SambaNova Systems closes $1B round at $11B valuation, targets enterprise on-prem AI inference

Speaker 2: Me tell you about Shopify. Shopify is the commerce platform that grows with your business and lets you sell in seconds online, in store, on mobile, on social, on marketplaces, and now with AI agents. And our next guest is Rodrigo Leong from SambaNova. The cofounder and CEO is here with us on the TV Guide for Realm. Rodrigo, how are you doing? I'm doing good. How are you guys? We're doing fantastically, but it sounds like you're doing better. Give us the news. What happened?

Speaker 5: You know, we're super excited. We did a big big funding announcement today with a billion dollar round that we did a first close on at at an 11,000,000,000 valuation. So super excited about announcing that. Incredible. There

Speaker 2: we go. There

Speaker 5: we go. There we go.

Speaker 1: This reminded me there was somebody posted, like, a couple weeks ago something to the effect of, like, every day there's a new billion dollar deal in chips from some company you've never heard of. Market. I guess the trend the trend continues. Yeah. Talk about it. Yeah.

Speaker 2: Yeah. Is this an overnight success? When did you start the company? How long has this been overdue? It's

Speaker 5: an overnight nine year success. Know, I've been I've building chips for thirty years, a high performance product, and we started this company in 2017. So no, but it's been a great journey. It's just, again, so noisy. So there's so many companies out there doing all sorts of awesome things, but look, Inference is now right in front of us. It's kind of broken everything open. It's what we do, and it's been what we've done for a long time. The new training run, and so people that are kind of been thinking about building just for training, well, now it's the time for inference. This is where we shine, and we shine, and investors are putting all their money behind that, and so we're excited about that. How how

Speaker 2: walk me through the the phases of I mean, you have some of the biggest companies in the world you're working with. JPMorgan Chase selected you for secure on prem AI inference. And I'm interested in the thought process that large Fortune 500 companies, Fortune 100 companies are going through right now. I imagine that there's a lot of explore with frontier models, very expensive. They're token maxing. Then they shift to, okay, this agentic workflow is working. Let's use an open source model for it that is, you know, either the open source frontier is caught up and can do this particular task. Maybe there's a fine tune or some RL on some private data that makes the the the system perform better. When is the conversation shifting to actually actually optimizing the inference stack, the hardware? What are what what is what's the conversation like there? What are the savings that people are focused on?

Speaker 5: Well, I think what people are starting to realize after they started using ChatGPP and Yeah. Drop the the frontier models is, there's a bunch of things I can do there, and it's great. You know, we'll use it, and it's going to be part of their business for a long, long time. Mhmm. Right? And hyperscale is offering all sorts of different services with different GPUs and GPUs and training chips. They're offering different things there, again, on the cloud. What's happening now is this next phase of production is people starting to realize, okay, what happens to the stuff that I'm not comfortable releasing to these models? Like my private data, my security, the stuff that I want your models to learn and then somehow become part of the global knowledge, right? And so what happens with that? Right? And so I don't know if I have, you know, you know, my clients' banking information, you know, I have, you know, some some very regulated information. What do I do with that? Right? And so people are starting to realize, okay, there is a whole another class of workloads. Whole another class of applications that you've got to go and do private. With someone like JPMorgan, which is fantastic, they are the top of the top when it comes to enterprise and IT and technology, fantastic partners. They selected us as the ones that can come in and go into an environment like JPMorgan, do full on prem secure private. They can bring their open source models. They can fine tune into it. They can deploy that privately security and control the models and control it forever. So I think we're starting to see the landscape starting to steady, right? There's some things you use the Anthropix for, there's some things that you're going to use the hyperscale clouds for, and there's some things you're going to say, Hey, let me run on prem and let me control the outcome. Especially since I don't know how the regulatory environment is going to settle, let me just be secure for that. Let me just be safe and just run those things on my own models in my own environment with my own security around it and then be done. Yeah. You can think, you know, pharma company being like, would love for all the other large pharma companies to share their data Yeah. With with the Frontier Lab. Totally. But I'll I'll pass. Mhmm.

Speaker 2: Does what does partnership with you look like for a large organization? If I have, you know, a large IT footprint already, I probably have data centers, but they might be more CPU based workloads. Maybe I have some GPUs, but I'm not doing, frontier inference. Not fully scaled up, but I have some are are you consulting with them at the level of, like, where they should be buying land for their next data center? How they set up a powered shell? Who they should partner with? Like, How deep in the stack will you go with a company that you're working with?

Speaker 5: Well, look, if you look at the AI ecosystems up to date, we've been really focused on hyperscale clouds, putting out the neo clouds. The frontier models, we're really focused on building those out, you know, gigawatts here, megawatts there. I mean, you know, just this large scale deployment. And then what you're still thinking about is, well, how do I get as many tokens out per, you know, megawatt per kilo you know, kilowatt, megawatt, and gigawatt that I have. Right? And so that's kind of we're very focused on that. Some of them will play into that are really, really efficient models. We run the big models really fast. That's really what we're known for. Mhmm. Huge models. Right? You know, the battleground for power and data center and all that is about huge models. It's not about the little models, it's about the trillion, multi trillion per model models. That's what we focus on and we run that faster than anybody else in the world. But then you flip it over to the enterprise, and for them, it's not as much about how many thousands of racks are going to fit into an environment. It's about data privacy. It's about security. It's about having the capacity when I want it. It's about low latency. It's about these things that, again, you go back to traditional computing. It's what they've always wanted. They always wanted low latency or high performance, all security They and always wanted that. What we can do is go on over is that because our our rack is a 10 kilowatt air cooled rack compared to what traditional GPs are 140 kilowatts per rack. With 10 kilowatt air cooled rack producing state of the art AI. Sure. Now you can go into your existing data centers. Don't need to go buy new data. You already have data centers. Roll out the old gear, bring in the new gear, off we go, right? And you're running the latest and greatest models at great performance. So that's kind of what's exciting about it. You know, there are first and foremost, our customer, you know, they're buying racks and deploying them, but then broadly, they're really also a co developer when it comes to enterprise and you secure AI for enterprise, right? Because they are the most knowledgeable when it comes to deploying these types of applications. They're just so smart about this stuff. And so we can partner with them and figure out how to bring the enterprise on board, which again, if we all think about it, there's three classes of AI clients. You've got the model makers. You've the clouds, and people forget, enterprise is just waking up. Historically, there have been a massive amount of spend. Mass, Right? And so they they're just looking at when you see at Jake Morgan, some of the banks, and some of the the other retails, some to come aboard. And I think that's gonna be a part of the ecosystem. I think it's gonna be something that's gonna be around. Yeah. Last question for me. Jordy, you can take whatever after. But

Speaker 2: walk me through the pre chat GPT era for your company. I imagine that since 2022, 2023, it's been on fire, and and and demand has been through the roof. But how did the company survive from 2017 to 2022? What were you doing? Who were the customers?

Speaker 5: Did you have revenue? Like, what was was it all r and d and venture funding? Like, what was the what was the early history of the company? Because that felt that feels like a very potentially rough period, but how did you get through that that that time? Well, look. No. I mean, I mean, I've been in chip and design for a long time. Well, I always know that the long term investment. And so, I mean, back in 2017, we're still trying to recognize cats and dogs on the Web. It's just a cat or a dog. That's what we're doing. We're trying to figure out what do we do with this thing. We're building recommender models. We've also submitted a thing for people, but the reality is what OpenAI did, it just fleshed out all of that noise pre OpenAI and focused all the value around, let's start here. It doesn't mean that images aren't important. Doesn't mean voice mail. All these other things are But what it did is focused us as a global market to say, let's start here. Let's just start with language. And it's a very good place to start, and that allowed us to actually go all of us can come come together to say hardware, software, models, everybody start focusing in on language as the use case. And now you've got so much production traction, and now you can focus on other things. Like, we're doing great in voice, and we're doing great in video. These are things that are also coming, but the ability to sequence these things in production so that you can actually see value has been incredibly important. Love it. On on that note,

Speaker 1: going forward, how are you thinking about, you know, reacting to customer needs? Like this partnership with with JPM and some of your other, you know, new customers feels like reacting to this, real time need of, like, okay, we understand the potential. We're willing to invest a lot here, but we have certain kind of criteria around how we wanna roll these products out versus skating to where the puck is going and trying to make predictions around r and d that you do today that will pay dividends three, four, five years out.

Speaker 5: Yeah. Well, when your chip design, as you guys know, it takes you two years to design and you've to go through TSMC, you've got to build these wafers, and you've got to build the systems all mean, there's a three or four year cycle. Right? So you're always it's a it's a blend between engineering and, fortune telling. Right? And so you're kind of mixing those. But but the reality the reality is you're always doing that. But here's the difference when when it comes to enterprise. And we we you know, not only we have JPMorgan, but last month, we announced a huge partnership with Vista Equity, has 92 portfolio companies building all these applications. And so what is the biggest thing that we run out with AI is we don't have offtakes. We don't have offtakes. Now what you're seeing is the JP Morgans of the world, the Empress of the world, the Vistas of the world, all these applications, all turning SaaS companies into AI first companies, and you're seeing the demand pull through. What we're excited about this is not only the web, the chips. You got connected with the data center that's there. Oh, the next thing is connected with the end user, the end off taker that applies AI into something that's useful to new businesses. And so so we're super excited about the JPMorgan, the Vista, all of these type of That's our that's our mentality is what are people using it for? Solve that problem. Everything underneath follows. Mhmm.

Speaker 2: Thank you so much for coming on the show. Great. Thank Congratulations on the massive round. Nine year overnight success. Can't wait to talk to you again soon. Have a great rest of your week. Thank you. Talk to you soon. Goodbye.