French startup FlexAI exits stealth with $30M to ease access to AI compute

A French startup has raised a hefty seed funding to “rearchitect compute infrastructure” for builders wanting to construct and prepare AI functions extra effectively.

FlexAI, as the corporate is known as, has been working in stealth since October 2023, however the Paris-based firm is formally launching Wednesday with €28.5 million ($30 million) in funding, whereas teasing its first product: an on-demand cloud service for AI coaching.

It is a chunky little bit of change for a seed spherical, which usually means actual substantial founder pedigree — and that’s the case right here. FlexAI co-founder and CEO Brijesh Tripathi was beforehand a senior design engineer at GPU large and now AI darling Nvidia, earlier than touchdown in numerous senior engineering and architecting roles at Apple; Tesla (working instantly below Elon Musk); Zoox (earlier than Amazon acquired the autonomous driving startup); and, most just lately, Tripathi was VP of Intel’s AI and tremendous compute platform offshoot, AXG.

FlexAI co-founder and CTO Dali Kilani has a powerful CV, too, serving in numerous technical roles at firms together with Nvidia and Zynga, whereas most just lately filling the CTO function at French startup Lifen, which develops digital infrastructure for the healthcare business.

The seed spherical was led by Alpha Intelligence Capital (AIC), Elaia Companions and Heartcore Capital, with participation from Frst Capital, Motier Ventures, Partech and InstaDeep CEO Karim Beguir.

FlexAI group in Paris Picture Credit: FlexAI

The compute conundrum

To know what Tripathi and Kilani try with FlexAI, it’s first value understanding what builders and AI practitioners are up in opposition to when it comes to accessing “compute”; this refers back to the processing energy, infrastructure and assets wanted to hold out computational duties corresponding to processing knowledge, working algorithms, and executing machine studying fashions.

“Utilizing any infrastructure within the AI area is complicated; it’s not for the faint-of-heart, and it’s not for the inexperienced,” Tripathi informed TechCrunch. “It requires you to know an excessive amount of about learn how to construct infrastructure earlier than you should use it.”

In contrast, the general public cloud ecosystem that has developed these previous couple of a long time serves as a positive instance of how an business has emerged from builders’ must construct functions with out worrying an excessive amount of concerning the again finish.

“In case you are a small developer and wish to write an software, you don’t must know the place it’s being run, or what the again finish is — you simply must spin up an EC2 (Amazon Elastic Compute cloud) occasion and also you’re completed,” Tripathi mentioned. “You possibly can’t do this with AI compute at present.”

Within the AI sphere, builders should work out what number of GPUs (graphics processing items) they should interconnect over what kind of community, managed by a software program ecosystem that they’re solely accountable for establishing. If a GPU or community fails, or if something in that chain goes awry, the onus is on the developer to type it.

“We wish to convey AI compute infrastructure to the identical stage of simplicity that the final objective cloud has gotten to — after 20 years, sure, however there isn’t any motive why AI compute can’t see the identical advantages,” Tripathi mentioned. “We wish to get to some extent the place working AI workloads doesn’t require you to turn out to be knowledge centre consultants.”

With the present iteration of its product going by its paces with a handful of beta prospects, FlexAI will launch its first business product later this 12 months. It’s principally a cloud service that connects builders to “digital heterogeneous compute,” which means that they’ll run their workloads and deploy AI fashions throughout a number of architectures, paying on a utilization foundation reasonably than renting GPUs on a dollars-per-hour foundation.

GPUs are very important cogs in AI improvement, serving to coach and run massive language fashions (LLMs), for instance. Nvidia is without doubt one of the preeminent gamers within the GPU area, and one of many principal beneficiaries of the AI revolution sparked by OpenAI and ChatGPT. Within the 12 months since OpenAI launched an API for ChatGPT in March 2023, permitting builders to bake ChatGPT performance into their very own apps, Nvidia’s shares ballooned from round $500 billion to more than $2 trillion.

LLMs are pouring out of the technology industry, with demand for GPUs skyrocketing in tandem. However GPUs are costly to run, and renting them from a cloud supplier for smaller jobs or ad-hoc use-cases doesn’t all the time make sense and might be prohibitively costly; because of this AWS has been dabbling with time-limited rentals for smaller AI projects. However renting remains to be renting, which is why FlexAI needs to summary away the underlying complexities and let prospects entry AI compute on an as-needed foundation.

“Multicloud for AI”

FlexAI’s start line is that the majority builders don’t actually look after essentially the most half whose GPUs or chips they use, whether or not it’s Nvidia, AMD, Intel, Graphcore or Cerebras. Their principal concern is having the ability to develop their AI and construct functions inside their budgetary constraints.

That is the place FlexAI’s idea of “common AI compute” is available in, the place FlexAI takes the person’s necessities and allocates it to no matter structure is smart for that individual job, taking good care of the all the mandatory conversions throughout the totally different platforms, whether or not that’s Intel’s Gaudi infrastructure, AMD’s Rocm or Nvidia’s CUDA.

“What this implies is that the developer is simply centered on constructing, coaching and utilizing fashions,” Tripathi mentioned. “We care for all the things beneath. The failures, restoration, reliability, are all managed by us, and also you pay for what you utilize.”

In some ways, FlexAI is getting down to fast-track for AI what has already been taking place within the cloud, which means greater than replicating the pay-per-usage mannequin: It means the power to go “multicloud” by leaning on the totally different advantages of various GPU and chip infrastructures.

For instance, FlexAI will channel a buyer’s particular workload relying on what their priorities are. If an organization has restricted price range for coaching and fine-tuning their AI fashions, they’ll set that throughout the FlexAI platform to get the utmost quantity of compute bang for his or her buck. This would possibly imply going by Intel for cheaper (however slower) compute, but when a developer has a small run that requires the quickest attainable output, then it may be channeled by Nvidia as a substitute.

Underneath the hood, FlexAI is principally an “aggregator of demand,” renting the {hardware} itself by conventional means and, utilizing its “robust connections” with the parents at Intel and AMD, secures preferential costs that it spreads throughout its personal buyer base. This doesn’t essentially imply side-stepping the kingpin Nvidia, however it presumably does imply that to a big extent — with Intel and AMD fighting for GPU scraps left in Nvidia’s wake — there’s a big incentive for them to play ball with aggregators corresponding to FlexAI.

“If I could make it work for patrons and produce tens to a whole lot of shoppers onto their infrastructure, they [Intel and AMD] might be very comfortable,” Tripathi mentioned.

This sits in distinction to related GPU cloud gamers within the area such as the well-funded CoreWeave and Lambda Labs, that are centered squarely on Nvidia {hardware}.

“I wish to get AI compute to the purpose the place the present basic objective cloud computing is,” Tripathi famous. “You possibly can’t do multicloud on AI. It’s a must to choose particular {hardware}, variety of GPUs, infrastructure, connectivity, after which preserve it your self. At present, that’s that’s the one solution to truly get AI compute.”

When requested who the precise launch companions are, Tripathi mentioned that he was unable to call all of them on account of an absence of “formal commitments” from a few of them.

“Intel is a powerful accomplice, they’re undoubtedly offering infrastructure, and AMD is a accomplice that’s offering infrastructure,” he mentioned. “However there’s a second layer of partnerships which might be taking place with Nvidia and a few different silicon firms that we’re not but able to share, however they’re all within the combine and MOUs [memorandums of understanding] are being signed proper now.”

The Elon impact

Tripathi is greater than geared up to take care of the challenges forward, having labored in a number of the world’s largest tech firms.

“I do know sufficient about GPUs; I used to construct GPUs,” Tripathi mentioned of his seven-year stint at Nvidia, ending in 2007 when he jumped ship for Apple because it was launching the first iPhone. “At Apple, I grew to become centered on fixing actual buyer issues. I used to be there when Apple began constructing their first SoCs [system on chips] for telephones.”

Tripathi additionally spent two years at Tesla from 2016 to 2018 as {hardware} engineering lead, the place he ended up working instantly below Elon Musk for his final six months after two individuals above him abruptly left the corporate.

“At Tesla, the factor that I discovered and I’m taking into my startup is that there are not any constraints aside from science and physics,” he mentioned. “How issues are completed at present is just not the way it must be or must be completed. It’s best to go after what the proper factor to do is from first ideas, and to try this, take away each black field.”

Tripathi was concerned in Tesla’s transition to making its own chips, a transfer that has since been emulated by GM and Hyundai, amongst different automakers.

“One of many first issues I did at Tesla was to determine what number of microcontrollers there are in a automobile, and to try this, we actually needed to type by a bunch of these large black bins with steel shielding and casing round it, to seek out these actually tiny small microcontrollers in there,” Tripathi mentioned. “And we ended up placing that on a desk, laid it out and mentioned, ‘Elon, there are 50 microcontrollers in a automobile. And we pay generally 1,000 instances margins on them as a result of they’re shielded and guarded in an enormous steel casing.’ And he’s like, ‘let’s go make our personal.’ And we did that.”

GPUs as collateral

Wanting additional into the long run, FlexAI has aspirations to construct out its personal infrastructure, too, together with knowledge facilities. This, Tripathi mentioned, might be funded by debt financing, constructing on a latest development that has seen rivals within the area including CoreWeave and Lambda Labs use Nvidia chips as collateral to safe loans — reasonably than giving extra fairness away.

“Bankers now know learn how to use GPUs as collaterals,” Tripathi mentioned. “Why give away fairness? Till we turn out to be an actual compute supplier, our firm’s worth is just not sufficient to get us the a whole lot of tens of millions of {dollars} wanted to put money into constructing knowledge centres. If we did solely fairness, we disappear when the cash is gone. But when we truly financial institution it on GPUs as collateral, they’ll take the GPUs away and put it in another knowledge middle.”