Nvidia has made its fortune on the AI boom, but the company has a problem. It faces a growing number of roadblocks which, if left unchecked, could pop the bubble. Thus, the AI arms dealer has been forced to play a game of Whac-a-Mole, shoring up the foundations of its empire by investing its spoils into ensuring its customers don’t stop buying. The latest example of this came Friday, when Nvidia announced it’d become a minority investor in Cloverleaf. Founded in 2024, Cloverleaf specializes in laying the literal foundations – by which we mean the land and power – necessary to build bit barns so they can be leased by the cloud providers and model devs that actually pay the bills for Nvidia’s hardware. Nvidia can only sell as many GPUs as there are datacenters to put them, and those datacenters can only be deployed where there’s adequate power to support them. And since its latest round of GPUs can’t just be dropped into any old bit barn, Nvidia is going to need a lot more datacenter capacity, and by extension a lot more power. A new breed of datacenter Up until 2024, the majority of Nvidia’s systems were air-cooled. Supercomputers, like those built by Cray and Eviden, were the exception to the rule. Unless you were deploying tens of thousands of GPUs, its accelerators could be deployed just about anywhere with minimal modification to the facility. That began to change with Nvidia’s Blackwell generation. You could still get air-cooled systems from Nvidia and its partners, and it certainly sold a lot of eight-way HGX B200 boxes, but deploying its most powerful NVL72 systems required liquid cooling that not every facility was capable of supplying. Liquid-cooled facilities are harder to build than their air-cooled counterparts. Liquid-cooled racks tend to be more compute dense, and therefore heavier and power hungry to operate. That means in addition to larger UPS backups and beefier PDUs, these facilities now need a bunch of coolant distribution units (CDUs) to pump coolant around — about one for every one to three megawatts of compute capacity. Datacenters must be designed to support all this extra weight, and the problem is only going to get thornier as Nvidia transitions from roughly 250kW to 600kW racks starting next year. With Nvidia’s Rubin generation of GPUs, air-cooled GPUs may as well be a thing of the past, at least in the HGX and NVL-style form factors that power the majority of AI training and inference deployments today. For the first time, Nvidia is offering the chips only in liquid-cooled varieties. So, to sell its next-generation GPUs, Nvidia not only needs its customers to have adequate capacity, but also needs those facilities to offer the right mix of power and cooling. Right sizing for deployment It’s well established that Nvidia prioritizes customers with datacenters ready to fill. However, many of the GPU-slinger's biggest customers don’t actually own datacenters, but instead lease capacity from others. For example, Crusoe built OpenAI’s Stargate facility in Abilene, Texas, and Oracle runs it. Altman and crew are simply supplying the demand. So, Nvidia has invested in model devs and neocloud partners by helping them get financing or even investing directly in their businesses. Nvidia’s investments in OpenAI, CoreWeave, and Nebius are just a few examples. That works so long as the bit barns available for rent are the right size and shape for the hardware. But, as we noted earlier, many existing facilities aren’t. To further reduce friction and make it easier for its customers to find space to deploy its kit, Nvidia introduced its DSX platform last fall and officially launched it this spring. In a nutshell, DSX is a set of blueprints outlining exactly how much power, cooling infrastructure, and space are needed to support a given amount of compute. It also provides tools to optimize that compute to minimize wasted capacity. The power problem But even designing to a spec won’t do bit barn builders any good if they can’t get their hands on enough power. The proliferation of AI datacenters has begun to strain grid capacity, forcing many new ones to employ behind-the-meter power generation. For example, Nebius has begun using Bloom Energy's natural gas fuel cells to power its datacenters, rather than relying entirely on existing grid infrastructure. Nvidia’s investment in Cloverleaf is the latest example of how the GPU slinger is using the circular AI economy to its advantage. The terms of the deal haven’t been disclosed, but the benefits are obvious: Cloverleaf gets an influx of cash, while Nvidia gets greater control over the build process. As part of the deal, Cloverleaf is also embracing Nvidia’s DSX spec. In theory, this should enable bit barn builders to drop a DSX-compliant datacenter on a Cloverleaf-supplied site and be serving customers without delay. For Cloverleaf, Nvidia’s spec includes a wealth of power-related capabilities, including DSX Flex, which allows datacenters to scale back their workloads in response to grid conditions. So if grid demand spikes during a heat wave because everyone’s AC just kicked on, datacenters know it’s probably not a good idea to kick off another training run. The tech can also be used to offload demand onto onsite power generation or storage systems, like batteries — all things a company tasked with supplying powered sites for datacenters might be interested in. The deal is the latest example of how Nvidia is using its war chest to shore up cracks in the broader AI ecosystem before they become a problem. We wrote a few weeks ago about how the company was helping AI infrastructure startups secure financing in exchange for a share of the revenues, but it's now clear Nvidia is attacking potential roadblocks from every angle. ®
