Nvidia, every mention
349 scenes, the whole family · ← back to Nvidia
tap a year for its mentions
every year anyone Shawn Wang 80Dylan Patel 55Kyle Kranen 43Ali Taha 26Ethan He 24Chris Lattner 24George Hotz 23Philip Kiely 20Doug O'Laughlin 19Sarah Chieng 17
Verbatim, from the transcripts: the passages where Nvidia comes up
Agent Inference at the "Speed of Light" — How NVIDIA moves like a $4.3 Trillion Startup
- ▶ 1:01:53 Kyle Kranen It was originally called NVIDIA AI Playground.
- ▶ 1:01:55 Kyle Kranen Then it was called AI Foundation.
- ▶ 1:02:35 unnamed speaker There was also NIMS, right? 3 times in the scene
- ▶ 1:07:16 Kyle Kranen We have a couple of people at NVIDIA. 2 times in the scene
- ▶ 1:12:27 unnamed speaker You guys are GPU rich at NVIDIA. 3 times in the scene
- ▶ 1:13:23 Kyle Kranen The Blackwell, like, RTX, 6000 cards. 2 times in the scene
- ▶ 1:13:59 Kyle Kranen I mean, the big difference against like the RTX, like gaming GPUs is it, I mean, obviously it's like black ball, like it's a pro GPU and has a lot of VRAM, which means you can run pretty large models on it.
- ▶ 1:16:14 Kyle Kranen Yeah, we're thinking about that for DIMO. 2 times in the scene
- ▶ 1:25:29 Shawn Wang You guys are, like, sort of, like, the sort of young faces of NVIDIA with so much energy, and, but, like, also a lot of technical depth, and I think, uh, people learned about for this session, so thank you.
- ▶ 1:25:44 Shawn Wang Uh, and, uh, see you at GTC.
Dylan Patel Explains the AI War While Cooking | In-Context Cooking
- ▶ 3:12 Dylan Patel I was moderating Reddit and all these things anonymously for, around like hardware, NVIDIA, Intelli, and this kind of stuff, and I was posting all the stuff,
- ▶ 12:31 Dylan Patel So for example, if you look at Nvidia or you look at like David Sachs,
- ▶ 38:49 unnamed speaker Because you said that NVIDIA's kind of covering their bases with the Rubin CPX, uh, the garage chips, standard GPUs, um, and there's a lot more startups out there that are very specialized, and even, like, a lot of YC companies, right,… 10 times in the scene
- ▶ 38:49 unnamed speaker Because you said that NVIDIA's kind of covering their bases with the Rubin CPX, uh, the garage chips, standard GPUs, um, and there's a lot more startups out there that are very specialized, and even, like, a lot of YC companies, right,… 2 times in the scene
- ▶ 42:46 Dylan Patel It was a large GPU, um, and it was like having, it was like the best memory, the best networking, everything, sort of the best as possible, um, and sort of like one size fits all, uh, with the main line of like A-one hundred, H-one…
- ▶ 42:46 Dylan Patel It was a large GPU, um, and it was like having, it was like the best memory, the best networking, everything, sort of the best as possible, um, and sort of like one size fits all, uh, with the main line of like A-one hundred, H-one…
- ▶ 42:46 Dylan Patel It was a large GPU, um, and it was like having, it was like the best memory, the best networking, everything, sort of the best as possible, um, and sort of like one size fits all, uh, with the main line of like A-one hundred, H-one…
- ▶ 43:01 Dylan Patel But as we look to Reuben and beyond, right, Jensen is really, like, starting to fully embrace heterogeneity, right?
Claude Code for Finance + The Global Memory Shortage: Doug O'Laughlin, SemiAnalysis
- ▶ 8:21 Doug O'Laughlin So Nvidia is probably the best case. 4 times in the scene
- ▶ 35:31 Doug O'Laughlin To just run the swarm, I think it's like a 16 node of H-one hundreds.
- ▶ 1:35:03 Doug O'Laughlin One, we wrote about it in TP V eight, like whatever we think Ruben will be much more competitive. 4 times in the scene
- ▶ 1:35:11 Doug O'Laughlin Between TCO, between NVIDIA and TPU, right? 10 times in the scene
Inside AI’s $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z
- ▶ 23:49 Shawn Wang Because that's how much we leave on the table every single time we do, like, generic NVIDIA.
The AI Frontier: from Gemini 3 Deep Think distilling to Flash — Jeff Dean
- ▶ 34:09 Shawn Wang Like, you know, I think, uh, obviously NVIDIA has caused a lot of waves with, uh, betting very hard on SRAM with Grok.
⚡️ Reverse Engineering OpenAI's Training Data — Pratyush Maini, Datology
- ▶ 21:35 Pratyush Maini As you can see that we achieved the same performance as the NVIDIA model in almost, like, 2.7 X, like, lesser time. 3 times in the scene
- ▶ 26:24 unnamed speaker and then it's like Microsoft or whatever, and it's Apple, then it's Hugging Face, then it's NVIDIA, now it's you guys, and I'm like, you know, where, where's the, the, the sort of persistence, or like, is this such a competitive field?
Goodfire AI’s Bet: Interpretability as the Next Frontier of Model Design — Myra Deng & Mark Bissell
- ▶ 23:41 Mark Bissell It takes a full, like, H-one hundred node. 2 times in the scene
Captaining IMO Gold, Deep Think, On-Policy RL, Feeling the AGI in Singapore — Yi Tay
- ▶ 53:25 unnamed speaker Would you say that the ideas that I see there, Nvidia has NemoTron, OpenAI has GPT-OSS, these are all basically checkpoints on what's publicly known about training models as of this year.
- ▶ 54:09 unnamed speaker So then this is very related to NVIDIA's recent purchase of GroK, which I don't know if you have views because you're very TPU centric, but are we memory or compute bound? 2 times in the scene
Artificial Analysis: The Independent LLM Analysis House — with George Cameron and Micah Hill-Smith
- ▶ 56:10 Shawn Wang And also a special shout out to Nvidia Nemo Tron, which doesn't get enough credit for the amount of stuff that they do. 4 times in the scene
- ▶ 1:00:24 George Cameron Nvidia stock go up. 4 times in the scene
- ▶ 1:03:02 Micah Hill-Smith When you run all of that, for especially big sparse models, you can get a lot better than two or three eggs gain going from hopper to blackwell generation to video. 2 times in the scene
- ▶ 1:03:02 Micah Hill-Smith When you run all of that, for especially big sparse models, you can get a lot better than two or three eggs gain going from hopper to blackwell generation to video.
[State of AI Papers 2025] Fixing Research with Social Signals, OCR & Implementation — Team AlphaXiv
- ▶ 5:18 unnamed speaker If you host it on your own A-One-Hundreds, and just like, you, you batch things properly, probably Deep Seek is best bang for your buck.
- ▶ 32:35 unnamed speaker Specifically, if you have anything really involving GPUs, I would call it launchables from NVIDIA.
- ▶ 32:35 unnamed speaker Specifically, if you have anything really involving GPUs, I would call it launchables from NVIDIA.
[NeurIPS Best Paper] 1000 Layer Networks for Self-Supervised RL — Kevin Wang et al, Princeton
- ▶ 24:19 Kevin Wang The nice thing is that all of our experiments, even the thousand layer networks, can be run on one single, 80 gigabyte, each 100 GPU.
[State of Context Engineering] Agentic RAG, Context Rot, MCP, Subagents — Nina Lopatina, Contextual
- ▶ 0:32 Nina Lopatina Turing and NVIDIA. 4 times in the scene
[State of RL/Reasoning] IMO/IOI Gold, OpenAI o3/GPT-5, and Cursor Composer — Ashvin Nair, Cursor
- ▶ 30:51 Ashvin Nair It was like, Deep Seek shows that NVIDIA chips are actually more useful than previously thought, and, like, NVIDIA's stock, like, goes down a bunch. 4 times in the scene
Steve Yegge's Vibe Coding Manifesto: Why Claude Code Isn't It & What Comes After the IDE
- ▶ 1:44 Steve Yegge Now you saw, I don't know if you saw Jordan Hubbard's post from Nvidia where he just laid out some really nice advice on how to get the most out of agents as you're coding.
- ▶ 16:16 Steve Yegge He's the one that wrote the article that crashed the stock market about Nvidia.
SAM 3: The Eyes for AI — Nikhila & Pengchuan (Meta Superintelligence), ft. Joseph Nelson (Roboflow)
- ▶ 4:50 Joseph Nelson Over 30 frames per second, for example, on like a small T four, or excuse me, small like edge device and hundreds of frames per second on like a T four. 2 times in the scene
- ▶ 9:28 unnamed speaker If I want a hundred detected objects on an H 200, obviously this is an H 200, but it's also like, this is impressively fast. 2 times in the scene
- ▶ 10:07 unnamed speaker So, so I'm reading in the paper, it's, uh, 10 objects on two HCOs, 28 on four HCOs, and 64 on eight HCOs, something like that. 3 times in the scene
⚡️Jailbreaking AGI: Pliny the Liberator & John V on Red Teaming, BT6, and the Future of AI Security
World Models & General Intuition: Khosla's largest bet since LLMs & OpenAI
- ▶ 29:07 Pim de Witte So even if you're, for instance, uh, simulating human behavior in Omniverse, because you're trying to create better training data for factory floors, um, you can use it.
After LLMs: Spatial Intelligence and World Models — Fei-Fei Li & Justin Johnson, World Labs
- ▶ 12:50 unnamed speaker Where, let's just say, you know, Nvidia has won, and we should just, you know, scale that out in infinity and write software to patch up any, any gaps we have in the, in the mix, right?
- ▶ 13:01 Justin Johnson Like, if you look at, if you look at the numbers, like, even going from Hopper to Blackwell, like, the performance per watt is about the same. 2 times in the scene
- ▶ 13:01 Justin Johnson Like, if you look at, if you look at the numbers, like, even going from Hopper to Blackwell, like, the performance per watt is about the same. 2 times in the scene
Anthropic, Glean & OpenRouter: How AI Moats Are Built with Deedy Das of Menlo Ventures
- ▶ 1:06:00 Deedy Das Kind of NVIDIA.
- ▶ 1:11:20 Alessio Fanelli And so it's like, okay, well, the amount of money being spent in this space is large enough to justify betting, like the same way Instagram was like one percent of Facebook market cap.
⚡️ The State of AI Engineer Hiring: Cheating, AI Adoption,Junior Devs — Vivek Ravisankar, HackerRank
- ▶ 4:56 Vivek Ravisankar There's everything from OpenAI, NVIDIA, Amazon, Salesforce, all of these, we work with all of the, all of the customers.
How Zyphra went all-in on AMD + Why Devs feel faster with AI but are slower — with Quentin Anthony
- ▶ 3:19 Quentin Anthony Um, we found that it's, it's great, uh, for flash attention to specifically, we were able to be H-one hundred. 2 times in the scene
- ▶ 4:32 Quentin Anthony Like, I would say MI-DX was not on the same level of eight 100.
- ▶ 4:39 Quentin Anthony So you basically had to split across, you know, you had a three level, uh, parallelism scheme instead of two level on NVIDIA, where you just had with within the node and across nodes.
- ▶ 5:32 unnamed speaker You basically have this kind of like these groups trying to make better software to make AMD just the same as Nvidia. 3 times in the scene
- ▶ 8:42 unnamed speaker Because I think the other question is, like, well, I'm gonna do all this work versus, like, I just write CUDA code that then, when the BG-G-Hundred comes online for my cluster, I'll just switch it over right away.
- ▶ 10:40 Quentin Anthony The ecosystems within NVIDIA and AMD are actually quite similar. 4 times in the scene
- ▶ 19:28 Quentin Anthony It doesn't necessarily have to be, they compare it to what NVIDIA does well, and they say, okay, well, it doesn't do that well,
⚡ Open Model Pretraining Masterclass — Elie Bakouch, HuggingFace SmolLM 3, FineWeb, FinePDF
- ▶ 2:50 Elie Bakouch Like, if you take this curve, uh, like, when it's mixed with, uh, with, like, uh, other, other web datasets, not PF-one, it basically has a very, very, very good performance compared to this, like, NemoTrans SCP-V-II, which is a very…
Why RL Won — Kyle Corbitt, OpenPipe (acq. CoreWeave)
- ▶ 48:53 Shawn Wang No, like I, I, I literally like after last week, I think maybe two weeks ago with the whole Oracle NVIDIA
Building Jamba 3B: the tiny Hybrid Transformer State Space Reasoning Model - Barak Lenz, CTO of AI21
- ▶ 5:27 Barak Lenz So we designed J to have a version that fits on a single GPU, a single AY 100 or H one, 80 gigabytes.
- ▶ 5:27 Barak Lenz So we designed J to have a version that fits on a single GPU, a single AY 100 or H one, 80 gigabytes.
- ▶ 9:37 Barak Lenz So I think NVIDIA released hybrid models and other companies started following.
- ▶ 13:35 Barak Lenz Jamba is that it starts with, with a large size, you know, Jamba mini is mini for enterprises, but it's not mini for the, for a developer that, you know, has a T four, has his own GPU and he wants to try stuff.
⚡️Raising $1.1b to build the fastest LLM Chips on Earth — Andrew Feldman, Cerebras
- ▶ 3:09 Andrew Feldman You think 20 times faster than Nvidia B 200 GPUs and it's, it's been an amazing run.
- ▶ 8:53 unnamed speaker You know, I think, like, what has been hard for a non-hardware person like myself to understand is, you know, like, a lot of people who are, who are sort of competing with NVIDIA and betting on, like, more on, uh, on, uh, on-chip memory… 3 times in the scene
- ▶ 26:06 Andrew Feldman I, uh, I, I think that, you know, 10 years ago, uh, NVIDIA was a twenty billion dollar company.
A Technical History of Generative Media
- ▶ 11:23 Batuhan Taskaya Right now, it's, like, much more competitive space, but, like, NVDA has, like, a fifty-percent, hundred-percent kernel team that's writing kernels.
- ▶ 15:25 Batuhan Taskaya Uh, when you use PyTorch with B-Torch's Blackwell chips, you're not getting the best performance.
- ▶ 22:50 Batuhan Taskaya And we, like, Kubernetes version at Google Cloud was fine in 2022 when we wanted to get eight A-one-hundreds. 2 times in the scene
- ▶ 23:15 Batuhan Taskaya And in, in this world, like, we had to build our orchestration layer, we had to build our own distributed file system, we had to build our own container runtimes, all, all the stack to make sure that the cold starts are extremely,…
- ▶ 23:57 unnamed speaker You keep mentioning H-one hundreds. 2 times in the scene
- ▶ 24:03 Batuhan Taskaya Blackwell is, is obvious, like, we, we have, like, we have five people dedicated to writing Blackwell kernels right now to make sure we can, like, because theoretically it looks good, right? 6 times in the scene
- ▶ 24:15 Batuhan Taskaya So we have a dedicated team that's, like, working with NVIDIA directly to write custom kernels for Blackwell for diffusion transformers to get to the, get to the point where it makes perf dollar make sense, and then, then we would start… 4 times in the scene
- ▶ 25:35 Batuhan Taskaya Uh, and like some of the, like, like B-Tree hundreds are gonna have, like, a better softmax instruction that gets, like, 1.5 X, whatever.
Better Data is All You Need — Ari Morcos, Datology
- ▶ 10:29 Alessio Fanelli NVIDIA is like four trillion, and SKIL is not four trillion, so what do you think there's that inefficiency?
⚡️Accelerators @ 3x NVIDIA H200 perf, Made in the USA - Thomas Sohmers + Mitesh Agrawal, Positron AI
- ▶ 7:46 Alessio Fanelli And maybe contrapose that both to, you know, NVIDIA and the kind of traditional ones, as well as Grok, obviously you work there, Cerebras, and then there's kind of like the long tail of all the other GPU alternatives things, but just give…
- ▶ 12:14 Shawn Wang I mean, just because most people are familiar with Nvidia H series. 6 times in the scene
- ▶ 12:14 Shawn Wang I mean, just because most people are familiar with Nvidia H series.
- ▶ 14:44 Thomas Sohmers And the A 100 comparison here is interesting because in most of these cases, they're actually, they,
- ▶ 14:53 Thomas Sohmers Percentage of theoretical memory bandwidth actually has gotten worse generation over generation, and we'll see exactly where Blackwell ends up, but all indications are, even though they, you know, more than doubled the theoretical memory…
- ▶ 14:53 Thomas Sohmers Percentage of theoretical memory bandwidth actually has gotten worse generation over generation, and we'll see exactly where Blackwell ends up, but all indications are, even though they, you know, more than doubled the theoretical memory…
- ▶ 17:59 Mitesh Agrawal Look, from a marketing term, we call it like falling within the NVIDIA ecosystem, but like from both of our past experiences, I can tell you that most of the, the silicon providers, you know, whatever technology they might come up with or,… 6 times in the scene
- ▶ 27:29 Thomas Sohmers So, but, in, NVIDIA's TF-thirty-two number format is a nineteen-bit number format. 2 times in the scene
- ▶ 34:06 Thomas Sohmers Like I would say just in my talking with, um, you know, the, the founders and people in, in all of the different semiconductor, you know, startups trying to go after NVIDIA. 6 times in the scene
- ▶ 38:59 Mitesh Agrawal So, like, like NVIDIA, we want to sell systems. 3 times in the scene
- ▶ 41:22 Thomas Sohmers Best relative advantage over NVIDIA is on that generation or decode side of it.
⚡️Mercury: Ultra-Fast Diffusion LLMs — Estefano Ermon, CEO Inception Labs
- ▶ 25:19 Stefano Ermon And so you can find something on, in the literature as well from NVIDIA, from academic groups.
The RLVR Revolution — with Nathan Lambert (AI2, Interconnects.ai)
- ▶ 18:44 Nathan Lambert I think the NVIDIA Lama-Nimetron reasoning paper is probably the
🕰️ The Oral History of Windsurf (ft. Varun Mohan, Scott Wu, Jeff Wang, Kevin Hou, Anshul R)
- ▶ 45:27 Varun Mohan So I guess for, for training, you're right in that it is actually nuts to think about how insane the networks are for Nvidia's most recent hardware.
- ▶ 45:36 Varun Mohan It's like for the H 100 boxes, you shove eight of these H 100 on a machine between two nodes. 3 times in the scene
The Shape of Compute (Chris Lattner of Modular)
- ▶ 2:23 Chris Lattner And so we need to be state of the art on NVIDIA GPUs meeting and beating NVIDIA's best on things like a Lana three model, which by the way is serving end to end, like very high bar, by the way, this is like 2 times in the scene
- ▶ 4:08 Chris Lattner It ran just on a 100, just one model, but it had state of the art performance. 2 times in the scene
- ▶ 5:03 Chris Lattner Let's, oh yeah, let's add H 100 support. 2 times in the scene
- ▶ 5:27 Chris Lattner And as you do that in Blackwell, like all this stuff is like all now in the product.
- ▶ 9:05 Chris Lattner NVIDIA's got hundreds or thousands of people working on it.
- ▶ 11:34 Chris Lattner So it's not as good as something like VLLM because it's missing some features, and it only supports NVIDIA and AMD hardware, for example. 4 times in the scene
- ▶ 14:28 Chris Lattner it turns out that, uh, an H-one hundred and AMD chip are actually quite different.
- ▶ 24:38 Chris Lattner We support Google TPUs and Infantria and AMD and NVIDIA, obviously, and CPUs and this and that and the other thing. 3 times in the scene