A French startup has raised a huge seed investment to “re-architect computing infrastructure” for developers who want to build and train AI applications more efficiently.
FlexAIas the company is called, has been operating in secrecy since October 2023, but the Paris-based company officially launched on Wednesday with 28.5 million euros ($30 million) in funding while teasing its first product: an on-demand cloud service for AI Training.
That’s a hefty chunk of change for a seed round, which usually means substantial founder pedigree — and that’s the case here. Co-founder and CEO of FlexAI Brijesh Tripathi he was previously a senior design engineer at GPU and now AI giant Nvidia, before landing various senior engineering and architect roles at Apple. Tesla (works directly under Elon Musk). Zoox (before Amazon acquired the self-driving startup). and, most recently, Tripathi was Vice President of Intel’s AI and supercomputing platform division, AXG.
Co-founder and CTO of FlexAI Dali Kilani he also has an impressive resume, serving in various technical roles at companies such as Nvidia and Zynga, and most recently held the role of CTO at French startup Lifen, which develops digital infrastructure for the healthcare industry.
The round was led by Alpha Intelligence Capital (AIC), Elaia Partners and Heartcore Capital, with participation from Frst Capital, Motier Ventures, Partech and InstaDeep CEO Karim Beguir.
FlexAI team in Paris Image credits: FlexAI
The computational puzzle
To understand what Tripathi and Kilani are attempting with FlexAI, it’s first worth understanding what AI developers and practitioners face when it comes to accessing “compute.” This refers to the processing power, infrastructure, and resources required to perform computational tasks, such as processing data, running algorithms, and running machine learning models.
“Using any infrastructure in the AI space is complex. it’s not for the faint of heart and it’s not for the inexperienced,” Tripathi told TechCrunch. “It requires you to know a lot about how to build infrastructure before you can use it.”
Instead, the public cloud ecosystem that has evolved over these past two decades serves as a good example of how an industry has emerged from the need for developers to build applications without worrying too much about the back end.
“If you’re a small developer and you want to write an app, you don’t need to know where it’s running or what the backend is — you just spin up an EC2 (Amazon Elastic Compute cloud) instance and we’re done,” Tripathi said. “You can’t do that with AI computing today.”
In the realm of artificial intelligence, developers must figure out how many GPUs (graphics processing units) they need to connect to which type of network, which is managed through a software ecosystem that they are entirely responsible for setting up. If a GPU or network fails, or if something in that chain goes wrong, the onus is on the developer to sort it out.
“We want to bring AI computing infrastructure to the same level of simplicity that the general purpose cloud has reached — after 20 years, yes, but there’s no reason why computational AI can’t see the same benefits,” he said Tripathi. “We want to get to a point where running AI workloads doesn’t require you to become a data center expert.”
With the current iteration of its product going through its paces with a few beta customers, FlexAI will release its first commercial product later this year. It’s basically a cloud service that connects developers to “virtual heterogeneous computing,” meaning they can run their workloads and develop AI models on multiple architectures, paying per usage instead of renting GPUs on a dollar-per-hour basis .
GPUs are vital cogs in AI development, serving to train and run large language models (LLMs), for example. Nvidia is one of the prominent players in the GPU space and one of the main beneficiaries of the AI revolution that sparked OpenAI and ChatGPT. In the 12 months since OpenAI released an API for ChatGPT in March 2023, allowing developers to integrate ChatGPT functionality into their own apps, Nvidia’s stock has risen from about $500 billion to more than $2 trillion.
LLMs are now pouring out of the tech industry, with demand for GPUs skyrocketing in tandem. But GPUs are expensive to run, and renting them for smaller jobs or ad-hoc use cases doesn’t always make sense and can be prohibitively expensive. That’s why AWS deals in time-limited rentals for smaller AI projects. But a rental is still a rental, so FlexAI wants to remove the underlying complexities and let customers access AI in computing on an as-needed basis.
“Multicloud for AI”
FlexAI’s starting point is that most developers don’t Really they mostly care about whose GPU or chip they use, be it Nvidia, AMD, Intel, Graphcore or Cerebra. Their main concern is to be able to develop their AI and create applications within their budget constraints.
This is where FlexAI’s idea of ”universal AI computing” comes in, where FlexAI takes the user’s requirements and distributes them to whatever architecture makes sense for the task, taking care of all the necessary conversions across platforms, either Intel’s Gaudi infrastructure, Rocm by AMD the Nvidia’s CUDA.
“This means that the developer only focuses on building, training and using models,” Tripathi said. “We take care of everything from below. Failures, recovery, reliability, it’s all handled by us and you pay for what you use.”
In many ways, FlexAI aims to fast-track for AI what has already happened in the cloud, which means more than replicating the pay-per-use model: It means being able to go “multi-cloud” based on the different benefits of different GPU and chip infrastructures.
FlexAI will channel a customer’s specific workload according to their priorities. If a company has a limited budget for training and refining their AI models, they can set it up on the FlexAI platform to get the most compute for their buck. This might mean going through Intel for cheaper (but slower) computations, but if a developer has a small run that requires the fastest possible output, then it can be channeled through Nvidia.
Under the hood, FlexAI is basically a “demand aggregator”, renting the hardware itself through traditional means and, using its “powerful connections” with people at Intel and AMD, secures premium prices that it distributes to its own base customers. That doesn’t necessarily mean you’ll be bypassing Nvidia, but it probably means that to a large extent — with Intel and AMD battle for GPU scrap left behind Nvidia — there’s a huge incentive for them to play ball with aggregators like FlexAI.
“If I can make it work for customers and bring tens to hundreds of customers into their infrastructure, they [Intel and AMD] I will be very happy,” Tripathi said.
This contrasts with similar GPU cloud players in the space, such as the well-funded CoreWeave and Lambda Labswhich focus squarely on Nvidia hardware.
“I want to take AI computing to where the current general purpose cloud computing is,” noted Tripathi. “You can’t multicloud on AI. You have to choose specific hardware, number of GPUs, infrastructure, connectivity and then maintain it yourself. Today, that’s the only way to really get AI computing.”
When asked who the exact launch partners are, Tripathi said he was unable to name all of them due to lack of “formal commitments” from some of them.
“Intel is a strong partner, certainly providing infrastructure, and AMD is a partner that provides infrastructure,” he said. “But there’s a second layer of partnerships happening with Nvidia and some other silicon companies that we’re not ready to share yet, but they’re all in the mix and the MOUs [memorandums of understanding] are signed at this time.”
The Elon Effect
Tripathi is more than equipped to take on the challenges ahead, having worked in some of the biggest technology companies in the world.
“I know quite a bit about GPUs. I used to make GPUs,” Tripathi said of his seven-year tenure at Nvidia, which ended in 2007 when he jumped ship for Apple as it launched the first iPhone. “At Apple, I focused on solving real customer problems. I was there when Apple started making their first SoCs [system on chips] for phones.”
Tripathi also spent two years at Tesla from 2016 to 2018 as chief hardware engineer, where he ended up working directly under Elon Musk in his last six months after two people above him abruptly left the company.
“At Tesla, what I learned and deal with in my startup is that there are no limitations other than science and physics,” he said. “The way things are done today is not how it should or should be done. You should follow the right thing to do from the first principles, and to do that, remove every black box.”
Tripathi was involved in Tesla’s move to make its own chips, a move that has since been emulated by GM and Hyundai, among other automakers.
“One of the first things I did at Tesla was figure out how many microcontrollers are in a car, and to do that we literally had to sort through a bunch of these big black boxes with metal shielding and casing around it. find these very tiny microcontrollers in there,” Tripathi said. “And we ended up putting it on a table, spread it out and said, ‘Elon, there are 50 microcontrollers in a car. And sometimes we pay 1,000 times the margins on them because they’re shielded and protected in a big metal case.’ And he says “let’s go make our own”. And that’s what we did.”
GPU as a guarantee
Looking further into the future, FlexAI has ambitions to build its own infrastructure, including data centers. This, Tripathi said, will be funded by debt financing, building on a recent trend that has seen competitors in the space including CoreWeave and Lambda Labs uses Nvidia chips as collateral to secure loans — rather than giving more equity.
“Bankers now know how to use GPUs as collateral,” Tripathi said. “Why give shares? Until we become a real computing provider, our company value is not enough to get us the hundreds of millions of dollars needed to invest in building data centers. If we only did equity, we disappear when the money is gone. But if we really tie it to GPUs as collateral, they can take the GPUs out and put them in some other data center.”
