You can say the same thing about a 24GB consumer card. Going from being able to run 13B llamas to 33B doesn't really help you in a commercial sense. This holds true, generally, for other LLM foundational models as well. To do commercial work you're going to need more RAM than consumer cards have. You need at least two if you're going to run the 70B and even then the 70B (and similar) aren't useful commercially. Except in the gathering money from investors who don't know better sense.

No one is arguing any of that. You're the one that brought up the 580 specifically.

By the way, still waiting for you to take me up on your 'bet'.

I was wrong. Sorry. Food trucks do accept cash most places.

Now it's your turn Mr. "You're not going to find rx580's with enough vram for AI. Typically 4-8gb." This is completely false. Rather than acknowledging that you then tried to move the goalposts (much like I did in that past thread saying, "Oh, but maybe it's just my region where they don't.") It looks like we both behave a bit silly when trying to save face when we're wrong.

> This is completely false.

It isn't completely false. You're doing super limited stuff as a hobbyist that barely works.

The parent article is entirely about running and benchmarking Llama2-7B/13B. This is the "super limited stuff as a hobbyist that barely works" and I've run them at entirely usable speeds on the AMD RX 580. Just admit that you're wrong, or at least that you didn't actually read the article.

"entirely usable" is not the same as "roi efficient"

> from your ignorant perspective

no need for the ad hominem.

Ignorance is not an insult. It just became obvious that you were talking about a different concept (commercial use with big models) than the article itself and everyone else were talking about (7B/13B models). So I generously assumed you just hadn't read it (ignorance). I guess now that you've ignored that and doubled down I can assume you were/are just arguing in bad faith.

is 70B not commercially useful because of the model storage requirements, or total inference performance, or additional memory per session that's inferencing, or what?

is the output better such that it's desirable, or is this just a case of "too much performance hit for a marginal gain"?

Home use is how you get employees that push your products at work. The lack of focus on home use is AMD's biggest ML weakness.

The lack of a place where you can hourly rent top of the line hardware from AMD is the biggest weakness. Nobody is going to buy and run a MI210/MI250 at home.

Having a community of people using your product has zero commercial value?

Do you even know how brand recognition works?

The amount of people swearing off of AMD because of bad drivers ten years ago easily cost them a billion dollars. More than the cost of developing a good driver.

> Having a community of people using your product has zero commercial value?

Not is not what I'm saying, I'm saying that if I buy up a bunch of rx580 cards, nobody is going to rent them from me.

Now, if I offered a bunch of MI250's on an hourly rate, you can absolutely bet people will rent them all.

i mean in AI specifically, you need your stuff to be usable by a small lab of prof/grad students, otherwise it will never get adoption.

usually at least some of the compute resources are "prosumer" workstations using commercial cards.

Agreed, AMD needs to get their high end cards into more schools. Short of that, they need a place where people can rent them by the hour (and give discounts to schools).