Lockheed Martin and Google Public Sector Bring Generative AI On-Premises for National Security
Lockheed Martin and Google Public Sector announced a collaboration to integrate Google’s Gemini generative AI models into the Lockheed Martin AI Factory, according to a press release. The deployment will bring generative AI capabilities to Lockheed Martin’s secure, on-premises, and air-gapped environments for use across its enterprise.
The phased rollout will begin with unclassified systems before expanding to classified infrastructure. The initiative aims to enable Lockheed Martin teams to apply advanced AI tools for faster data analysis, research, and logistics optimization while meeting national security standards.
Lockheed Martin will use Google’s AI through Google Distributed Cloud to process multimodal data, accelerate R&D, and improve supply chain management. The companies stated that the collaboration is designed to ensure secure and scalable use of generative AI in mission-critical operations across aerospace, space, and cybersecurity sectors.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like Defense AI Brief or Daily AI Brief.
Also, consider following us on social media:
More from Defense
Oct 16 Saab Completes Sea Trials of Large Uncrewed Underwater Vehicle Oct 15 Bridge Defense Wins Software Agreement With Up to $63 Million in Initial Capacity Oct 15 Ukrainian Drones Hit Sberbank Data Center Planned for Supercomputer Oct 15 US Air Force Extends Use of PTC Servigistics Oct 15 Ukrainian Strikes Disrupt Yandex Services Across Central AsiaDefense AI Brief
Your weekly intelligence briefing on the technology shaping modern warfare and national security.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
Google Adds Unified AI Agent to Gemini for Businesses
BNP Paribas and Google Cloud Sign Five Year AI Partnership
Google Introduces Gemini 4 Argon for Coding and Cybersecurity
Balyasny Deploys Gemini for Financial Research
Google Launches Gemini 4 Argon With 1 Million Token Context
Daily AI Brief: the AI news that matters, in your inbox.