[woctordho]

There's nothing wrong to run CUDA on non-Nvidia hardware. CUDA has an interface that is reasonably well-designed, well-documented/reverse-engineered, and battle-tested for decades. What we need is not to invent another interface just under the name of 'open standard', but to implement the same interface. ROCm is exactly doing this, and so are other hardware SDKs such as MooreThread and Alibaba T-Head.

[superkuh]

The difference between ROCm and CUDA is that when a consumer GPU is released by nvidia it's supported for CUDA for about a decade (1xxx series cards just dropped last year). When a consumer GPU is released by AMD it's not supported by ROCm till about a year after release and then it's supported for about 3-4 years. With the RX 580 there were only 3.7 years after release before ROCm support was pulled. I bought mine a couple years after release and so only had about a year and a half of ROCm. Never again.

Things might be different in enterprise but for consumer AMD GPU ROCm is a trap. It is a mayfly. Sure, you can try to run the cards unsupported but you're just multiplying the difficulty and maintainence burden. And nothing will just work.

[michaelt]

I was in graduate school for robotics when CUDA came out, and the consumer card support was critical to nvidia getting to where they are today.

High Performance Computing option A wants to set up a call with someone with the authority to spend the best part of six figures, which could maybe be part of a funding application within a year or two, if there's a strong enough case for it.

High Performance Computing option B recommends you put in an application for time at the national centre that doles out access in 15 minute increments after you outline your entire project to them.

Then along comes nvidia, with CUDA - they want a one-off payment of $100, and on the day CUDA came out, almost every CS department already had a few dozen of the cards in computers they already owned.

No huge outlay, no ongoing spending commitment, no permission or application process.

[chorizo]

Not to mention Nvidia loans out their engineers to academic computational software teams to help build CUDA ports.

[3abiton]

Another crucial difference is: ROCm is open source, while cuda isn't. Yes it's tough to port things to newer gpus, but in theory people did it (therock spear heading the ROCm patch for strix halo before the offical support)

[LogicFailsMe]

Someone needs to stand up a benchmark suite for ROCM, this, and everyone else attempting it to really get the ball rolling here. SemiAnalysis could have a blast with this.

[zdw]

A YouTuber automated ROCm version testing for Strix Halo/R9700 + LLM workloads:

https://kyuz0.github.io/amd-strix-halo-toolboxes/

https://kyuz0.github.io/amd-r9700-ai-toolboxes/

[tekacs]

So is Spectral, which is mentioned in the headline of the article! As it says there:

> SCALE delivers nearly a 6x performance boost on AMD GPUs compared to using HIPIFY to convert CUDA code to AMD’s own ROCm environment

... whilst also running CUDA.

[rfv6723]

That sounds nice on paper, but you’re assuming Nvidia wants to play fair. Nvidia is never going to share future microarchitecture secrets, so the moment they drop a new chip and update the compiler, everyone playing the compatibility game has to start from scratch.