I’ve a minipc running an AMD 5700U where I host some services, including ollama and openwebui.

Unfortunately the support of rocm isn’t quite there yet and not to mention that of mobile GPUs.

Surprisingly the prompts work when configured to use the CPU, but the speed is just… well, not good.

So, what’d be a cheap and energy efficient setup to run sone kind of LLM for personal use, but still get decent speed?

I was thinking about getting an e-gpu case, but I’m not sure about how solid this would end up.

  • adONis@lemmy.worldOPEnglish
    0·
    6 months ago

    I have the exact same gpu and tried that. But couldn’t get ollama docker version (rocm) to work with the gpu. even changing the env variable to 10.30.1. (rocminfo reports gfx1031)

    would you mind giving some instructions or a link?

    • passepartout@feddit.deEnglish
      2·
      6 months ago

      If you’re lucky you just set it to the wrong version, mine uses 10.3.0 (see below).

      I tried running the docker container first as well but gave up since there are seperate versions for cuda and rocm which comes packaged with this as well and therefor gets unnecessary big.

      I am running it on Fedora natively. I installed it with the setup script from the top of the docs:

      curl -fsSL https://ollama.com/install.sh | sh

      After that i created a service file (also stated in the linked docs) so that it starts at boot time (so i can just boot my pc and forget it without needing to login).

      The crucial part for the GPU in question (RX 6700XT) was this line under the [service] section:

      Environment="HSA_OVERRIDE_GFX_VERSION=10.3.0"

      As you stated, this sets the environment variable for rocm. Also to be able to reach it from outside of localhost (for my server):

      Environment="OLLAMA_HOST=0.0.0.0"

      • adONis@lemmy.worldOPEnglish
        2·
        6 months ago

        oh man… I’m such a dumb dumb … didn’t even try 10.3.0, now I did and the docker version works and is extremely fast, compared to a CPU… Thank you so much.