Lambda raises $24.5M for AI-optimized hardware infrastructure

Lambda, an AI infrastructure company, this week announced it raised $15 million in a venture funding round from 1517, Gradient Ventures, Razer, Bloomberg Beta, Georges Harik, and others, plus a $9.5 million debt facility. The $24.5 million investment brings the company’s total raised to $28.5 million, following an earlier $4 million seed tranche.

In 2013, San Francisco, California-based Lambda controversially launched a facial recognition API for developers working on apps for Google Glass, Google’s ill-fated heads-up augmented reality display. The API — which soon expanded to other platforms — enabled apps to do things like “remember this face” and “find your friends in a crowd,” Lambda CEO Stephen Balaban told TechCrunch at the time. The API has been used by thousands of developers and was, at least at one point, seeing over 5 million API calls per month.

Since then, however, Lambda has pivoted to selling hardware systems designed for AI, machine learning, and deep learning applications. Among these are the TensorBook, a laptop with a dedicated GPU, and a workstation product with up to four desktop-class GPUs for AI training. Lambda also offers servers, including one designed to be shared between teams and a server cluster, called Echelon, that Balaban describes as “datacenter-scale.”

(Lambda’s Balaban presented at VB Transform this week, as part of the event’s innovation showcase of cool, emerging technologies. See Balaban’s presentation above.)

“We initially created a facial recognition API, a deep learning-powered image editor called Dreamscope, and, because of the experience of running our own GPU infrastructure, decided to offer preconfigured workstations and servers,” Balaban told VentureBeat via email. “We provide the infrastructure — laptops, workstations, and servers — so that our customers can focus on training models and building value for their customers. Most companies get distracted by building huge infrastructure teams when they should be building huge machine learning teams to use infrastructure that’s easier to manage.”

Above: One of Lambda workstations.

Image Credit: Lambda Labs

Software plus hardware

A number of startups offer preconfigured hardware for AI development, including Graphcore. But Balaban says Lambda’s major differentiator is its software tools.

Every Lambda machine comes preinstalled with Lambda Stack, a collection of machine learning software development frameworks, including Google’s TensorFlow and Facebook’s PyTorch. Developers can update the frameworks with a single console command, and if they’ve trained the model on a local machine, they can copy it up to a Lambda server running in the cloud.

“Our customers include Apple, Intel, Microsoft, Amazon Research, Tencent, Kaiser Permanente, MIT, Stanford, Harvard, Caltech, and the Department of Defense,” Balaban said. “We have [thousands] of users, [and] most of the Fortune 500 and almost every major research university in the U.S. — as well as [many of] the research labs at the Department of Transportation and Department of Energy — use Lambda hardware and Lambda Stack.”

Balaban also claims that the over-40-employee company, which was founded in 2012, has been GAAP (generally accepted accounting principles) profitable since 2019. It’s on a $60 million revenue run rate for 2021 and plans to have around 60 employees by the end of the year.