Nvidia, every mention
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tap a year for its mentions
every year anyone Dylan Patel 93Anjney Midha 21David George 20Gavin Baker 16Erik Torenberg 15Marc Andreessen 14Guido Appenzeller 14Jeff Schmidt 13Dwarkesh Patel 12Martin Casado 10
Verbatim, from the transcripts: the passages where Nvidia comes up
Jensen Huang & Arthur Mensch: Why Every Nation Needs Its Own AI Strategy
- ▶ 51:56 unnamed speaker How could it be an answer other than Nvidia other than H 100?
- ▶ 53:01 Jensen Huang GTC is a developers conference.
- ▶ 54:51 Arthur Mensch Uh, so it's, uh, I guess it's a bull case for data centers and for NVIDIA.
- ▶ 1:00:32 Jensen Huang Jensen at nvidia.com.
DeepSeek, Reasoning Models, and the Future of LLMs
- ▶ 2:22 Guido Appenzeller If you look through the steps, right, pre-training, that's typically done on very large computer infrastructure, that's where, you know, you need the large 10,000 H-one hundreds or, or, or more, and you pretty much want to train on all the…
- ▶ 24:15 Guido Appenzeller So I think here's a slide from Jason Wong, CEO of NVIDIA, where I think the way he, he framed it was to say like, look, we've, we've had our first sort of curve,
What DeepSeek Means For The Future Of AI | Tech Veterans Weigh In
- ▶ 2:43 Martin Casado You have, like, all this excitement about O-one and how that's gonna drive compute costs and NVIDIA, and then, you know, R-one comes out, and it looks pretty good, and then all of a sudden they're saying, well, you know,
- ▶ 20:41 Martin Casado NVIDIA can take a price dip. 2 times in the scene
- ▶ 28:22 Martin Casado My reaction was not, oh, shit, I need to, like, short NVIDIA or whatever. 3 times in the scene
- ▶ 41:37 Martin Casado So again, like there's this view of deep seek as a crisis moment for Nvidia crisis moment for open AI and anthropic.
AI Is Becoming a Regional Race
- ▶ 16:22 Anjney Midha So you've had an, an enormous amount of NVIDIA's purchasing orders come from, from the balance sheet of governments. 2 times in the scene
Virtual Worlds Mean Real Business: How Games Power the Future
- ▶ 1:10 unnamed speaker People forget that NVIDIA was a gaming company. 3 times in the scene
- ▶ 12:43 unnamed speaker As with most emerging tech, there's probably going to be, you know, initial use cases in gaming that sort of, um, are the, are the wedge for, for these companies to, uh, use consumer spend to fund their R and D similar to Nvidia.
Marc & Ben on AI Policy, Safety, Censorship & Unexpected Risks
- ▶ 1:11:23 Marc Andreessen NVIDIA, the, the news of the week is NVIDIA, just NVIDIA, just passed the total market cap of both the German stock market and Italian stock market combined. 3 times in the scene
The Quest for Community-Trained Open Source AI Models
- ▶ 20:24 Jeff Schmidt And there is a real reason, which is that we don't have 20,000 H 100. 3 times in the scene
- ▶ 24:59 unnamed speaker H 100.
- ▶ 36:00 Jeff Schmidt And that's why just from a practical perspective, we want to find out now actually bring it to the community and say, okay, well, let's run the giant one together because we don't have the 10,008 to hundreds to just
- ▶ 41:08 Anjney Midha oh wow, that's terrible for NVIDIA, right? 7 times in the scene
- ▶ 41:21 Anjney Midha People buying 10,020 thousand, soon, you mentioned Elon buying a 100,000 H-one hundreds, you know, co-located in a single place.
- ▶ 44:20 Anjney Midha The current distro, uh, experiment still uses H 100. 8 times in the scene
- ▶ 45:20 Jeff Schmidt Um, and so, uh, there, uh, maybe like an enterprise markup that you're getting in there, you know, that, uh, that NVIDIA is charging for like the H 100, knowing that you need to use them for training versus someone who wants to, you know,… 3 times in the scene
- ▶ 49:14 Jeff Schmidt What we didn't have was the huge stack of H 100. 3 times in the scene
- ▶ 54:29 unnamed speaker Uh, it could be possible to train seven B to like four trillion tokens with maybe 1000 H 100, like just rented all over run pod, right?
- ▶ 56:23 Jeff Schmidt Um, I think right now there's going to be a lot of these engineering open questions when we release it, and we're going to need help with, um, for example, the NVIDIA has this library called Nickel, which is used internally to actually… 2 times in the scene
- ▶ 56:23 Jeff Schmidt Um, I think right now there's going to be a lot of these engineering open questions when we release it, and we're going to need help with, um, for example, the NVIDIA has this library called Nickel, which is used internally to actually…
- ▶ 1:09:05 Anjney Midha Of people who run data centers who might, which might have, you know, two K plus H 100. 3 times in the scene
- ▶ 1:12:02 unnamed speaker And the problem with that is that right now on specialized, unspecialized hardware, like general hardware, like Nvidia H 100, the inference time and the backdrop time is almost the same.
Why Human Data is Key to AI: Alexandr Wang from Scale AI
- ▶ 1:56 Alexandr Wang Um, compute has been powered by folks like NVIDIA.
- ▶ 15:39 Alexandr Wang So, uh, so below, I mean, NVIDIA is obviously an incredible business, but the clouds also have really great businesses too, because, you know, it turns out it's pretty hard, uh, logistically to actually set up
“The Future of AI is Here” — Fei-Fei Li Unveils the Next Frontier of AI
- ▶ 8:11 Justin Johnson The newest, the latest and greatest from NVIDIA is the GB 200.
- ▶ 8:11 Justin Johnson The newest, the latest and greatest from NVIDIA is the GB 200. 3 times in the scene
Trump Vs. Biden: Tech Policy
- ▶ 45:36 Marc Andreessen Um, and, and I should also say just on that, like Moore's law, like if, you know, for people tracking like NVIDIA, you know, NVIDIA is not one of the most valuable companies in the world, you know, sort of like three trillion dollars. 3 times in the scene
AI, Robotics & the Future of Manufacturing
- ▶ 35:53 Ben Horowitz You know, it's, I think on a general chip, it is really hard to, like the current chip makers, like NVIDIA is really good and innovating and AMD is really good and innovating and so forth.
Startup Building: Challenges & Opportunities
- ▶ 39:38 Ben Horowitz Um, but, uh, you know, we had that like a really interesting kind of meeting with, uh, Jensen from NVIDIA, where he was just pointing out that the reason NVIDIA is still so productive is because it is really small. 2 times in the scene
Marty Chavez (Sixth Street): Finding a Single Source of AI Truth
- ▶ 32:34 Marty Chavez I, I had the opportunity of being, uh, the fireside chat post for, uh, for Jensen, uh, at, at, uh, of NVIDIA at the, uh, at the recursion at the JP Morgan healthcare event. 2 times in the scene
Future of Crypto, Blockchain & Web3 w/ Chris Dixon
- ▶ 1:12:47 Chris Dixon So instead of having to go buy, you know, Facebook just bought, I think it was 10,000 H 100, ten billion dollars in capex.
Text to Video: The Next Leap in AI Generation
- ▶ 9:52 Robin Rombach Um, like when I first saw this myself, like we, we had like this latent diffusion approach that we developed at the university, and then, um, I mean, we got a machine with like eight, eight gigabyte A-one hundreds, um, just after we put it… 2 times in the scene
- ▶ 33:35 Andreas Blattman But now we have extremely nice accelerators and like with the newest H-H-E-H-E-H-E-H-E-H-E-H-E-S, it's insane how fast these GPUs are actually running, how fast you can train models on those.
Tony Robbins on the Future of Health & Longevity
- ▶ 13:52 Tony Robbins And, you know, kind of like, uh, Invita, what it's done in its ability to forecast, you know, how they build a chip, doing that and actually being able to predict what would happen in a study and so forth.
Advancing AI, Approaching VC's & Crypto Scandals: Ask Us Anything!
- ▶ 39:54 Ben Horowitz So if you look at GPUs, you know, Nvidia is clearly the leader, but like one of their biggest advantages is 4 times in the scene
Universally Accessible Intelligence with Character.ai's Noam Shazeer
- ▶ 4:17 Noam Shazeer Well, I, I think we just need a sort of global pause of like six months, no, about four months until we get enough H-one hundreds online to train our next model.
- ▶ 13:25 Noam Shazeer Like if, I mean, if you just look at it, um, I, I think I saw an article yesterday, like NVIDIA is going to build like another one and a half million H 100, like next year.
- ▶ 13:25 Noam Shazeer Like if, I mean, if you just look at it, um, I, I think I saw an article yesterday, like NVIDIA is going to build like another one and a half million H 100, like next year. 2 times in the scene
Improving AI with Anthropic's Dario Amodei
- ▶ 7:18 Dario Amodei Um, and so I think that, that factor of 100 plus the compute inherently getting faster with the H-one hundreds, uh, that's been a particularly big jump because of the move to lower precision.
The True Cost of Compute
- ▶ 6:48 Guido Appenzeller Then you can be like, okay, so let's take, say, an A-Hundred, right? 2 times in the scene
- ▶ 9:38 Guido Appenzeller If you take something like Stable Diffusion, right, a very popular model for image generation, um, you know, that runs, that, that runs on a, on a MacBook, for example, out of, uh, you know, that, that has enough memory and enough compute…
Chasing Silicon: The Race for GPUs
- ▶ 3:43 unnamed speaker Maybe this is a silly question, but what really is stopping companies like Intel, like NVIDIA from going in, like, Tenexing their production?
AI Hardware, Explained.
- ▶ 1:55 unnamed speaker And in this first segment, we dive into the terminology and technology from GPU to TPU, including what they are, how they work, the key players like Nvidia competing for chip dominance, and also, we address the question, is Moore's Law…
- ▶ 6:16 unnamed speaker Today's GPUs are far more powerful than their ancestors, whether we're comparing to the earliest graphics cards in arcade gaming days, 50 years ago, or the GeForce two 56, the first personal computer GPU unveiled by Nvidia in 1999.
- ▶ 7:12 unnamed speaker Alright, so perhaps it's not so surprising that NVIDIA's prized GPUs are aligned to this AI wave, but they're also not the only company participating. 4 times in the scene
- ▶ 7:32 Guido Appenzeller A-one-hundred is the workhorse that powers the current AI revolution.
- ▶ 7:35 Guido Appenzeller They're coming up with a new one called the H-one-hundred, you know, which is of the next generation.
- ▶ 8:15 unnamed speaker When we think about the different chips, you mentioned, like, the A-One hundreds are the strongest, and maybe there's the most demand for those, but how do they compare to some of these chips created by other companies?
- ▶ 8:39 Guido Appenzeller There's others that are very competitive with what NVIDIA has. 6 times in the scene
- ▶ 9:31 unnamed speaker So NVIDIA's CUDA system makes it easier for engineers to plug in and make optimizations, like running with lower precision numbers.
a16z Podcast | From Research to Startup, There and Back Again
- ▶ 6:41 Sonal Chokshi And the reason I think about this is because I think about what happened with GPUs and NVIDIA and how it then became the enabling for, like, artificial intelligence.
a16z Podcast | Technological Trends, Capital, and Internet 'Disruption'
- ▶ 53:01 Chris Dixon So like, just like the, the, that market is one where the, the gamers have been endlessly hungry for more polygons, and, and that created this kind of, you know, Nvidia and this whole industry around it.
a16z Podcast | High Growth in Companies (and Tech)
- ▶ 24:09 Elad Gil So if you look at NVIDIA and NVIDIA, GPUs are really the primary basis
- ▶ 29:01 Elad Gil Yeah, and I think every major technology wave also had a major silicon company created in it, and right now to some extent that's Nvidia for not only graphics processing but also for machine learning purposes.
a16z Podcast | The Self-Flying Camera
a16z Podcast | AI, from 'Toy' Problems to Practical Application
- ▶ 1:25 Scott Clark And now they have the infrastructure readily available with things like AWS and all these new Nvidia chips.
a16z Podcast | The Cloud Atlas to Real Quantum Computing
- ▶ 2:00 Sonal Chokshi By GPUs, we mean graphical processing units like the kind that NVIDIA makes and other companies make that were originally used for the gaming industry, but they're now being used widely deployed in machine learning and lots of.
- ▶ 3:13 Jeff Cordova Uh, CUDA is, uh, NVIDIA's, uh, language for doing parallel processing on the GPU.
a16z Podcast | Quantum Computing, Now and Next
- ▶ 8:01 Chad Rigetti NVIDIA has built an incredible business around GPUs, which, which really kind of owns a parallelization of, of tasks across a, a account, a small number of processors, hundreds or thousands of processors in a single die, and then
a16z Podcast | Apple and the Case of Invisible But Audible Innovation
- ▶ 30:50 Kyle Russell This requires collaboration between Oculus and the chip makers like NVIDIA and Microsoft, and Apple can do all of this on their own.
a16z Podcast | Software Programs the World
- ▶ 8:01 Marc Andreessen One of the really interesting hardware platforms that's emerging right now is, um, NVIDIA, which is a very well-established public chip company, been very successful to your point, doing graphics chips for a very long time, um, has become… 2 times in the scene
a16z Podcast | When Humanity Meets A.I.
- ▶ 2:51 Sonal Chokshi Is it like what's happening with Nvidia's chips right now or something different? 2 times in the scene
a16z Podcast | Innovation vs. Invention at Google I/O
- ▶ 24:55 Benedict Evans Nvidia.
16 Questions About Self Driving Cars
- ▶ 8:21 Frank Chen NVIDIA would have you believe that the path to get to a self-driving car is basically deep learning end-to-end.
AI, Deep Learning, and Machine Learning: A Primer
- ▶ 43:15 Frank Chen In fact, Nvidia attributes a lot of its recent growth and success as a business to this new line of business, which is providing deep learning systems for autonomous cars. 2 times in the scene
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