AWS AI Factories Cheat Sheet

AWS AI Factories represent a transformative approach to enterprise AI infrastructure deployment. Announced in December 2024 at AWS re:Invent, this offering brings the full power of AWS cloud infrastructure directly into customer data centers, eliminating years of build complexity while meeting strict sovereignty and regulatory requirements.

This solution combines cutting-edge AI accelerators, high-performance networking, enterprise storage, and comprehensive AI services into a fully managed, dedicated environment that customers can deploy in their existing facilities.

What are AWS AI Factories?

AWS AI Factories are dedicated, fully managed AI infrastructure deployments that AWS builds and operates within customer-owned data centers. Think of them as private AWS Regions specifically designed for AI workloads, offering the same advanced capabilities available in public cloud regions but with complete data sovereignty and isolation.

The Core Concept

The fundamental value proposition is straightforward: organizations provide the physical infrastructure (data center space, power, and network connectivity), while AWS delivers, deploys, and manages everything else needed to run enterprise-scale AI workloads.

• Customer Provides: Physical data center space, electrical power capacity, and network connectivity
• AWS Provides: Complete AI infrastructure, including compute, storage, networking hardware, and comprehensive AI services
• AWS Manages: Ongoing operations, maintenance, updates, optimization, and technical support

Architecture Overview

AWS AI Factories deliver a complete, integrated AI infrastructure stack within your data center. The architecture is organized into distinct layers, each providing critical capabilities for large-scale AI workloads.

Key Components & Features

1. Compute Infrastructure

AWS AI Factories deploy the latest generation of AI accelerators for both training and inference workloads:

AWS Trainium Accelerators

• Trainium2: Currently available, purpose-built for AI training workloads
• Trainium3: Latest generation offering 4.4x more compute performance and 3.9x more memory bandwidth
• Trainium4: In development, promising 6x FP4 performance improvement and 4x memory bandwidth over Trainium3

NVIDIA GPU Platform

• NVIDIA Grace Blackwell architecture (B200, GB200 GPUs)
• Next-generation NVIDIA Vera Rubin platform (B300, GB300 GPUs)
• Full NVIDIA AI software stack and GPU-accelerated applications
• EC2 UltraClusters for scaling to thousands of GPUs

2. High-Performance Networking

The networking infrastructure is critical for large-scale AI training and inference:

• Elastic Fabric Adapter (EFA): Low-latency, high-throughput network interface optimized for HPC and ML
• Petabit-scale non-blocking network fabric enabling seamless communication between thousands of accelerators
• Future support for NVIDIA NVLink Fusion chip-to-chip interconnect technology

3. Storage Systems

Specialized storage solutions designed for the demanding I/O requirements of AI workloads:

• Amazon FSx for Lustre: High-performance parallel file system delivering hundreds of GB/s throughput
• Amazon S3 Express One Zone: Ultra-fast object storage for training data and model checkpoints
• Millions of IOPS capability for concurrent data access patterns

4. AI Services & Platforms

Amazon Bedrock

• Access to leading foundation models from multiple providers
• No need to negotiate separate contracts with individual model providers
• Simplified model selection, deployment, and management

Amazon SageMaker AI

• Comprehensive platform for building, training, and deploying custom AI models
• Integrated development environment for data scientists and ML engineers
• MLOps capabilities for production model management

5. Security & Compliance

AWS Nitro System

• Hardware-enforced security boundaries ensuring no one, including AWS, can access sensitive workloads
• Firmware-level protection with automated updates that maintain operational stability
• Cryptographic attestation of system integrity

Classification Level Support

AWS AI Factories are designed to meet government and enterprise security requirements across all classification levels:

• Unclassified
• Sensitive
• Secret
• Top Secret

Primary Use Cases

1. Sovereign AI Computing

Organizations with strict data sovereignty requirements can maintain complete control over where their data is processed and stored while still accessing cutting-edge AI capabilities.

• Target Industries: Government agencies, financial services, healthcare, defense contractors
• Key Benefits: Regulatory compliance, data residency control, secure isolated environments
• Example: National AI initiatives requiring local data processing for economic advancement

2. Large Language Model Training

Organizations developing proprietary foundation models or fine-tuning existing models on sensitive data need massive computational resources with data isolation.

• Target Users: Enterprises building industry-specific AI, research institutions, AI companies
• Key Benefits: Massive scale compute, proprietary data protection, optimized training infrastructure
• Technical Capability: Access to exaflops of compute with petabit networking for distributed training

3. AI-Powered Application Deployment

Deploy production AI applications with low-latency inference requirements while maintaining data locality.

• Target Applications: Real-time analytics, intelligent automation, customer-facing AI services
• Key Benefits: Low latency to on-premises systems, high availability, scalable inference
• Infrastructure: Amazon Bedrock and SageMaker for simplified deployment and management

Key Benefits & Value Proposition

Accelerated Time to Value

• Deployment measured in months, not years compared to building independently
• Eliminates complex procurement cycles for GPUs, networking, and specialized hardware
• Leverages AWS’s nearly two decades of cloud infrastructure expertise
• Pre-integrated software stack reduces configuration and optimization effort

Reduced Operational Complexity

• AWS manages hardware maintenance, firmware updates, and system optimization
• Integrated monitoring and management tools

• 24/7 AWS support and enterprise-grade SLAs
• Continuous infrastructure improvements without customer intervention

Data Sovereignty & Compliance

• Data never leaves customer premises
• Dedicated, isolated infrastructure operated exclusively for each customer
• Meets strict regulatory requirements for data residency
• Hardware-enforced security via AWS Nitro System

Leverage Existing Investments

• Utilize already-acquired data center space and power capacity
• Option to integrate existing NVIDIA GPU infrastructure
• Flexibility to start at current capability level and scale as needed
• Integration with existing on-premises systems and workflows

Deployment Process & Timeline

The deployment of AWS AI Factories follows a structured, four-phase approach. AWS manages the complexity of infrastructure deployment, allowing you to focus on your AI initiatives from day one.

💡 Key Advantage: AWS AI Factories accelerate time-to-production by 18-30 months compared to traditional DIY infrastructure builds, while AWS handles all operational complexity.

Ideal Customer Profile

Key Considerations & Planning Factors

Infrastructure Requirements

• Physical Space: Adequate data center floor space for rack installations
• Power Capacity: Substantial electrical power (potentially multi-megawatt requirements)
• Cooling: Advanced cooling systems to handle high-density compute heat loads
• Network: High-bandwidth connectivity for data transfer and remote management

Cost Considerations

While AWS has not publicly disclosed pricing, organizations should plan for:

• Significant capital commitment for large-scale AI infrastructure
• Premium pricing compared to public cloud due to dedicated hardware and management
• Ongoing operational costs managed by AWS
• Cost-benefit analysis compared to a multi-year in-house build and maintenance

Organizational Readiness

• AI/ML expertise to utilize the infrastructure effectively
• Clear use cases and business objectives for AI initiatives
• Data governance and AI ethics frameworks
• Commitment to the AWS environment and services

AWS AI Factories vs. Alternatives

Conclusion

AWS AI Factories represent a significant evolution in how enterprises can deploy and operate large-scale AI infrastructure. By combining AWS cloud expertise, cutting-edge hardware, and comprehensive AI services with customer-controlled data centers, this offering addresses the critical challenge of balancing sovereignty requirements with the need for advanced AI capabilities.

For organizations with strict regulatory requirements, existing data center investments, or national AI strategies, AWS AI Factories provide a compelling path forward. The solution eliminates years of infrastructure build time while maintaining complete data control and enabling access to the same advanced technologies available in AWS public cloud regions.

As AI continues to transform industries and economies, infrastructure solutions like AWS AI Factories will play a crucial role in democratizing access to advanced AI capabilities while respecting data sovereignty and regulatory boundaries. Organizations considering this path should carefully evaluate their requirements, infrastructure readiness, and long-term AI strategy to determine if AWS AI Factories align with their needs.

References:

• https://aws.amazon.com/about-aws/global-infrastructure/ai-factories/
• https://www.aboutamazon.com/news/aws/aws-data-centers-ai-factories
• https://aws.amazon.com/about-aws/whats-new/2025/12/aws-ai-factories/