506
planned Proton replacement (jlai.lu)
submitted 1 week ago by to c/privacy@lemmy.ml
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๐Ÿ™‡ 1
 

I've cancelled my subscription yesterday, conveniently it was about to renew at the end of the week, so I have a few days to migrate from Proton, here's what I'm thinking of, but I'd love to hear what other people are switching to

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[โ€“] 1 point 1 week ago (3 children)

What does "lower-end" mean in this case? 8GB VRAM could be anything from a 2014 Radeon R9 290X to a 2026 RTX 5060.

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  • [โ€“] 1 point 1 week ago (2 children)

    Radeon RX 7600, but yes, you're right, it's relative. It's good enough for any gaming I do, which is not an awful lot. I think it's considered mid-range for its generation, I guess I was just thinking of the GPUs I see people mention they have which seem much beefier than mine lol.

    I'm no AI expert but when I looked into it, it seemed that VRAM was the biggest factor being mentioned in discussions of GPUs, hence why I mentioned it.

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  • [โ€“] 1 point 1 week ago (1 child)

    Iโ€™m no AI expert but when I looked into it, it seemed that VRAM was the biggest factor being mentioned in discussions of GPUs, hence why I mentioned it.

    I could be wrong, but I think there are differences not just in FLOPS but perhaps more importantly in what kinds of functions are supported (with newer GPUs becoming gradually more general-purpose), so I'm not sure if the GPU from 2014 is capable of running AI models at all (slowly or otherwise).

    I actually kinda want to find out, because my old GPU is a Vega 56 (which is also 8GB) and I'm not sure if it would be useful or not.

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  • [โ€“] 1 point 6 days ago

    I'm sure you're probably right. I haven't looked into how things like clock speed/FLOPS/etc affect LLM performance.

    The main thing is you need ROCm/CUDA/Vulkan support. If you do then just try running some models that fit into your VRAM and see how they perform? On my card, it takes maybe a minute or two to generate an answer, sometimes several minutes if the answer is long. I use small models that are <8GB in size, so not the best open-weight ones, but I don't use LLMs for anything particularly complex anyway so the dumb ones do the trick for me.

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