Nvidia, every mention
27 scenes (2024), the whole family · ← back to Nvidia
tap a year for its mentions
every year 2024 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
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.