SambaNova CEO on Raising $1B at $11B: "It's a Land Grab Right Now"

**Rodrigo Liang** (0:00)
We just did the first close of a billion dollar fund raise at an 11 billion valuation. I've been in this industry for 32 years, built in high performance trips for a long time. I've never seen the interest in semiconductors higher. Now what you're seeing at scale with Anthropic and with OpenAI and with Gemini, you've got millions and millions of people using it every day. We released SN40 a couple years ago. It became incredibly popular because instead of a 130, 140 kilowatt rack of Nvidia GPU, we were outperforming it with a 10 kilowatt SN40 rack. You could take a trillion parameter model and run it in a single rack, where it would take dozens of racks of other people's equipment to run the same model. We're two and a half billion dollars raised in the history of the company, and there aren't really that many companies that have raised into the multiple billions. It's all about scale. It's all about who can get to scale faster.

**Molly O'Shea** (0:59)
Rodrigo, welcome to Sourcery.

**Rodrigo Liang** (1:01)
Thanks for having me.

**Molly O'Shea** (1:02)
Well, we're here for Context. We're in Paris right now for the RAISE Summit. And right now, we're sitting right in front of where the conference is. I don't really actually know what this park is called, but it's next to The Loop.

**Rodrigo Liang** (1:14)
Yeah.

**Molly O'Shea** (1:15)
I don't know if you know. You've been here a bunch, right?

**Rodrigo Liang** (1:18)
I've been here, but I'm not sure if I know exactly the name of the park, but right in front of The Loop.

**Molly O'Shea** (1:24)
Well, you have some big news. I think this will come out about a week after the news drops, but we'll still make a clip on that. So what is the big news?

**Rodrigo Liang** (1:32)
Well, we're super excited. We just did the first close of a billion dollar fundraise at an 11 billion valuation.
This is a great show of momentum for the company and great show of support. The round was led by General Atlantic with a number of incredible investors that came in. Seligman Ventures, T. Rowe Price, Capital Group, these are all significant American investors that are coming in. That shows that the company's got momentum, we're driving towards the scale and a significant amount of capital infusion to help us do that.

**Molly O'Shea** (2:06)
All the energy right now is going into semiconductors. I'm sure this was a very hyped up round in some way or another, maybe it's been faster than others. What was the process like for you?

**Rodrigo Liang** (2:16)
I've been in this industry for 32 years, built in high-performance trips for a long time. I've never seen the interest in semiconductors higher. And I think it's a realization that chips at the center of this transformation. If you look at what AI is doing in the world and the build-ups of the data centers, you can't do it without chips that run and run efficiently. And so with SambaNova, we're coming in and providing technology that is able to take it to scale, take it to a level of inference scaling that's just really not that practical to achieve just with traditional GPUs. And so I think the world sees that and the excitement is coming in from some of the top investors in the world.

**Molly O'Shea** (3:00)
So where we are at today is inference. Inference has really taken the stage and it's been the next evolution of computing and where everything's going with AI. So for people that don't know SambaNova, can you walk through the products and how you've evolved them for inference?

**Rodrigo Liang** (3:17)
Yeah, I mean, this, look, with AI, you've got a sophisticated audience, so they know, with AI, there was always a training in the inference. There's no point of training a model if you aren't gonna inference it, if you're not gonna use it, right? And so the example I use with people is, you don't go and invent a search algorithm if you're never gonna do search, right? And so say we're not gonna train a model if you're not gonna use it and now we're in the phase of using these models.
We've always used them, we've always inferenced them, but it was still research to train models better and better. And so when we started the company in 2017, we're very focused on how we actually lower the cost of training, right? And back at the time, we're training models for image recognition. Can we tell the difference between dogs and cats? And, you know, can we recognize voices? Can we make voices? You know, we're doing all that research, but really in the end, inference wasn't really a problem yet because the number of people using it were very small. It was people trying to test the model that they trained. Now what you're seeing at scale with Anthropic and with OpenAI and with Gemini, and you're at scale, you've got millions and millions of people using it every day.