I have currently a RX 6700XT and I’m quite happy with it when it comes to gaming and regular desktop usage, but was recently doing some local ML stuff and was just made aware of huge gap NVIDIA has over AMD in that space.

But yeah, going back to NVIDIA (I used to run 1080) after going AMD… seems kinda dirty for me ;-; Was very happy to move to AMD and be finally be free from the walled garden.

I thought at first to just buy a second GPU and still use my 6700XT for gaming and just use NVIDIA for ML, but unfortunately my motherboard doesn’t have 2 PCIe slots I could use for GPUs, so I need to choose. I would be able to buy used RTX 3090 for a fair price, since I don’t want to go for current gen, because of the current pricing.

So my question is how is NVIDIA nowadays? I specifically mean Wayland compatibility, since I just recently switched and would suck to go back to Xorg. Other than that, are there any hurdles, issues, annoyances, or is it smooth and seamless nowadays? Would you upgrade in my case?

EDIT: Forgot to mention, I’m currently using GNOME on Arch(btw), since that might be relevant

  • Kanedias
    9•9 months ago

    Same here, but it turned out a lot of frameworks like tensorflow or pytorch do support AMD ROCm framework. I managed to run most models just by installing a rocm version of these dependencies instead of the default one.

    • @GlowHuddy@lemmy.worldOP
      13•9 months ago

      Yeah, I’m currently using that one, and I would happily stick with it, but it seems just AMD hardware isn’t up to par with Nvidia when it comes to ML

      Just take a look at the benchmarks for stable diffusion:

      • @AnUnusualRelic@lemmy.worldEnglish
        2•9 months ago

        Aren’t those things written specifically for nVidia hardware? (I used to e a developer, but this is not at all my area of expertise)