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I don’t understand why the US government is moving to cripple NVidia. It’s not Jensen’s fault that he’s a visionary CEO who anticipated this market and invested

I don’t understand why the US government is moving to cripple NVidia. It’s not Jensen’s fault that he’s a visionary CEO who anticipated this market and invested in CUDA. Jensen also didn’t anything to do with the fact that Intel had incompetent leadership for 15 years before Pat Gelsinger joined in 2021.

vlovich123 2y ago
It’s questionable whether or not he anticipated this specific market vs he just positioned the company to eat up sales to high energy particle physics & similar super-computer applications and then got lucky that this accelerator also turned out to work pretty well for AI. At best, he saw that he needed a more general compute platform for GPUs but OpenCL was a thing so he wasn’t alone in recognizing this. There’s even an argument to be made that NVidia intentionally undermined OpenCL to cause it to fail; since NVidia has the fastest cards & mind share, if they make CUDA perform better than OpenCL, they encourage developers to buy into that development environment and thus lock out the standard from adoption.
- radicalbyte 2y ago
  It seemed like a no-brainer to me to open the hardware up to more general purpose compute.. but I'm a software guy who started on machines where where completely open and can "do magic" thanks to that.
- bcatanzaro 2y ago
  People have been saying that GPUs randomly happened to be good at ML since at least 2014 when Nervana was founded. It was clear 12 years ago to anyone paying attention that DL was revolutionizing the world and that NVIDIA GPUs were at the center of the revolution. Whatever random chance factored into NVIDIA's success is outweighed by the decade+ of pedal-to-the-metal development NVIDIA has undertaken while its competitors decried AI as an overhyped bubble. I have been there for this history, working on ML on GPUs at NVIDIA for a few years before Jensen decided to productize my little research project, CUDNN.
  - talldayo 2y ago
    Yeah, you really get this sense when you page through their research history on the topic: https://research.nvidia.com/publications?f%5B0%5D=research_area%3A26 https://research.nvidia.com/publications?f%5B0%5D=research_a... CUDA won big because it made a big bet. Were it that OpenCL was ubiquitous and at feature-parity with CUDA, maybe there would be more than one player dealt-in at the table today. But everyone else folded while Nvidia ran the dealer for 10 long years.
  - vlovich123 2y ago
    The history page for CUDA is pretty accessible [1]. It originated from experiments at Stanford in 2000 with Ian taking on leadership of CUDA development in 2004. > In pushing for CUDA, Jensen Huang aimed for the Nvidia GPUs to become a general hardware for scientific computing. CUDA was released in 2006. Around 2015, the focus of CUDA changed to neural networks.[8] Credit to Jensen for pivoting, but I recall hearing about CUDA networks from Google tech talks in 2009 and realizing they would be huge. It wasn't anything unique to realize NNs were a huge innovation but it did take another 5 years for it to mature enough and for it to become clear that GPUs could be useful for training and whatnot. Additionally, it's important to remember that Google had a huge early lead here & worked closely with Nvidia since CUDA was much more mature than OpenCL (due to intentional sabotage or otherwise) and Nvidia's chips satisfied the compute needs of that early development. So it was more like Google leading Nvidia to the drinking well and Nvidia eventually realizing it was potentially an untapped ocean and investing some resources. Remember, they also put resources behind cryptocurrency when that bubble was inflating. They're good at opportunistically taking advantage of those bubbles. It was also around this time period that Google realized they should start investing in dedicated accelerators with their TPUs because Nvidia could not meet their needs due to lack of focus (+ dedicated accelerators could outperform) leading to the first TPU being used internally by 2015 [2]. Pretending like Jensen is some unique visionary seeing something no one else in the industry didn't is insane. It was a confluence of factors and Jensen was adept at navigating his resources to take advantage of it. You can appreciate Nvidia's excellence here without pretending like Jensen is some kind of AI messiah. [1] https://en.wikipedia.org/wiki/CUDA https://en.wikipedia.org/wiki/CUDA [2] https://en.wikipedia.org/wiki/Tensor_Processing_Unit https://en.wikipedia.org/wiki/Tensor_Processing_Unit
- paulmd 2y ago
  > It’s questionable whether or not he anticipated this specific market vs he just positioned the company to eat up sales to high energy particle physics & similar super-computer applications and then got lucky it's actually specifically not questionable because it's been covered by journalists etc. https://www.newyorker.com/magazine/2023/12/04/how-jensen-huangs-nvidia-is-powering-the-ai-revolution https://www.newyorker.com/magazine/2023/12/04/how-jensen-hua... > Within a couple of years, every entrant in the ImageNet competition was using a neural network. By the mid-twenty-tens, neural networks trained on G.P.U.s were identifying images with ninety-six-per-cent accuracy, surpassing humans. Huang’s ten-year crusade to democratize supercomputing had succeeded. “The fact that they can solve computer vision, which is completely unstructured, leads to the question ‘What else can you teach it?’ ” Huang said to me. > The answer seemed to be: everything. Huang concluded that neural networks would revolutionize society, and that he could use CUDA to corner the market on the necessary hardware. He announced that he was once again betting the company. “He sent out an e-mail on Friday evening saying everything is going to deep learning, and that we were no longer a graphics company,” Greg Estes, a vice-president at Nvidia, told me. “By Monday morning, we were an A.I. company. Literally, it was that fast.” > Around the time Huang sent the e-mail, he approached Catanzaro, Nvidia’s leading A.I. researcher, with a thought experiment. “He told me to imagine he’d marched all eight thousand of Nvidia’s employees into the parking lot,” Catanzaro said. “Then he told me I was free to select anyone from the parking lot to join my team.”
  - deleted 2y ago
    [deleted]
  - vlovich123 2y ago
    I mean your article literally suggests that CUDA was a misstep. It failed to bring supercomputing to the masses until they got bailed out by AI coinciding with needing supercomputer powers. > In marketing cuda, Nvidia had sought a range of customers, including stock traders, oil prospectors, and molecular biologists. At one point, the company signed a deal with General Mills to simulate the thermal physics of cooking frozen pizza. One application that Nvidia spent little time thinking about was artificial intelligence. There didn’t seem to be much of a market. > Despite the snub, Hinton encouraged his students to use cuda, including a Ukrainian-born protégé of his named Alex Krizhevsky, who Hinton thought was perhaps the finest programmer he’d ever met. In 2012, Krizhevsky and his research partner, Ilya Sutskever, working on a tight budget, bought two GeForce cards from Amazon. Krizhevsky then began training a visual-recognition neural network on Nvidia’s parallel-computing platform, feeding it millions of images in a single week. “He had the two G.P.U. boards whirring in his bedroom,” Hinton said. “Actually, it was his parents who paid for the quite considerable electricity costs.” Basically Hinton thought CUDA was a better platform and kept encouraging his acolytes to utilize it with NVidia completely ignoring it. For what it’s worth ChatGPT and LLMs are not the start of the story. I remember seeing Hinton present his research in 2009 and thought it was a revolutionary step forward. It took a much longer amount of time for NVidia to notice that they should be marketing CUDA to AI. They did so around the same time that Google started building their own inference accelerators for Tensorflow. So at that point NVidia was getting multiple signals already from a big customer that there’s something here.
arlcode 2y ago
Laws in this field do not make a moral judgement on how a company got where they are. They look at what you are doing with the power you actually have. Once you are the size and importance of MS, OpenAI or Nvidia, the rules change and everyone knows that.
bmacho 2y ago
Nvidia always tries to be anticompetitive, creating and exploiting vendor lock-in, instead of giving back to the world , like e.g. like Sun did, open-sourcing everything. It is definitely Jensen's fault. I think it is a law of nature that shitty and harmful behaviour pays off, but that's not a problem, if we can correct it time-to-time. With antitrust inquires for example.

vlovich123 2y ago

It’s questionable whether or not he anticipated this specific market vs he just positioned the company to eat up sales to high energy particle physics & similar super-computer applications and then got lucky that this accelerator also turned out to work pretty well for AI. At best, he saw that he needed a more general compute platform for GPUs but OpenCL was a thing so he wasn’t alone in recognizing this. There’s even an argument to be made that NVidia intentionally undermined OpenCL to cause it to fail; since NVidia has the fastest cards & mind share, if they make CUDA perform better than OpenCL, they encourage developers to buy into that development environment and thus lock out the standard from adoption.

radicalbyte 2y ago
It seemed like a no-brainer to me to open the hardware up to more general purpose compute.. but I'm a software guy who started on machines where where completely open and can "do magic" thanks to that.
bcatanzaro 2y ago
People have been saying that GPUs randomly happened to be good at ML since at least 2014 when Nervana was founded. It was clear 12 years ago to anyone paying attention that DL was revolutionizing the world and that NVIDIA GPUs were at the center of the revolution. Whatever random chance factored into NVIDIA's success is outweighed by the decade+ of pedal-to-the-metal development NVIDIA has undertaken while its competitors decried AI as an overhyped bubble. I have been there for this history, working on ML on GPUs at NVIDIA for a few years before Jensen decided to productize my little research project, CUDNN.
- talldayo 2y ago
  Yeah, you really get this sense when you page through their research history on the topic: https://research.nvidia.com/publications?f%5B0%5D=research_area%3A26 https://research.nvidia.com/publications?f%5B0%5D=research_a... CUDA won big because it made a big bet. Were it that OpenCL was ubiquitous and at feature-parity with CUDA, maybe there would be more than one player dealt-in at the table today. But everyone else folded while Nvidia ran the dealer for 10 long years.
- vlovich123 2y ago
  The history page for CUDA is pretty accessible [1]. It originated from experiments at Stanford in 2000 with Ian taking on leadership of CUDA development in 2004. > In pushing for CUDA, Jensen Huang aimed for the Nvidia GPUs to become a general hardware for scientific computing. CUDA was released in 2006. Around 2015, the focus of CUDA changed to neural networks.[8] Credit to Jensen for pivoting, but I recall hearing about CUDA networks from Google tech talks in 2009 and realizing they would be huge. It wasn't anything unique to realize NNs were a huge innovation but it did take another 5 years for it to mature enough and for it to become clear that GPUs could be useful for training and whatnot. Additionally, it's important to remember that Google had a huge early lead here & worked closely with Nvidia since CUDA was much more mature than OpenCL (due to intentional sabotage or otherwise) and Nvidia's chips satisfied the compute needs of that early development. So it was more like Google leading Nvidia to the drinking well and Nvidia eventually realizing it was potentially an untapped ocean and investing some resources. Remember, they also put resources behind cryptocurrency when that bubble was inflating. They're good at opportunistically taking advantage of those bubbles. It was also around this time period that Google realized they should start investing in dedicated accelerators with their TPUs because Nvidia could not meet their needs due to lack of focus (+ dedicated accelerators could outperform) leading to the first TPU being used internally by 2015 [2]. Pretending like Jensen is some unique visionary seeing something no one else in the industry didn't is insane. It was a confluence of factors and Jensen was adept at navigating his resources to take advantage of it. You can appreciate Nvidia's excellence here without pretending like Jensen is some kind of AI messiah. [1] https://en.wikipedia.org/wiki/CUDA https://en.wikipedia.org/wiki/CUDA [2] https://en.wikipedia.org/wiki/Tensor_Processing_Unit https://en.wikipedia.org/wiki/Tensor_Processing_Unit
paulmd 2y ago
> It’s questionable whether or not he anticipated this specific market vs he just positioned the company to eat up sales to high energy particle physics & similar super-computer applications and then got lucky it's actually specifically not questionable because it's been covered by journalists etc. https://www.newyorker.com/magazine/2023/12/04/how-jensen-huangs-nvidia-is-powering-the-ai-revolution https://www.newyorker.com/magazine/2023/12/04/how-jensen-hua... > Within a couple of years, every entrant in the ImageNet competition was using a neural network. By the mid-twenty-tens, neural networks trained on G.P.U.s were identifying images with ninety-six-per-cent accuracy, surpassing humans. Huang’s ten-year crusade to democratize supercomputing had succeeded. “The fact that they can solve computer vision, which is completely unstructured, leads to the question ‘What else can you teach it?’ ” Huang said to me. > The answer seemed to be: everything. Huang concluded that neural networks would revolutionize society, and that he could use CUDA to corner the market on the necessary hardware. He announced that he was once again betting the company. “He sent out an e-mail on Friday evening saying everything is going to deep learning, and that we were no longer a graphics company,” Greg Estes, a vice-president at Nvidia, told me. “By Monday morning, we were an A.I. company. Literally, it was that fast.” > Around the time Huang sent the e-mail, he approached Catanzaro, Nvidia’s leading A.I. researcher, with a thought experiment. “He told me to imagine he’d marched all eight thousand of Nvidia’s employees into the parking lot,” Catanzaro said. “Then he told me I was free to select anyone from the parking lot to join my team.”
- deleted 2y ago
  [deleted]
- vlovich123 2y ago
  I mean your article literally suggests that CUDA was a misstep. It failed to bring supercomputing to the masses until they got bailed out by AI coinciding with needing supercomputer powers. > In marketing cuda, Nvidia had sought a range of customers, including stock traders, oil prospectors, and molecular biologists. At one point, the company signed a deal with General Mills to simulate the thermal physics of cooking frozen pizza. One application that Nvidia spent little time thinking about was artificial intelligence. There didn’t seem to be much of a market. > Despite the snub, Hinton encouraged his students to use cuda, including a Ukrainian-born protégé of his named Alex Krizhevsky, who Hinton thought was perhaps the finest programmer he’d ever met. In 2012, Krizhevsky and his research partner, Ilya Sutskever, working on a tight budget, bought two GeForce cards from Amazon. Krizhevsky then began training a visual-recognition neural network on Nvidia’s parallel-computing platform, feeding it millions of images in a single week. “He had the two G.P.U. boards whirring in his bedroom,” Hinton said. “Actually, it was his parents who paid for the quite considerable electricity costs.” Basically Hinton thought CUDA was a better platform and kept encouraging his acolytes to utilize it with NVidia completely ignoring it. For what it’s worth ChatGPT and LLMs are not the start of the story. I remember seeing Hinton present his research in 2009 and thought it was a revolutionary step forward. It took a much longer amount of time for NVidia to notice that they should be marketing CUDA to AI. They did so around the same time that Google started building their own inference accelerators for Tensorflow. So at that point NVidia was getting multiple signals already from a big customer that there’s something here.

arlcode 2y ago

Laws in this field do not make a moral judgement on how a company got where they are. They look at what you are doing with the power you actually have. Once you are the size and importance of MS, OpenAI or Nvidia, the rules change and everyone knows that.

bmacho 2y ago

Nvidia always tries to be anticompetitive, creating and exploiting vendor lock-in, instead of giving back to the world , like e.g. like Sun did, open-sourcing everything. It is definitely Jensen's fault. I think it is a law of nature that shitty and harmful behaviour pays off, but that's not a problem, if we can correct it time-to-time. With antitrust inquires for example.