AWS AI Factories bring NVIDIA GPUs, Trainium chips, and Amazon Bedrock directly into enterprise data centers. Here’s the complete breakdown of specs, features, and why this matters for AI adoption.
AWS Just Solved Enterprise AI’s Biggest Problem
At re:Invent 2025, Amazon Web Services announced something that makes enterprise IT leaders very happy: AWS AI Factories—dedicated AI infrastructure that runs inside your own data center, not in the cloud.
For companies with strict data sovereignty requirements, regulatory constraints, or simply massive AI workloads that benefit from colocation, this changes everything. You get AWS’s AI stack—NVIDIA GPUs, Trainium chips, Bedrock, SageMaker—without your data ever leaving your facility.
This is AWS’s answer to a fundamental enterprise dilemma: how do you leverage cutting-edge AI without compromising on security, latency, or compliance?
What Are AWS AI Factories?
AWS AI Factories are essentially private AWS Regions deployed inside customer data centers. They’re not edge devices or lightweight deployments—they’re full-stack AI infrastructure with enterprise capabilities.
The Core Components
| Component | Description |
|---|---|
| NVIDIA GPUs | Grace Blackwell and Vera Rubin platforms, NVLink interconnects |
| AWS Trainium | Custom chips for training and inference |
| Amazon Bedrock | Managed generative AI service with foundation models |
| SageMaker AI | ML development and deployment platform |
| AWS Networking | Low-latency interconnects optimized for AI workloads |
AWS handles the deployment complexity. You provide the data center space, power, and network connectivity. They manage the integrated infrastructure.
How It Works
1. Infrastructure Assessment: AWS evaluates your data center for power, cooling, and space requirements
2. Hardware Deployment: NVIDIA GPUs, Trainium chips, and networking equipment installed
3. Software Stack: Bedrock, SageMaker, and supporting services are deployed
4. Integration: Connected to your existing systems via AWS networking
5. Management: AWS handles infrastructure management; you control your AI workloads
The result is something that feels like AWS cloud but runs locally—with all the benefits of data residency and reduced latency.
The Hardware: NVIDIA and Trainium
NVIDIA Integration
AWS AI Factories incorporate the full NVIDIA AI stack:
Grace Blackwell Platform:
- Next-generation GPU architecture
- Optimized for both training and inference
- NVLink Fusion interconnects for ultra-low latency between GPUs
Vera Rubin Platform:
- Upcoming architecture for even higher performance
- AWS has committed to support via AI Factories
NVIDIA AI Enterprise:
- Available on AWS Marketplace within AI Factories
- Full-stack software for data science pipelines
- Production-grade AI application development
AWS Trainium Chips
Alongside NVIDIA, AWS is deploying its own silicon:
Trainium3 UltraServers:
| Metric | Trainium3 | vs. Trainium2 |
|---|---|---|
| Compute Performance | 362 FP8 petaflops per UltraServer | 4.4x improvement |
| Energy Efficiency | 40% better | Significant |
| Training Costs | Up to 50% lower for large models | Substantial savings |
Use Cases:
- Text summarization
- Code generation
- Fraud detection
- Large language models (100B+ parameters)
- Custom foundation model training
The hybrid NVIDIA + Trainium approach gives enterprises options: use NVIDIA for general-purpose GPU workloads and Trainium for cost-optimized training at scale.
Amazon Bedrock in AI Factories
Bedrock is AWS’s managed generative AI service, and it’s fully available within AI Factories.
Foundation Model Access
| Provider | Models Available |
|---|---|
| Amazon | Titan Text, Titan Embeddings, Titan Image |
| Anthropic | Claude Opus 4.5, Claude Sonnet 4.5 |
| Meta | Llama 3.3 70B, Llama 3.2 405B |
| Mistral | Mixtral, Mistral Large |
| AI21 | Jamba |
| Cohere | Command R+ |
All accessible through a single API, with the models running locally in your AI Factory—not in AWS cloud.
Bedrock Capabilities
Knowledge Bases:
- Retrieval Augmented Generation (RAG) using your company data
- Vector embeddings stored locally
- Semantic search across enterprise documents
Agents:
- Autonomous AI systems that can take actions
- Integration with company APIs and databases
- Multi-step task execution
Guardrails:
- Content safety filtering
- PII detection and redaction
- Custom policy enforcement
Optimization Features:
- Model Distillation: Create smaller, faster models from larger ones
- Intelligent Prompt Routing: Route queries to optimal models based on complexity
- Cost Optimization: Balance performance and spend
Running on Trainium3
Amazon confirmed that Bedrock production workloads are already running on Trainium3 within AI Factories. This means:
- Proven compatibility with real-world use cases
- Cost savings from Trainium vs. NVIDIA for inference
- AWS is eating its own cooking
Why Enterprises Care: The Business Case
1. Data Sovereignty
Many industries—healthcare, finance, government—have strict requirements about where data can be processed. With AI Factories:
- Data never leaves your premises
- AI inference happens locally
- Compliance teams sleep better
2. Latency Reduction
For real-time AI applications, cloud round-trips add unacceptable latency:
| Scenario | Cloud Latency | AI Factory Latency |
|---|---|---|
| Fraud detection | 100-300ms | <10ms |
| Manufacturing automation | 50-200ms | <5ms |
| Healthcare diagnostics | 200-500ms | <20ms |
AI Factories eliminate the physical distance between compute and data.
3. Regulatory Compliance
Regulations like GDPR, HIPAA, and industry-specific rules often require:
- Data residency within specific jurisdictions
- Audit trails for data access
- Control over processing environments
AI Factories provide this control while still leveraging AWS’s managed services.
4. Accelerated Time-to-Value
AWS claims AI Factories can accelerate AI buildouts by months or years compared to independent development. This comes from:
- Pre-integrated hardware and software
- Proven configurations
- AWS expertise in deployment
For enterprises that have struggled to build AI infrastructure internally, this is a compelling shortcut.
The Competitive Landscape
AWS isn’t alone in offering on-premises AI infrastructure:
| Vendor | Offering | Key Differentiator |
|---|---|---|
| AWS AI Factories | Full AWS stack on-prem | Broadest model ecosystem (Bedrock) |
| Azure Stack HCI | Azure services on-prem | Microsoft ecosystem integration |
| Google Distributed Cloud | GCP on-prem | Gemini integration |
| NVIDIA DGX BasePOD | Pure NVIDIA hardware | Highest raw GPU performance |
| IBM watsonx Local | IBM AI on-prem | Enterprise support focus |
AWS’s advantage is the combination of:
- Multiple hardware options (NVIDIA + Trainium)
- Managed AI services (Bedrock, SageMaker)
- Existing AWS ecosystem integration
For companies already invested in AWS, AI Factories are a natural extension.
Pricing and Economics
AWS hasn’t published specific AI Factories pricing (it’s negotiated per-deployment), but the economics involve:
Capital Expenditure:
- Data center space allocation
- Power and cooling infrastructure
- Network connectivity upgrades
Operational Expenditure:
- AWS managed services fees
- Hardware maintenance (handled by AWS)
- Software licensing
Cost Savings:
- Up to 50% lower training costs with Trainium3 vs. traditional GPUs
- Reduced data egress fees (data stays local)
- Lower latency = faster iteration = reduced development time
For large enterprises running significant AI workloads, the math often favors AI Factories over pure cloud, especially when data residency is already required.
Real-World Deployment Considerations
Prerequisites
Before deploying an AI Factory, you need:
| Requirement | Details |
|---|---|
| Space | Significant data center footprint |
| Power | High-density power provisioning |
| Cooling | Liquid cooling capability for modern GPUs |
| Network | High-bandwidth, low-latency connectivity |
| Staff | Operations team for facility management |
This isn’t for small companies. AI Factories target enterprises with existing data center operations and substantial AI ambitions.
Timeline
AWS estimates deployments can begin within 90 days of contract signing for standard configurations. Complex custom deployments may take longer.
Support Model
AWS provides:
- 24/7 infrastructure monitoring
- Hardware replacement and maintenance
- Software updates and security patches
- Technical support for Bedrock and SageMaker
You provide:
- Facility operations (power, cooling, physical security)
- AI application development
- Business integration
- Data management
The NVIDIA Partnership Deep Dive
The AWS-NVIDIA relationship in AI Factories is worth examining:
NVLink Fusion
AWS is the first cloud provider to support NVIDIA NVLink Fusion for custom AI infrastructure. This technology allows:
- Ultra-low latency GPU-to-GPU communication
- Efficient scaling across multiple nodes
- Training larger models than individual servers can handle
Trainium4 Integration
AWS announced plans to integrate Trainium4 chips with NVLink Fusion in future AI Factory deployments. This hybrid approach means:
- NVIDIA for flexibility and ecosystem compatibility
- Trainium for cost-optimized workloads
- NVLink connecting both seamlessly
NVIDIA AI Enterprise
The full NVIDIA software stack—including frameworks, tools, and optimizations—is available within AI Factories. This isn’t just hardware; it’s the complete NVIDIA AI development experience.
Who Should Consider AI Factories?
Good Fit If:
- You have strict data residency requirements
- Your AI workloads are large enough to justify dedicated infrastructure
- Latency matters for your applications
- You’re already invested in AWS ecosystem
- You have existing data center capacity
Not a Fit If:
- Your AI needs are modest or experimental
- You prefer variable cloud costs over committed infrastructure
- You lack data center operations expertise
- Your data can freely move to cloud without regulatory concern
The Bottom Line
AWS AI Factories represent a fundamental shift in how enterprises can deploy AI. By bringing AWS’s full AI stack—NVIDIA GPUs, Trainium chips, Bedrock, SageMaker—directly into customer data centers, AWS eliminates the trade-off between cutting-edge AI and data sovereignty.
For enterprises that have been waiting for a way to leverage cloud AI capabilities without cloud compromises, this is the answer.
The combination of:
- 4.4x performance improvement with Trainium3
- Full foundation model access via Bedrock
- NVIDIA compatibility for existing workloads
- AWS managed services for reduced operational burden
…makes AI Factories the most compelling enterprise AI infrastructure offering to date.
If you’re an enterprise with serious AI ambitions and data residency requirements, AI Factories should be on your evaluation list.
FAQ
How much does an AI Factory cost?
Pricing is negotiated per-deployment based on scale, configuration, and support requirements. Contact AWS for quotes.
Can I use my existing data center?
Yes, if it meets power, cooling, and space requirements. AWS will assess suitability.
Do I need to use both NVIDIA and Trainium?
No. You can configure AI Factories with NVIDIA-only, Trainium-only, or hybrid configurations.
How does this compare to simply building our own infrastructure?
AWS claims AI Factories accelerate deployment by months or years. You also get managed services and proven configurations vs. building from scratch.
Is Bedrock required?
No. You can use AI Factories for custom AI workloads without Bedrock, though Bedrock provides significant value for generative AI applications.
