I recently sat and thought about it, and I made the bold claim to a friend that the development of CUDA over the last 15 years.. just that activity, as a whole, is one of the largest use of human intelligence to ever be focused on any one thing in history. It's less people than worked on getting to the Moon, but if you measure the project in PhD-person-years it is probably off the charts. And if you include all the engineers and scientists that the program affects in the same way you would have included all the subcontractors for the Apollo program, it is way, way bigger.

I think the only reason CUDA isn't talked about like the monumentally important human milestone in technological development that it is, is that it is a pretty abstract thing that is difficult for laypeople to visualize.

This is an excellent point.

I've been accused of being an Nvidia "fanboy" when I touch on this. I attempt to explain:

"Nvidia made the very hard, very expensive commitment to developing and supporting CUDA 15 years ago when this space was in it's infancy (non-existent). They SUNK incredible resources into this gamble/vision to universally support CUDA on every chip and every platform - for 15 years. They didn't achieve their position through shady dealings or luck, they earned it with a decade and a half of investment, focus, and execution."

Granted, they do somewhat abuse the position they have now (as often noted on HN and elsewhere). At the risk of whataboutism I ask: "Show me a corporation that wouldn't. Do you think if AMD had their market share they'd be nice, cuddly good guys?" Microsoft, Intel, Nvidia, Standard Oil, the Phoebus cartel, etc - it goes on and on. Always has and always will. Of course I'm not saying it's a good thing, it's just a fact of the real world.

So what if other companies would similarly abuse their position? If it doesn't excuse Nvidia's behavior in some way then it hardly seems relevant to point out. We still need more competition in this space, or Nvidia parts will become increasingly bad deals (as we've seen for consumer GPUs).

I'm not saying it's a good thing, I'm simply pointing out it's more-or-less universal standard behavior for people and corporations to abuse positions of power unless checked externally - anti-trust action, checks and balances within government, other regulation, etc. As noted the recurring tendency on HN and elsewhere to paint Nvidia as some kind of unique diabolical actor is very strange.

How are Nvidia consumer GPUs a bad deal? When comparing top of the line cards (I'm not going to bother looking elsewhere in the product lines) for 60% more cost (yes, significant) with an RTX 4090 you get:

- Performance that walks all over the 7900 XTX[0].

- The ability to use it to self-host, experiment, learn, etc a never-ending range of ML applications that (as discussed) "just work" as a docker pull away.

With an RTX 4090 you could have stunning gaming performance one minute and then seconds later be running a local LLM, etc. That is tremendously more value than the 60% price difference. If all I wanted to do was gaming and I was price sensitive I'd save myself the $600 and be happy with an AMD GPU. But, looking at market share[1] (at least 80% across desktop gaming and GPGPU) either the value of AMD GPUs is a little known secret (it isn't) or consumers, with the ability for choice, overwhelmingly see the value in Nvidia GPUs.

[0] - https://techguided.com/7900-xtx-vs-rtx-4090/

[1] - https://wccftech.com/q3-2022-discrete-gpu-market-share-repor...

The sheer volume of work of 'just' creating and maintaining a C++-like compiler, highly performant cuBLAS, cufft, cuSOLVER, cuDNN with very high perf, all by their lonesome in closed source shows how far one would have to go to even eat parts of their lunch.

We (who want a real alternative to NVIDIA) either find a way to pool global resources applicable to this level kind of effort on all new accelerator architectures, or we wait for them to stumble, Intel-like. Intel and AMD not pooling their resources on this is self-defeating.

<< I'm often asked by users with no experience in the space why we don't target AMD, Coral TPUs (don't even get me started), etc. It's almost impossible to understand "why CUDA" unless you've fought these battles and spent time with "alternatives".

Would you be willing to elaborate ( ie. I would love to hear you get started )? I absolutely agree that some competition is needed in this space. I am absolutely not an expert so it is hard for me to understand why there is no real alternative to CUDA. Are they just too hard to set up? Not popular enough to have any support?

Probably the best reference is HN itself. Look at the dozens of GPGPU projects, articles, papers, etc that hit the front page in any given week. Then look to see how many of them support AMD/ROCm. Spoiler alert: virtually zero.

That's compelling enough to justify the position here but you can do further research to explore the challenges with other platforms (like ROCm). Just glance at issue trackers for Pytorch, Tensorflow, and higher level projects that (rarely) support ROCm - you will notice a clear trend. Even though CUDA is more capable and outnumbers AMD use 10:1 the issues reported currently are:

Pytorch

Search for ROCm - 2,557 issues open (~10% market share)

Search for CUDA - 5,430 issues open (at least 80% market share)

Even in the limited cases where it's attempted the capability and experience with AMD/ROCm is significantly worse, to the point of "almost no one even bothers anymore" (see my top paragraph).