Open Models, Closed Environments: Palantir Brings Secure AI to US Agencies With NVIDIA Nemotron
Summary
Palantir has introduced a new intelligent engine leveraging NVIDIA Nemotron open models to provide secure AI capabilities for U.S. government agencies. This initiative underscores the historical role of open source software in U.S. technology leadership, from DARPA's 1969 network to UNIX (1969), C (1972), Linux Kernel (1991), GitHub (2008), and Docker (2013). Open models like Nemotron now make frontier-level AI broadly accessible, offering customization, transparency, and control vital for national security and industrial innovation. Palantir's engine enables agencies to run customized Nemotron models on their own air-gapped NVIDIA-accelerated infrastructure, train with proprietary data, and retain full ownership, including model weights. This is supported by Palantir's Sovereign AI Operating System, which ensures data authorization, isolation, and auditability, fostering continuous model improvement. The collaboration aims to bolster U.S. technology leadership through secure, cost-effective, and transparent AI deployments.
Key takeaway
For AI Architects or Directors of AI/ML within U.S. government agencies evaluating secure AI deployments, Palantir's new engine with NVIDIA Nemotron open models offers a compelling solution. You can deploy customized, frontier-quality AI on your own air-gapped infrastructure, ensuring full data ownership and auditability. This approach allows continuous model improvement with new data, maintaining strict control over sensitive information and addressing critical national security needs.
Key insights
Palantir and NVIDIA bring secure, customizable open AI models to U.S. government agencies for sensitive, air-gapped environments.
Principles
- Open source drives U.S. tech leadership.
- Transparency builds trust in AI models.
- Customization ensures data control.
Method
Palantir's Sovereign AI Operating System deploys customized NVIDIA Nemotron models on air-gapped infrastructure, allowing agencies to train on their data and retain full ownership, fostering a data flywheel for continuous improvement.
In practice
- Deploy Nemotron models in air-gapped setups.
- Train AI on proprietary government data.
- Retain full ownership of model weights.
Topics
- NVIDIA Nemotron
- Open Models
- Palantir Sovereign AI OS
- U.S. Government AI
- Air-gapped Environments
- Data Ownership
- AI Trust
Best for: CTO, VP of Engineering/Data, Executive, AI Architect, Director of AI/ML, Policy Maker
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Editorial summary, takeaway, and curation by AIssential. Original article published by NVIDIA Blog.