Venture with Grace
00:24:36 · Jerry Tworek, Grace Gong
[00:24:36] S2: First and foremost, we are a research company, so largely working on delivering the breakthrough technology even before thinking about commercialization. I don't want to participate in the existing agents' market of training transformers and deploying them. It's not what we set out to do. So first, we need to build what we want, and then we can deploy it to the world. When I am largely thinking about enterprise customers and the companies that are interested and willing to undergo the internal transformation of moving the business towards more and more automated way of doing things. And again, it will be gradual. It will be step by step. But businesses already are playing with using agents in various role functions, and I would like to offer them for our different technology something that is just as much better for their use cases than than the than the existing setup. And my ideal way of charging it would be per rack. I a lot of a lot of businesses, they [00:25:40] S1: Per rack? [00:25:40] S2: Per... They they like having hardware on prem. Mhmm. And I would like to say this is a GB 300 and NVL72 to rack from NVIDIA that you can put in your data center. We put their Core Automation software, and that software will learn from your data and can perform all the business functions that you need in your company. This what we would like to do. And, like, I don't think charging per token probably makes sense in some cases, but also very often users are usage conscious and charging per token suddenly makes you feel bad.
00:25:40 · Jerry Tworek, Grace Gong
[00:25:40] S2: Per... They they like having hardware on prem. Mhmm. And I would like to say this is a GB 300 and NVL72 to rack from NVIDIA that you can put in your data center. We put their Core Automation software, and that software will learn from your data and can perform all the business functions that you need in your company. This what we would like to do. And, like, I don't think charging per token probably makes sense in some cases, but also very often users are usage conscious and charging per token suddenly makes you feel bad. [00:26:19] S1: Please, we're streaming. Sorry. Oh my god. My bad. I didn't know this was like... I mean, we only came... I would typically only ran this place for dinner. So typically, these kind of things don't bother us. But today, it's been... Everything has been happening. So... Which makes the show very diverse. [00:26:37] S2: It is. It is diverse. [00:26:38] S1: We only got interrupted sometimes. Sorry. Okay. So... That's that's okay. Yeah. The Parag. The rack.
00:26:38 · Grace Gong, Jerry Tworek
[00:26:38] S1: We only got interrupted sometimes. Sorry. Okay. So... That's that's okay. Yeah. The Parag. The rack. [00:26:45] S2: It was like per per rack. Yeah. Per rack billing model where, like, you basically buy your own hardware Mhmm. With your own capacity. It is only serving for you. And because the model is customized, that rack has a memory. And that memory integrates the data from your business. And it's used for your usage. And you know that your data doesn't leave your systems. It is not something that goes to some other company that you never heard of and it's hard to trust. So I think it's a good business model of the future. In the end, compute drives it. And we want to be kind of like an extra layer on top of NVIDIA chips that's help you achieve your business goals. [00:27:26] S1: Let's talk about NVIDIA. You guys have a great relationship with NVIDIA, apparently. So some people are both investor and client at the same time. So how how do you kind of, like, think about the NVIDIA layer? So obviously, there are a few people on our podcast also are building NVIDIA's layer. Obviously, NVIDIA is still one of the biggest provider in the market. So what's your thought process on these other kind of players in the industry, their approach to GPUs? And how do you think about maybe expanding the solution to different things as well?
00:27:26 · Grace Gong, Jerry Tworek
[00:27:26] S1: Let's talk about NVIDIA. You guys have a great relationship with NVIDIA, apparently. So some people are both investor and client at the same time. So how how do you kind of, like, think about the NVIDIA layer? So obviously, there are a few people on our podcast also are building NVIDIA's layer. Obviously, NVIDIA is still one of the biggest provider in the market. So what's your thought process on these other kind of players in the industry, their approach to GPUs? And how do you think about maybe expanding the solution to different things as well? [00:28:11] S2: Yeah, yeah. That's a great question. First and foremost, at least today, NVIDIA builds just the best training chips in the market, and that's why we are very happy to be working with them. There are many companies trying to compete in a hardware market. And it's hard for me to say much about how that will play out. I'm just not an expert in the hardware market. I'm always very happy to be using the best hardware of the day and make sure our models will be running very well in there. There a reason why we didn't choose to build our own hardware. We are willing to devise and develop all of the deep learning stack and all of the ways of training models. But the hardware layer is where we are starting. So we take the existing chips. And our goal is try to figure out how to develop the best model with the maximum amount of learning capabilities on the existing hardware, which largely is built to execute matrix multiplications very, very fast.
00:28:11 · Jerry Tworek, Grace Gong
[00:28:11] S2: Yeah, yeah. That's a great question. First and foremost, at least today, NVIDIA builds just the best training chips in the market, and that's why we are very happy to be working with them. There are many companies trying to compete in a hardware market. And it's hard for me to say much about how that will play out. I'm just not an expert in the hardware market. I'm always very happy to be using the best hardware of the day and make sure our models will be running very well in there. There a reason why we didn't choose to build our own hardware. We are willing to devise and develop all of the deep learning stack and all of the ways of training models. But the hardware layer is where we are starting. So we take the existing chips. And our goal is try to figure out how to develop the best model with the maximum amount of learning capabilities on the existing hardware, which largely is built to execute matrix multiplications very, very fast. [00:29:12] S1: Mhmm. Thinking about, I guess, like, your day to day as a CEO, what's the difference between, like, being the researcher yourself to, like, running a company? Because I I would assume, like, seems like you would enjoy research.
00:29:30 · Jerry Tworek
[00:29:30] S2: I do enjoy research a lot. It's one of my favorite things to do, obviously, being a CEO and building your own company. It does require a of other things. Like, one thing is you need to secure funding for a company. You need to be somewhat external facing. And you need to be able to tell your story and explain to all of other people that AI companies of the day that don't really exist in a vacuum. How do you fit into the ecosystem and how you relate to other companies is also very important. And there are also a lot of operational things that you need to do in some way when you are a CEO of a company. Everything is your problem, everything is your responsibility, not just research. Research is probably one of the things I care about the most, but I have a lot of other headaches and a lot of other things I need to worry about every day that are just beyond research. But in some way, it is an extremely enabling thing to do, to try to design something that you want to be built, which is this new type of model, and then try to bring it to the world. The reality we live in is that starting a new company is one of the best ways to achieve certain technological goals. And then companies are not just models, not just training, but everything that needs to come together, it's an organism. That needs to function very well. And that's what we are doing.