Nvidia, every mention
17 scenes (2023), the whole family · ← back to Nvidia
tap a year for its mentions
every year 2023 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
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.