---
description: Palantir and NVIDIA deploy post-trained Nemotron models in Foundry and AIP for NVIDIA&#039;s supply chain, 1.3 million parts per Vera Rubin rack.
title: Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack
image: https://www.storagereview.com/wp-content/uploads/2026/09/StorageReview-palantir-nvidia.jpg
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[Home](https://www.storagereview.com/) » [News](https://www.storagereview.com/news) » Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack

# Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack

by Harold Fritts on September 10, 2026

[AI](https://www.storagereview.com/enterprise/ai)  ◇  [Enterprise](https://www.storagereview.com/enterprise)

Palantir and NVIDIA have built a sovereign AI stack for supply chain operations and are running it first inside NVIDIA’s own supply chain, the one that has to line up 1.3 million parts for every Vera Rubin rack. The stack brings NVIDIA Nemotron open models into Palantir Foundry and its Artificial Intelligence Platform (AIP), grounded in the Palantir Ontology, with the aim of giving planners visibility across the chain, surfacing constraints early, and codifying the operational judgment that, to this point, lives in people’s heads. NVIDIA’s deployment starts with materials allocation, the decisions about which parts go where that set how fast a rack moves from wafer to first token.

![Palantir and NVIDIA logos side by side on a black background]()

## NVIDIA’s Supply Chain as the First Customer

A rack-scale AI system needs compute, memory, networking, power, cooling, and mechanical parts to arrive together, across thousands of suppliers and a global manufacturing network, and NVIDIA says the Vera Rubin supply chain is twice the size of Grace Blackwell’s. The new stack gives NVIDIA’s supply chain teams what the companies call a shared command center, beginning with allocation decisions, so they can identify constraints earlier, evaluate alternatives faster, and allocate materials based on end-to-end production impact.

“Supply chains are the operating system of the physical economy, and AI factories are among the most complex systems ever built,” said Jensen Huang, founder and CEO of NVIDIA. “From wafers and components to manufacturing, systems and customer delivery, hundreds of companies and trillions of dollars of global economic activity come together to deliver AI infrastructure.” Palantir cofounder and CEO Alex Karp put it more bluntly: “NVIDIA has arguably the most valuable, intricate and complex supply chain in the world.” The two companies first announced their operational AI work together at [GTC DC last October](https://www.storagereview.com/news/nvidia-gtc-dc25-operational-ai-with-palantir-ai-native-6g-nokia-ai-ran-with-arc-pro-hyperion-10-for-avs-and-igx-thor-at-the-edge); this is that partnership producing a deployed system.

## Post-Trained Nemotron, cuOpt, and a Human in the Loop

Palantir customers post-train [Nemotron](https://www.storagereview.com/news/nvidia-extends-open-hpc-and-ai-stack-with-schedmd-acquisition-and-nemotron-3) open models on their own operational data inside Foundry and AIP, using NVIDIA NeMo Data Libraries to prepare and augment it. Within AIP, NVIDIA cuOpt handles optimization and scenario planning, so teams can model supply constraints, weigh tradeoffs, and see the operational impact of an allocation decision before making it. The post-trained model recommends actions, explains the tradeoffs, and flags emerging risks, while the supply chain experts keep the final call. Palantir Autopilot, integrated with the NeMo AutoModel and NeMo RL libraries, closes the loop by feeding each recommendation, planner action, and production outcome back into model improvement, which is how the companies say operational knowledge gets preserved instead of lost when people move on.

NVIDIA’s technical write-up of its own deployment gives a sense of how light the model work is. The production model is Nemotron 3.5 Lightning, a 30-billion-parameter mixture-of-experts model with roughly 3 billion parameters active per pass, chosen over the larger Nemotron 3 Ultra for agentic workflows. Post-training used LoRA adapters with the base weights frozen and finished on two B200 GPUs in minutes. On NVIDIA’s internal allocation-decision benchmark, the post-trained Lightning model scored 86.7 percent accuracy, 31.2 points ahead of the untuned Nemotron 3 Ultra and 69.2 points ahead of its own base weights. Those are NVIDIA’s numbers on NVIDIA’s data, but they make the case for the approach: a small open model with the organization’s own decisions trained into it beats a much larger general one on that organization’s problem.

## Sovereign by Design

Because the data is NVIDIA’s supply chain, the deployment runs on NVIDIA reference architectures and the jointly developed Palantir Sovereign AI Operating System Reference Architecture, or SAIOS, which Dell Technologies and Cisco support. Proprietary data, model weights, and inference stay inside a single governed environment, and the stack can be deployed on premises with Cisco or Dell, or in colocation and cloud with Rackspace and Nebius. Palantir and NVIDIA say they intend to extend what they learn from NVIDIA’s deployment to customers in agriculture, manufacturing, pharmaceuticals, retail, energy, healthcare, automotive, aerospace, and government, and will show the stack and its industry applications at Palantir’s AIPCon 11.

### [Palantir AIP](https://www.palantir.com/platforms/aip/)

### [NVIDIA Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/)

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### Harold Fritts

I have been in the tech industry since IBM created Selectric. My background, though, is writing. So I decided to get out of the pre-sales biz and return to my roots, doing a bit of writing but still being involved in technology.

Previous post: [Backblaze B2 and WEKA NeuralMesh Validated as a Two-Tier AI Storage Pipeline, With Snap-to-Object Checkpoints in B2](https://www.storagereview.com/news/backblaze-b2-and-weka-neuralmesh-validated-as-a-two-tier-ai-storage-pipeline-with-snap-to-object-checkpoints-landing-in-b2)

Next post: [IBM Quantum System Two Heads to Switzerland: 120-Qubit Nighthawk r2 at CSCS by End of 2026](https://www.storagereview.com/news/ibm-quantum-system-two-heads-to-switzerland-120-qubit-nighthawk-r2-at-cscs-by-end-of-2026)

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