12345/ROCmFPX
Stuff happens fast currently, so may be worth to wait a week or two ig you need something super stable, but if you are up for experimenting, that's the way to go
THis is great, thanks. I'm on the z-13 and needed to use it for a work project, which is wrapping up soon. I'm planning on re-building it as a locally hosted agent support machine.
Great man! Gentoo lover and long time addicted here.... Keep it the good work!