The race to build out the world's artificial intelligence infrastructure has seen many newcomers emerge onto the scene, hoping to chip away at the hyperscalers' market dominance.

For Lambda Labs, the moment is less about chasing a trend, and more about making sure it can keep up with a boom it predicted more than a decade ago.

The company was set up in 2012 with an eye to driving down the costs of running AI models after its founders struggled with the costs of Amazon Web Services (AWS) in a previous venture. Lambda started by selling GPU-powered workstations and servers, and soon launched its own cloud service.

The Lambda GPU Cloud now operates out of colocation data centers in San Francisco, California, and Allen, Texas, and is backed by more than $820 million in funds raised just this year. As we go to press, another $800m is close to being finalized.

"20-25 years ago with the advent of the Internet, the software developer as a class came up and there are now millions of different software developers in the world," Mitesh Agrawal, Lambda's head of cloud, explains of the company's philosophy. "There are going to be millions of AI developers as well and we really want to work closely with them, we want to grow with them."

To catch what may prove to be a huge AI wave, Lambda is focusing on those developers early on. "Today, for example, Lambda has on our on-demand cloud, where you can go from entering your credit card to actually running a job from your GitHub or HuggingFace transformer library in two minutes," Agrawal says.

"If you go to AWS, because they have to think about so many types of customers, first you have to install SageMaker, then you have to install your environment, and so on.

"Lambda focuses only on GPU use cases for AI, initially for training and now going into inference. There is something to be said about a stack that allows you to spin up your application with a lot more ease."

Beyond simplicity and speed, another factor that works in the company's favor is cost. AWS, with huge historical margins to defend, can charge more than $12 for an on-demand H100 GPU per hour. Lambda currently charges $2.49 - although a price only comparison leaves it behind CoreWeave, which manages $2.23.

The challenge for Lambda, and others in its situation, is less about attracting developers with its ease and price. It's about keeping them if they grow. Cloud provider DigitalOcean focused on software developers in the pre-AI era, but saw customers 'graduate' to other cloud providers as they reached scale - leaving DO's growth stagnant.

"If you want to talk about longevity, like Lambda being around for 20, 30 years as a company, you do need to keep those startups that are growing," Agrawal says.

"That comes from more software features, as well as being aggressive on pricing. We're not public - we are very financially savvy and want to maintain a level of [financial] sustainability in the company - but we are not under pressure from financial or public markets where they have to make certain margins. Pricing does become a knob for us."

The company may "not be even there in features for graduations," Agrawal admits. "But we sacrifice some of the pricing margin, and then pick some of our major breadwinners and focus on the features they need to make sure they graduate with you," he says. "And then once they graduate with you, people see that, and other companies come in and do that."

That's the plan, at least. "Right now, the strategy is market capture,” Agrawal explains. “Deploy as much compute as possible and then, as these companies grow, we make sure that we're keeping in touch and following them and making sure that at least some of them are graduating with us."

Lambda Echelon Clusters Rack Crates
– Lambda Labs

It also hopes to collect more established businesses along the way, and boasts Sony, Samsung, and Intuitive Surgical as customers. “We really do think the world is going to have major AI companies, but there's also going to be a big, fat, long tail of existing enterprises that will adopt AI,” Agrawal says.

He continues: “There's so much utilization of older models and layers. Technology companies are first to adopt, financial services companies, pharmaceutical companies, and media and entertainment are getting into it. But insurance companies may just be starting. There's just so many layers of this that you can carve out niches here.”

The ambition, Agrawal says, is “to be the number one cloud for things like this,” and he believes there are big businesses in each of these sectors that could be within reach for his company. “You keep on adding [them to your service] as you grow and hopefully you get to a certain level of ‘too big to fail’ kind of setup,” he says.

For Lambda, and every other cloud provider, differentiation beyond price and some software features has become increasingly difficult in a hardware market wholly captured by Nvidia.

"If you think about it from a 40 foot view, you're getting the same Nvidia GPUs from AWS as CoreWeave, OCI, and us,” he says. “But AWS is a commodity market, the CPU market is commodity, and you can build an $80 billion ARR business out of that.”

For some, the key differentiator is scale. Microsoft has deployed exaflops of compute over the past couple of years, with a huge portion dedicated to its favored son OpenAI. Now, rumors swirl of a $100 billion, 5GW 'Stargate' mega data center project for the ChatGPT creator.

"I know that they have not commented on it, but they're going to do it," Agrawal says. "We heard about it before the media started reporting on it."

This has led to fears that, as models grow unfathomably large, only a few companies with near-bottomless resources will be able to keep up.

Agrawal takes a different view. "It's actually great that some company is going to spend $100bn for AI,” he says. “Especially given that AI is [likely] going to be great for humanity.”

For Lambda more specifically, Agrawal says that the potential project simply proves the immense value of building in the AI space. "It's a great market indicator that one of the smartest companies on the planet is willing to put in $100 billion," he says.

"Of course, we can't do $100 billion right out of the gate today. But there is a number there that we can do, and then we have an ambition that someday, maybe five years, maybe 50 years, Microsoft and Lambda will be similar size deployments."

Just as Microsoft is stretching itself to fund such an enormous deployment, "Lambda is deploying at a massive scale, because we do believe that the training runs are going to get larger."

While OpenAI and some of the other large AI teams will turn to these super-supercomputers, Agrawal believes there is a market for others looking for smaller systems. "And, once you deploy a big model, it doesn't mean you can't break it down to smaller ones," he says.

For now, despite some concerns raised by Goldman Sachs and others about the long-term costs of AI, the market appears to be willing to support both large and small AI deployments. The demand is just for as many GPUs as possible, as soon as possible.

This has led to an imperfect allocation of customer funds to lesser cloud providers. “If your cloud products suck, but the customer has exhausted all avenues to find a provider - so, say Lambda doesn't have capacity right now - they will go to the shittier cloud,” Agrawal says.

“That's the market in which we are in, the demand curve is higher than supply. We anticipate that not just for six months, 18 months, but for as far as we can see: Three years, four years, five years in terms of both training and inference demand.”

Agrawal foresees breakneck growth for some time yet, even as the US grid struggles to keep up. “Look, it's easy to get swept up in it all,” he says. “You hear Elon [Musk] saying the next model requires 100,000 GPUs, or you hear about [OpenAI video generator] Sora and how many hours of GPU time it uses.”

But, he argues, “when you think about the compute demand and the unfathomable amounts of GPUs and power, we believe it is going to explode. We are so confident about the space and about extracting value in the space.”

The Silicon Valley company has built its business on predicting this boom, and believing that it will last.

“We, as AI engineers, believe in it. We are here for the long term,” Agrawal says. “We want to contribute and make an impact by accelerating human progress through AI.”