Nvidia and Palantir want to speed up the AI buildout. Nvidia is first in line

Nvidia has begun running its own supply chain on Palantir software, making the chipmaker at the center of the AI boom the first customer of a platform the two companies now plan to sell across industry and government.

The collaboration, which builds off a partnership that began last October, turns Palantir’s software into a shared command center for Nvidia’s global supply chain, deploying Nvidia’s own open-weight models to fix its bottlenecks. “We’re really at the center of the world’s largest infrastructure buildout in human history,” says Justin Boitano, Nvidia’s vice president of enterprise AI.

The speed of Nvidia’s supply chain governs how quickly artificial intelligence infrastructure gets built anywhere in the world. The company has said it plans to produce up to $500 billion of AI infrastructure in the U.S. through partnerships and federal initiatives, alongside building supercomputers and secure AI factories for the government.

Nvidia’s aim is to cut the time that its inventory sits idle. Each Vera Rubin rack of GPUs requires Nvidia to coordinate thousands of suppliers and roughly 1.3 million components across compute, memory, networking, power, cooling, and mechanical systems. “Any single [piece] could essentially break the whole system,” Boitano tells Fast Company, “so how you allocate those materials really matters.”

Amid a chip shortage, a power shortage, years-long grid interconnection queues, and a national pushback against data center construction, every minute gained counts. The CEOs of both companies and the Trump administration call the stakes of the buildout existential.

Boitano says the combination of Nvidia’s open-weight models and Palantir’s software can help streamline supply chains for every level of AI deployment, including, through a preexisting partnership, building the data center itself. “When does power come online, how does the building get constructed, when do I have different [types of] cooling show up, when does the physical compute infrastructure show up,” he says, “and then automating everything to allocation of compute in tokens through that infrastructure.”

But the system, which is designed to run on a customer’s own servers as needed, can help anyone with a complex supply chain to build, Boitano says, “which you can imagine really is a large portion of the GDP of our economy.”

The companies call their offering “sovereign intelligence,” echoing a growing demand among governments and companies for more control over their AI stacks, and a recent fixation of Palantir’s CEO, Alex Karp, who has called on enterprises to end their dependence on hyperscalers like Google, OpenAI, and Anthropic.

During a fiery appearance on CNBC in July, Karp blasted the enterprise business model of the large AI labs, and warned that it was forcing enterprises and governments to give up “their means of production.” “Are we really going to outsource the battlefield of this country to the consensus view in Silicon Valley? That is effing insane,” he said.

Kevin Kawasaki, Palantir’s global head of business development, says sovereignty isn’t just about data custody, or freedom from the frontier model companies, but how well your own custom AI stack works. “Your primary source of sovereignty is your own outperformance,” he says. “Forget about your data if your business isn’t succeeding.”

‘I did not think this was possible a year ago’

That case rests on open-weight models having improved fast enough to beat frontier models on the narrow benchmarks a given company cares about, at a fraction of the cost. For most customers, frontier models are still essential, Kawasaki says, “but in a lot of cases, you can use these open models and post-train them to achieve beyond frontier capabilities.” He says the post-training runs at Nvidia took minutes rather than requiring massive clusters. “I did not think this was possible a year ago . . . that a custom post-trained model was even worth the time.”

The partnership originated in work for military and intelligence agencies that operate their own classified clouds, where Nvidia sells chips and Palantir sells its battlefield-focused Maven Smart System. “This is where we learned it was possible, and we learned it was effective, and we learned it would work,” Kawasaki says. “That was where Palantir learned that this was going to be a big market.”

Government interest now, he says, is “urgent and extreme.” “You can’t use the closed source models on a submarine, down range. It has to be, you’re either disconnected or it’s not allowed.”

The push for sovereign AI picked up steam last year as governments around the globe questioned their reliance on the U.S. and its cloud providers. More than 180 government-backed sovereign AI projects are now underway worldwide, according to the Center for a New American Security. On the corporate side, says Kawasaki, “I don’t have a customer that isn’t interested in this.”

The companies did not disclose new customers for their platform, but last December they announced an initial collaboration to help the retail giant Lowe’s optimize its global supply chain, covering more than 1,700 stores and 7,500 vendors.

While Nvidia and Palantir have announced no joint sovereign deployments outside the U.S., in January a U.K. data center developer named Sovereign AI selected Palantir and Accenture to build AI infrastructure across Europe and the Middle East, using Dell servers with Nvidia chips. The two companies also unveiled a system in March, Sovereign AI OS, for deploying AI in organizations’ own data centers, aimed at customers with latency-sensitive workflows, data sovereignty needs, and wide geographic distribution.

Nvidia’s own sovereign business abroad is large and growing. In July, Nvidia, Japan’s trade ministry, and the Japanese government-backed AI and robotics company Noetra announced an “AI factory” billed as the world’s first national AI infrastructure for physical AI, with 27,500 Rubin GPUs across 140 megawatts of data center capacity. Nvidia has struck similar arrangements with Humain in Saudi Arabia, G42 in the UAE, Naver in South Korea, Mistral in France, and BharatGen in India.

But the pushback on U.S. giants overseas has also extended to Palantir’s own contracts. The Bundeswehr, the German armed forces, is testing a number of European alternatives to Palantir’s Maven—including Almato, Orcrist and ChapsVision—and a broader European backlash is gathering, including inside NATO. Palantir’s European business has grown regardless: it inked a £1.5 billion ($2 billion) U.K. partnership last September, then a £240.6 million Ministry of Defence contract in December, its largest with the agency to date.

Palantir doesn’t develop its own large language models. Instead, it sells software that sits on top of other companies’ AI models, offering harnesses, controls, audit logs, and tools for fine-tuning and post-training. Its main platforms—Gotham, Foundry and AIP—wrangle data, analyze trends, and automate processes for companies and government agencies, including Airbus, Walmart, NATO, and ICE. The company sends employees into customers’ offices to set up the software; a Palantir team arrived at Nvidia four weeks ago. (Neither company disclosed financial terms.)

Kawasaki, the Palantir business development head, argues that what distinguishes the system is not optimization, which many software companies try to do, but how it learns. Decades of judgment held by experienced planners—about which supplier slips, which substitution is safe—along with each recommendation and outcome, feed back into the weights of the model.

“If I can take those decisions and understand why they were made, what was the expectation of that decision, and then what happened—and think about that more like a training trace, as opposed to just essentially friction in the machine—this is now an asset,” he says.

Previously at Nvidia, a planner assembled data by hand to decide allocations, “a manual process across multiple systems of record,” as Boitano, the enterprise AI VP, puts it. Palantir’s Ontology, a data layer that maps a company’s physical operations into rules, unifies those sources. Nvidia’s cuOpt software models supply constraints and weighs tradeoffs, while its Nemotron models, post-trained on a user’s internal data, recommend actions, explain them, and flag risks. Human planners still make key decisions, says Boitano.

In the process, more decisions can be automated. Boitano compares it to how programmers came to trust coding agents, reviewing every output until they stopped. “Initially you want people in the loop,” he says, “but as you trust the system, you can let it run in a longer horizon.”

Amid fears of hacks and agents going rogue, or both, safeguards trained into models should be assumed removable. “The question is how does the system still fail safe,” Biotano says, and the Ontology “acts as the rule set of what the AI can do.”

Extending Nvidia’s reach

For Nvidia, the Palantir deal could further extend the chipmaker’s reach across the AI ecosystem. Jensen Huang, the Nvidia CEO, has backed open models and invested aggressively in the labs and neoclouds that buy his chips, a strategy that has drawn scrutiny for its circularity but that Dylan Patel, founder of SemiAnalysis, reads as insurance against being squeezed. “A world where OpenAI, Anthropic, and Google models are the only models is one in which he’s screwed,” Dylan Patel told the podcaster Dwarkesh Patel in March. “A world where hyperscalers are the only ones building compute is one he’s screwed in.”

Both Nvidia and Palantir collectively hold billions of dollars in U.S. government contracts and have strong ties to the Trump administration. Nvidia and a competitor, AMD, last year arranged a deal with the White House to share 15% of their China-related revenue with the government in exchange for resuming exports of specific chips like the H20.

President Donald Trump’s financial disclosures also showed dividend earnings from significant stakes in many companies, including Palantir and Nvidia. Both companies’ market values have soared since late 2024, to roughly $300 billion for Palantir and $5.6 trillion for Nvidia, making it the most valuable company in history.

As the Palantir system proves itself at Nvidia, Kawasaki argues that more automation—across the AI buildout and the rest of the economy—is not optional.

“We haven’t done a lot of building like this in the United States in quite some time,” he says. “When you think about how many more people can be employed to do complex construction jobs, the number is somewhat limited. And so therefore, if you want to produce more with the same amount of inputs, the computer really does have to do more work.”