AWS AI Factories Combine Nvidia, Trainium Chips, and High-Speed Networking

TLDRs;

• AWS launches AI Factories allowing enterprises to run high-powered AI infrastructure on-site.
• The service uses Nvidia GPUs, Trainium chips, and high-speed networking for faster AI workloads.
• AWS handles deployment and management, letting enterprises focus on regulatory compliance and AI adoption.
• On-premises AI deployments may require power upgrades and liquid cooling retrofits in data centers.

Amazon Web Services (AWS) has unveiled its latest innovation, AWS AI Factories, a service that allows organizations to deploy dedicated AI infrastructure directly within their own data centers.

By integrating Nvidia accelerated computing, AWS Trainium chips, and high-speed networking, the offering provides enterprises with the ability to run advanced AI workloads on-premises while leveraging AWS-managed services.

The AI Factories platform is designed for organizations needing both high-performance AI capabilities and strict control over their data. Enterprises and public sector bodies that must meet regulatory or data sovereignty requirements can now run AI models without transferring sensitive data to the cloud.

AWS oversees deployment, management, and maintenance, ensuring companies can focus on AI adoption rather than infrastructure complexities.

Combining power and speed for AI

At the heart of AWS AI Factories are Nvidia GB200 NVL72 systems and AWS Trainium chips. These components offer rack-scale computing with exceptional processing power, enabling large-scale AI workloads to run efficiently.

Each GB200 NVL72 rack draws approximately 120 kW and requires specialized liquid cooling systems with manifolds and cold plates to keep the hardware operating safely. By pairing these advanced chips with high-speed networking, AWS ensures that AI models can be trained and deployed rapidly on-site.

AWS AI Factories also integrate with Amazon Bedrock, a managed service providing access to foundation AI models, and Amazon SageMaker, the company’s machine learning platform. This combination allows enterprises to leverage pre-built models and tools while maintaining full control of their on-premises environment.

Infrastructure considerations for data centers

While AWS handles AI software and deployment, customers are responsible for providing the physical data center space, network connectivity, and power required for these high-density systems. Many existing facilities may lack the capacity to support 120 kW per rack or the specialized liquid cooling needed for the Nvidia GB200-class equipment.

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As a result, data center operators and integrators have opportunities to offer retrofits, power upgrades, and liquid cooling solutions to meet the demands of AI Factories deployments.

Enterprise IT teams will also increasingly rely on Data Center Infrastructure Management (DCIM) tools to track power usage, environmental conditions, and capacity. This ensures that AI workloads can run safely and efficiently without overloading existing infrastructure.

Implications for AI adoption

By enabling on-premises AI deployments, AWS is providing organizations with a pathway to accelerate AI adoption while remaining compliant with regulatory standards. Although pricing, contract terms, and minimum footprint requirements have not been disclosed, the service marks a significant step in bringing cloud-level AI capabilities directly to enterprise data centers.

This move could reshape how large organizations approach AI, offering a hybrid model that combines the convenience and scalability of cloud AI with the control and security of local infrastructure. As AI demand grows, AWS AI Factories could play a pivotal role in helping enterprises deploy sophisticated AI workloads faster and more securely than ever before.

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