Nvidia and Palantir fine-tune a 30B Nemotron model for Nvidia’s supply chain. It beats a model 18 times its size.
Nvidia and Palantir announced on Thursday that they’re working together to bring “sovereign AI to critical supply chains,” kicking off initially with Nvidia’s own sprawling supply chain.
The news builds on a partnership that kicked off last October, when the duo said they would combine Nvidia’s AI computing and models with Palantir’s software to help companies use AI to make complex operational decisions. Then in June, they expanded that effort into sovereign AI, allowing organizations to run and customize Nvidia’s AI models inside tightly controlled environments while keeping sensitive data and model weights under their own control.
Now, they’re applying that technology inside Nvidia itself, where they say a smaller, fine-tuned model is already outperforming a far larger one.
A proving ground for sovereign AI
The companies have fine-tuned Nvidia’s 30-billion-parameter Nemotron 3.5 Lightning model on decisions made by Nvidia’s supply-chain operations team. Palantir’s Foundry and Artificial Intelligence Platform (AIP) bring together the data behind those decisions, while its Ontology acts as a live map connecting components, factories, capacity and production commitments. Nvidia’s cuOpt software, meanwhile, works out how scarce parts could be distributed, with Nemotron weighing the wider context and recommending what planners should do.
They then plan to “extend the learnings from Nvidia’s deployment” to companies in other sectors, including manufacturing, energy, healthcare, automotive and aerospace. Palantir’s own customers will be able to build versions tailored to their own supply chains by training Nemotron on their proprietary data using Foundry and AIP, then run the resulting system on-premises or through cloud and colocation providers.
So, in effect, Nvidia and Palantir are putting the sovereign AI partnership they outlined in June into practice inside Nvidia, while using that deployment as a proving ground for an architecture other companies can adapt to their own use-cases.
Nvidia as a test case
As the world’s most valuable public company at $5.4 trillion market cap, there’s good reason for Nvidia to start close to home. Its supply chain spans millions of parts, thousands of suppliers and a global network of manufacturing partners, with the company saying a single Vera Rubin rack alone contains some 1.3 million parts. Those components have to arrive in the right place at the right time: if one part is missing, assembly can stall while everything else that arrived sits waiting.
“Supply chains are the operating system of the physical economy, and AI factories are among the most complex systems ever built.
Jensen Huang
And that complexity is what Nvidia founder and CEO Jensen Huang says makes supply chains a natural target for the technology. From chips and memory to manufacturing, networking, power and cooling, he argues that building modern AI systems increasingly depends on coordinating an enormous web of companies and components.
“Supply chains are the operating system of the physical economy, and AI factories are among the most complex systems ever built,” Huang says in a statement.
Palantir co-founder and CEO Alex Karp goes further, arguing that Nvidia’s operations provide an unusually demanding environment in which to put the companies’ approach to the test.
“Nvidia has arguably the most valuable, intricate, and complex supply chain in the world.”
“Nvidia has arguably the most valuable, intricate, and complex supply chain in the world,” Karp adds in a separate statement.
The sovereignty selling point
Nvidia has long been positioning itself at the center of the open-model debate. In July, Huang even used his first-ever post on X to promote an industry letter lobbying Washington to support frontier open-weight models, arguing that they give companies and countries more control over their AI infrastructure.
Then in early September, Nvidia swooped in with a $12.9 billion deal for Hugging Face, the so-called “GitHub for AI models.” Amid concerns that ownership by the world’s dominant AI chipmaker could undermine Hugging Face’s neutrality, Huang pledged that it would remain open, continue hosting models from across the industry and support hardware beyond Nvidia’s own.
Nemotron is central to Nvidia’s own open-model push. The name dates back to 2023, when Nvidia released its first Nemotron-3 8B models for enterprises to customize and fine-tune. Those early models were downloadable through Hugging Face and Nvidia’s NGC catalog, although access was gated and governed by Nvidia’s own community license. So they were customizable and their weights were available, but the much broader “open model” positioning Nvidia uses today came later.
The current Nemotron 3 series arrived back in December, initially spanning Nano, Super and Ultra models aimed at different agentic AI jobs. Nvidia now publishes weights and, for many of the models, training data and recipes so developers can customize themselves. Nemotron 3.5 Lightning, released in August, is the 30B model Nvidia and Palantir have fine-tuned for this supply-chain deployment.
That openness is also at the heart of the whole sovereignty pitch: companies can adapt Nemotron using proprietary data while keeping that data, the model weights and inference inside their own environment.
Specialization over size
Nvidia’s own deployment gives outsiders a result to chew on. It says that the fine-tuned 30B Lightning scored 86.7% accuracy on its supply-allocation task, versus 55.5% for the 550B Nemotron 3 Ultra — a model roughly 18 times its size.
In a technical blog post published on Thursday alongside the main announcement, Nvidia solutions architects Nell Barber, Rana Haber, and Aastha Jhunjhunwala note that the result shows how far specialization can go: on a tightly defined allocation task, the 30B model was able to outperform a general-purpose model more than an order of magnitude larger.
“This doesn’t mean the smaller model is more capable overall. Its gains are concentrated in the domain it was post-trained on.”
“This doesn’t mean the smaller model is more capable overall,” they add. “Its gains are concentrated in the domain it was post-trained on. Future production risk forecasting remained difficult despite fine tuning. Specialization improved the decision task but failed to solve every prediction problem attached to it.
For companies thinking about following Nvidia’s blueprint, that may be the more interesting takeaway: a smaller open model, taught the specifics of a business, can sometimes be more useful than simply reaching for the biggest model available.
