NVIDIA and Palantir have announced a collaboration to bring sovereign AI to supply chain operations, starting with a deployment within NVIDIA's own supply chain.

Announced on 10 September, the companies said the resulting AI stack combines NVIDIA's Nemotron open models with Palantir's Foundry and Artificial Intelligence Platform, grounded in Palantir's Ontology system, aiming to improve supply chain visibility and identify constraints while keeping NVIDIA in control of its proprietary data. NVIDIA said it manages one of the world's most complex supply chains, spanning millions of parts and thousands of suppliers, including 1.3 million parts in each Vera Rubin server rack. These claims about the system's capabilities and impact are the companies' own and have not been independently verified.

The companies said other organisations would be able to build similar supply chain systems by training Nemotron models on their own data through Palantir's platform, citing manufacturing, energy, healthcare, automotive and aerospace as indicative industries. The deployment runs on a jointly developed reference architecture, the Palantir Sovereign AI Operating System, supported by Dell Technologies and Cisco, and can be run on-premises or through cloud providers including Rackspace and Nebius.

The Sovereign AI Reality Check- Governance, cost, and the limits of control
Carolyn Duby, Field CTO at Cloudera, joins Stewart Tinson for a candid look at what sovereign AI actually protects, and where the concept stops being useful. Carolyn frames sovereign AI as a risk mitigation strategy rather than a silver bullet: it reduces the exposure that comes from handing data to third parties, but it doesn’t replace insider threat monitoring, access controls, or offboarding discipline. She’s direct about the limits, pointing out that basic cyber hygiene has to be in place before sovereign infrastructure adds any real protection, and that a sovereign AI system is simply another piece of IT requiring the same auditing and monitoring as everything else. The conversation covers the practical trade-offs businesses face when moving off SaaS models onto owned infrastructure, including the cost predictability that comes with saturating owned GPUs versus the unpredictability of pay-as-you-go pricing. Carolyn also discusses model provenance and supply chain risk when downloading open source models, why guardrails have to be built around what a system should do rather than relying on a model’s built-in defaults, and Cloudera’s work on an AI gateway designed to route requests to the most appropriate model based on sensitivity, cost, and performance. She closes on the growing importance of data in motion for agentic and autonomous systems, arguing that stale context undermines decision quality just as much as poor governance of data at rest. Key takeaways: sovereign AI mitigates specific risks but doesn’t replace basic security hygiene, cost predictability often matters more than raw cost, and model choice increasingly depends on matching sensitivity and task to the right infrastructure.
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