Comment by wtallis
4 days ago
It's amazing to step back and look at how much of NVIDIA's success has come from unforeseen directions. For their original purpose of making graphics chips, the consumer vs pro divide was all about CAD support and optional OpenGL features that games didn't use. Programmable shaders were added for the sake of graphics rendering needs, but ended up spawning the whole GPGPU concept, which NVIDIA reacted to very well with the creation and promotion of CUDA. GPUs have FP64 capabilities in the first place because back when GPGPU first started happening, it was all about traditional HPC workloads like numerical solutions to PDEs.
Fast forward several years, and the cryptocurrency craze drove up GPU prices for many years without even touching the floating-point capabilities. Now, FP64 is out because of ML, a field that's almost unrecognizable compared to where it was during the first few years of CUDA's existence.
NVIDIA has been very lucky over the course of their history, but have also done a great job of reacting to new workloads and use cases. But those shifts have definitely created some awkward moments where their existing strategies and roadmaps have been upturned.
Maybe some luck. But there’s also a principle that if you optimize the hell out of something and follow customer demand, there’s money to be made.
Nvidia did a great job of avoiding the “oh we’re not in that market” trap that sunk Intel (phones, GPUs, efficient CPUs). Where Intel was too big and profitable to cultivate adjacent markets, Nvidia did everything they could to serve them and increase demand.
Most people don't appreciate how many dead end applications NVIDIA explored before finding deep learning. It took a very long time, and it wasn't luck.
It was luck that a viable non-graphics application like deep learning existed which was well-suited to the architecture NVIDIA already had on hand. I certainly don't mean to diminish the work NVIDIA did to build their CUDA ecosystem, but without the benefit of hindsight I think it would have been very plausible that GPU architectures would not have been amenable to any use cases that would end up dwarfing graphics itself. There are plenty of architectures in the history of computing which never found a killer application, let alone three or four.
Even that is arguably not lucky, it just followed a non-obvious trajectory. Graphics uses a fair amount of linear algebra, so people with large scale physical modeling needs (among many) became interested. To an extent the deep learning craze kicked off because of developments in computation on GPUs enabled economical training.
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There's something of a feedback loop here, in that the reason that transformers and attention won over all the other forms of AI/ML is that they worked very well on the architecture that NVIDIA had already built, so you could scale your model size very dramatically just by throwing more commodity hardware at it.
It was luck, but that doesn't mean they didn't work very hard too.
Luck is when preparation meets opportunity.
It was definitely luck, greg. And Nvidia didn't invent deep learning, deep learning found nvidias investment in CUDA.
I remember it differently. CUDA was built with the intention of finding/enabling something like deep learning. I thought it was unrealistic too and took it on faith in people more experienced than me, until I saw deep learning work.
Some of the near misses I remember included bitcoin. Many of the other attempts didn't ever see the light of day.
Luck in english often means success by chance rather than one's own efforts or abilities. I don't think that characterizes CUDA. I think it was eventual success in the face of extreme difficulty, many failures, and sacrifices. In hindsight, I'm still surprised that Jensen kept funding it as long as he did. I've never met a leader since who I think would have done that.
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So it could just as easily have been Intel or AMD, despite them not having CUDA or any interest in that market? Pure luck that the one large company that invested to support a market reaped most of the benefits?
They were also bailed out by Sega.
When they couldn't deliver the console GPU they promised for the Dreamcast (the NV2), Shoichiro Irimajiri, the Sega CEO at the time let them keep the cash in exchange for stock [0].
Without it Nvidia would have gone bankrupt months before Riva 128 changed things.
Sega console arm went bust not that it mattered. But they sold the stock for about $15mn (3x).
Had they held it, Jensen Huang ,estimated itd be worth a trillion[1]. Obviously Sega and especially it's console arm wasn't really into VC but...
My wet dream has always been what if Sega and Nvidia stuck together and we had a Sega tegra shield instead of a Nintendo switch? Or even what if Sega licensed itself to the Steam Deck? You can tell I'm a sega fan boy but I can't help that the Mega Drive was the first console I owned and loved!
[0] https://www.gamespot.com/articles/a-5-million-gift-from-sega...
[1] https://youtu.be/3hptKYix4X8?t=5483&si=h0sBmIiaduuJiem_
I don't think it was luck. I think it was inevitable.
They positioned the company on high performance computing, even if maybe they didn't think they were a HPC company, and something was bound to happen in that market because everybody was doing more and more computing. Then they executed well with the usual amount of greed that every company has.
The only risk for well positioned companies is being too ahead of times: being in the right market but not surviving long enough to see a killer app happen.
The counter question is: why have AMD been so bad by comparison?
Because GPUs require a lot on the software side, and AMD sucks at software. They are a CPU company that bought a GPU company. ATI should have been left alone.
Because AMD only every had one strategy for software:
Look at it as, we're David against the Goliath, we're going to utilize open-source because that's how you win.
Then make zero effecting trying to increase adoption, which then result in reducing support for it and eventually giving up.
The whole GPU history is off and being driven by finance bros as well. Everyone believes Nvidia kicked off the GPU AI craze when Ilya Sutskever cleaned up on AlexNet with an Nvidia GPU back in 2012, or when Andrew Ng and team at Stanford published their "Large Scale Deep Unsupervised Learning using Graphics Processors" in 2009, but in 2004, a couple of Korean researches were the first to implement neural networks on a GPU, using ATI Radeons (now AMD): https://www.sciencedirect.com/science/article/abs/pii/S00313...
I remember ATI and Nvidia were neck-and-neck to launch the first GPUs around 2000. Just so much happening so fast.
I'd also say Nvidia had the benefit of AMD going after and focusing on Intel both at the server level as well as the integrated laptop processors, which was the reason they bought ATI.