Deep learning engineers and researchers spend too much time managing their infrastructure. Fortune 500 companies, startups, and universities are forced to build huge teams to administer the exponentially growing compute resources required to train modern deep learning models. Billions of dollars are spent each year on these efforts. We want to build a world where a single researcher can manage petaflops of GPU compute. A world where training a model on a datacenter scale computer is as easy as training a model on your laptop. A world where access to computation is as effortless and ubiquitous as electricity.
To that end, I'm happy to announce that Lambda has secured $24.5M in financing to bring us closer to achieving these goals. This financing round consists of a $15M Series A equity round and a $9.5M debt facility that will allow us to grow Lambda GPU Cloud and expand our on-prem AI infrastructure software products.
What will Lambda use the money for?
First, we'll expand the cloud engineering team for the Lambda GPU Cloud. We’re building the fastest, least expensive, and easiest to use GPU cloud for deep learning. It's not a coincidence that Lambda GPU Cloud already ranks number three on a Google search for “GPU cloud”, behind only NVIDIA and Google itself.
Second, we’re hiring an engineering team to build deep learning infrastructure software that will simplify the management of our customers’ servers and clusters. We look forward to offering more great software like Lambda Stack.
Finally, the debt facility from Silicon Valley Bank will be used for capital expenditures related to our GPU cloud business and to satisfy the working capital requirements of our on-prem hardware business.
Who participated in the financing?
The Series A saw participation from 1517, Gradient Ventures, Bloomberg Beta, Razer, and Georges Harik. The debt facility is provided by Silicon Valley Bank. With the exception of Razer, all major funding parties were existing Lambda investors. We are grateful for their ongoing support. Lambda has a fantastic Board of Directors with Robert Youngjohns and Zach Bratun-Glennon and we're backed by some of the best investors and advisors in the world: 1517 Fund, Gradient Ventures, Bloomberg Beta, Razer, Scott McNealy, Austin Russell, Georges Harik, James Hong, James Cham, Jun Hong Heng, Nicolas Pinto, Gary Bradski, Ken Patchett, Randy Ching, Keller Rinaudo, and others.
Working with Razer
At first glance, it seems like an odd pairing. What does the world-famous gaming hardware company have to do with deep learning infrastructure? It comes down to a shared strategic vision. Razer's motto, “For Gamers. By Gamers.”, perfectly aligns with Lambda’s hyperfocus on building deep learning systems for engineers, by engineers. That shared customer obsession is what drove us to reach out to Min-Liang. He introduced us to the zVentures team and the rest was history. We can't wait to show you what's next.
At Lambda, we don't often spend time talking about our funding activities. Despite this being our third round of financing, it's the first time we've ever spoken about it publicly. We're heads down, talking with customers, writing code, manufacturing systems, deploying infrastructure, and simply building. That said, I'm glad that we took the time today to pull back the curtain and tell the world about what we're working towards.
Back to building.
If you, or someone you know, wants to work at Lambda, please apply via our careers page.