I ran 130,000 RX470-580 cards, so I know them quite well. Those cards aren't going to do anything useful with AI/ML. That technology is just too old and things are moving too quickly. It isn't just the card, but the mobo, disks, ram, networking...

RX570 is going to do ML faster than a typical desktop CPU. That's all you need for the person who has one to want to use it for Llama or Stable Diffusion, and then want to improve the software for the thing they're now using.

Not everything is a huge datacenter.

What is nonsense is that you think that AMD should dedicate limited resources to supporting a 6 year old card with only 4-8gb of ram (the ones I ran had 8).

I didn't say they are bad cards... they are just outdated at this point.

If you really want to put your words to action... let me know. I'll put you in touch with someone to buy 130,000 of these cards, and you can sell them to every college kid out there... until then, I wouldn't hold AMD over the coals for not wanting to put effort into something like that when they are already lagging behind on their AI efforts as it is. I'd personally rather see them catch up a bit first.

8GB is enough for Stable Diffusion or Llama 13B q4. They're outdated, but non-outdated GPUs are still expensive, so they're all many people can afford.

> I'll put you in touch with someone to buy 130,000 of these cards, and you can sell them to every college kid out there...

Just sell them on eBay? They still go for $50-$100 each, so you're sitting on several million dollars worth of GPUs.

> I'd personally rather see them catch up a bit first.

Growing the community is how you catch up. That doesn't happen if people can't afford the only GPUs you support.

> Growing the community is how you catch up.

Agreed 100%.

> That doesn't happen if people can't afford the only GPUs you support.

On this part, we are going to have to agree to disagree. I feel like being able to at least affordably rent time on the high end GPUs is another alternative to buying them. As I mentioned above, that is something I'm actively working on.

> I feel like being able to at least affordably rent time on the high end GPUs is another alternative to buying them.

There are two problems with this.

The first is high demand. GPU time on a lot of cloud providers is sold out.

The second is that this costs money at all, vs. using the GPU you already have. "Need for credit card" is a barrier to hobbyists and you want hobbyists, because they become contributors or get introduced to the technology and then go on to buy one of your more expensive GPUs.

You want the barrier to adoption to be level with the ground.

> The first is high demand. GPU time on a lot of cloud providers is sold out.

Something I'm trying to help with. =) Of course, I'm sure I'll be sold out too, or at least I hope so, cause that means buying more GPUs! But at least I'm actively putting my own time/energy toward this goal.

> The second is that this costs money at all, vs. using the GPU you already have.

As much as I'd love to believe in some utopia that there is a world where every single GPU can be used for science, I don't think we are ever going to get there. AMD, while large, isn't an infinite resource company. We're talking about a speciality level of engineering too.

> You want the barrier to adoption to be level with the ground.

100% agreed, it is a good goal, but that's a much larger problem than just AMD supporting their 6-7 year old cards.

That's fine for corporate customers, but how do you expect kids and hobbyists to learn the basics without spending thousands on an A6000 or something?

I believe strongly in "where there is a will, there is a way."

Those kids and hobbyists can't even rent time on high end AMD hardware today. I see that as one piece of the puzzle that I'm personally dedicating my time/resources to resolving.

The older cards are going to be supported by Mesa and RustiCL for the foreseeable future. Rocm is not the only game in town, far from it.

Thing is, for there to be a realistic alternative to CUDA, something needs to become "the only game in town" because people definitely won't add support for ROCm and Mesa and RustiCL and something else; getting support for one non-CUDA thing already is proving to be too difficult, so if the alternatives are fragmented, that makes the situation even worse.

RustiCL is just an OpenCL implementation. You can't have one-size-fits-all because hardware-specific stuff varies a lot across generations. (Which is also why you have newer versions of e.g. Rocm dropping support for older hardware.) The best you can do is have baseline support + extensions, which is the Vulkan approach.

ROCm works on RX570 ("gfx803", including RX470+RX580 too).

Support was dropped upstream, but only because AMD no longer regularly test it. The code is still there and downstream distributors (like if you just apt-get install libamdhip64-5 && pip3 install torch) usually flip it enabled again.

https://salsa.debian.org/rocm-team/community/team-project/-/...

There is a scary red square in the table, but in my experience it worked completely fine for Stable Diffusion.