Why the focus on AMD and Nvidia? It really isn't that hard to design a large number of ALU blocks into some silicon IP block and make them work together efficiently.
The real accomplishment is fabricating them.
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It really is that hard, and the fabrication side of the issue the easy part from Nvidia's perspective - you just pay TSMC a shitload of money. Nvidia's real victory (besides leading on performance-per-watt) is that their software stack doesn't suck. They invested in complex shader units and tensor accelerators that scale with the size of the card rather than being restrained in puny and limited NPUs. CUDA unified this featureset and was industry-entrenched for almost a decade, which gave it pretty much any feature you could want be it crypto acceleration or AI/ML primitives.
The ultimate tragedy is that there was a potential future where a Free and Open Source CUDA alternative existed. Apple wrote the OpenCL spec for exactly that purpose and gave it to Khronos, but later abandoned it to focus on... checks clipboard MLX and Metal Performance Shaders. Oh, what could have been if the industry weren't so stingy and shortsighted.
Nvidia's lead is not only cemented by dense silicon. Their designs are extremely competitive, perhaps even a generational leap over what their competitors offer.
YES!! Thank you!
> Nvidia's real victory (besides leading on performance-per-watt) is that their software stack doesn't suck
YES! And it's not just CUDA and CUDA-adjacent tools, but also their cuDNN/cuBLAS/etc. libraries. They invest a massive amount of staffing into squeezingt the last drop of performance out of their hardware, identifying areas for improvement and feeding that back to the architects.
> Apple wrote the OpenCL spec for exactly that purpose and gave it to Khronos
Nitpick: Affie Munshi from Apple wrote down a draft and convinced his management to offer it to Khronos, where it was significantly modified over... was it a year or so?... by a number of representatives from a dozen companies or so. A ton of smart people contributed a ton of work into what became the 1.0 version.
And let me tell you that the discussions were often tense, both during the official meetings as well as what happened behind the scenes. The end result was as good as you can expect from a large committee composed of representatives from competing companies.
But, in summary, you get it, unlike so many commenters in HN.
The practical answer is that all of FAANG will have to pick up the pieces once their supply chain is shattered. Samsung would quickly reach capacity with AMD and potentially Nvidia as priority customers, and Intel will be trying to court Nvidia and Apple as high-margin customers for some low-yield 18A contract. Depending on whether TSMC's Arizona foundry ever reaches operational capacity, they will be balancing orders from Nvidia and Apple in the same way they do today. Given the pitifully low investment, it's not really likely the Arizona facility will make a dent in the supply chain.
Fact is, Nvidia is well positioned to pick up the pieces even if 5nm> processes go away for the next decade. The only question is whether or not people will continue to have demand for CUDA, and the answer has been "yes" since long before crypto and AI were popular. If TSMC was bombed tomorrow, Nvidia would still have demand for their product and they would still have the capacity to sell it. Their competition with AMD would be somewhat normalized and Apple would be blown into the stratosphere upon realizing that they have to contract either Samsung or Intel to stay afloat. The implications for the American economy are a little upsetting but there's nothing particularly world-ending about that scenario. It would be a sad day to be a Geekbench enthusiast but life would go on.
My predicition is there will be some strong competition for Nvidia in the coming years.
Since most people use CUDA through some other library (like Torch or TF), I think the dependence on CUDA isn't as strong as you make it seem.
Intel actually has proven to be more clever than AMD in that regard, as DataParalell C++ builds on top of SYCL (it isn't only SYCL), and Intel Fortran now also does GPU offloading.