AMD MI300X 30% higher performance than Nvidia H100, even with optimized stack

Tensorflow had first mover advantage but they also had first mover disadvantage. TF isn't that easy to write nor to debug. PyTorch got to see everything that was wrong with TF and fix it. No doubt Meta has put in lots of amazing effort. It is an incredible piece of software.

I'll also add that PyTorch does a lot of great stuff that people don't use or recognize. There are a lot of good distributional works out there for prob and stats people that give you cuda acceleration. Really a lot of numpy can be replaced with pytorch and its great. But be warned of FMA and other optimization differences.

It is ported (I think by AMD). I use it daily. Biggest problem is that the people maintaining PyTorch aren't very interested as they don't use it themselves. Hence there are silly bugs in it. Users contribute fixes and they get closed down as "unsupported" or get plain misunderstood.

The irony of those technologies is that they shine best on high end cards.

If you're on a low end card and say pushing the limit of a game and are upscaling 720p to 1080p, then dlss can't do miracles, there will be tons of defects due how little information about the scene the upscaler has. But if you're on a high end system trying to upscale 1440p to 4k then even not only is the upscaler less likely to make a mistake, it's also much less noticeable.

Same with framegen, on a low end system that's getting sub 60fps, the framegen not only adds noticeable input lag, but often makes noticeable defective frames in between, if your system was already pushing above 60 then not only is the input lag much more bearable, the possible defective frames stay on screen for so little that you'd have to be paying attention to it to notice them most of the time.

They're still useful for low end systems, they can prolong their lifespan quite a bit for current gen games, but as this tech becomes the norm, developers rely on them to hit performance targets and your card will be outdated just as fast as older cards used to.

You may not be running DLSS with a super high scaling factor but I almost guarantee DLSS or DLAA is going to be on for most games nowadays if you want to keep framerates stable on ultra.

You say this as if momentum isn't one of the most powerful forces in the universe. Many a better technology has gone extinct because a competitor simply had more momentum. It's difficult to catch up and it's difficult to stop.

Nvidia also is aware of this and not making mistakes that many others do by sitting back and relaxing. They are doing everything they can to not let AMD even begin to compete, but they are also very aware that hardware is far from the full package. 75% max, but probably less.

Alan Wake 2 will use FSR or DLSS for AA even at native resolutions, with no other AA option. I expect more games will do this in the future.

This is completely false.

Have you seen fully realtime path-traced Cyberpunk?

It's a pretty solid strategy, but naturally favors the current incumbent who has the $$$ to throw around.

If they would have split resources off of Zen to spend on CUDA they would have failed at both and AMD would be a bankrupt husk.

The thing AMD doesn't have is the software stack.

1) the user above me thinks ML is just matrix multiplies, which it is far from that.

2) It's more than hardware, and one team has (huge) momentum

3) You can have better tech and not win. It actually happens relatively frequently.

There may be nothing better for the ML space than AMD being a meaningful competitor. It will give all of us better tools. It'll make AMD better and it'll even make Nvidia better, because (healthy) competition is good for everyone. But let's not be naive in thinking it is just hardware and that one example is enough. It's a good sign, but a Tesla driving on the highway in nominal conditions isn't even close to fully self driving. It's still a year away as it's been for a decade.

So I do hope this ages poorly. But I'm not holding my breath because as AMD succeeding is one of the best things for ML, hype is the absolute worst and there's way too fucking much of it. And it it takes 5+ years to age, well I wouldn't say it aged poorly.

OP and OP’s OP never frame it about that. But you can still get the answer you are looking for from their answers.

... I'm not sure what you mean that you always use high-level language and packages... But it doesn't sound right.

The reason game developers use DX/VK/GL as opposed to cuda is entirely: A. You want access to non-nvidia customers B. You need access to more aspects of the hardware then is exposed to Cuda. Cuda provides a nice interface to Nvidia's compute. Almost no access to the entire rest of the fixed function pipeline.