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AI Infrastructure

29 posts

Cloud infrastructure, chips, data centers, model deployment, edge AI, compute platforms, secure systems, and the physical infrastructure behind artificial intelligence products and services.

Liquid cooling hoses and server infrastructure inside an NVIDIA AI factory reference design

NVIDIA Rubin Pushes AI Data Centers Toward Hotter, Drier Cooling

NVIDIA says its Rubin-generation AI infrastructure can run fully liquid-cooled servers with 45°C coolant, cutting facility cooling water use from conventional tower-based levels to near zero in favorable climates. The design is a real shift for AI factories, but it does not erase the water tied to power generation, chip manufacturing, or local data center siting fights.
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Agility Robotics Digit humanoid robot carrying a bin in a warehouse environment

NVIDIA Halos Turns Robot Safety Into a Full-Stack AI Platform

NVIDIA Halos for Robotics gives robot makers a shared safety stack for physical AI, combining IGX Thor compute, Halos OS, sensor infrastructure, outside-in safety agents, and an inspection lab for certification. Agility Robotics is the first public adopter, bringing parts of the system into Digit humanoid deployments for factories, warehouses, and logistics operations.
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AWS Trainium3 AI chip on a circuit board

Amazon’s Trainium Talks Push AWS Chips Beyond the Cloud

AWS is in early talks to sell Trainium AI chips for use in other companies’ data centers, a shift that could move Amazon from cloud-only accelerator provider toward a more direct role in the AI chip market. The opportunity is real, but so are the constraints: Trainium capacity is already tight, Nvidia still owns the broadest software ecosystem, and selling racks outside AWS could weaken the cloud bundle that makes custom silicon so valuable to Amazon.
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