AWS AI Factories: Bringing Trainium to Your Data Center
Not all AI workloads can run in the cloud. Data residency, latency, or regulatory requirements may demand on-premises infrastructure. AWS AI Factories bring Trainium-powered AI infrastructure to your existing data centers.
What Are AI Factories?
AI Factories are turnkey AI infrastructure deployments that AWS installs and manages in your data center:
- Trainium chips: Same silicon as EC2 Trn2 instances
- AWS management: AWS handles hardware, updates, monitoring
- Your data center: Data never leaves your premises
- Outposts integration: Seamless hybrid experience
AI Factory vs Other Options
| Option | Location | Best For |
|---|---|---|
| EC2 Trn2 | AWS Region | Most AI workloads |
| SageMaker | AWS Region | Managed ML pipelines |
| AI Factory | Your data center | Sovereignty, ultra-low latency |
| Outposts | Your data center | General AWS on-prem |
Use Cases
Healthcare
Train models on patient data that cannot leave hospital premises due to HIPAA and data residency requirements.
Financial Services
Real-time fraud detection with sub-millisecond latency requirements that cloud round-trips can’t meet.
Manufacturing
Computer vision for quality control at the factory floor, processing camera feeds locally.
Government
Classified workloads that require air-gapped or highly controlled environments.
Getting Started
# Contact AWS for AI Factory deployment
# Requirements:
# - Data center space and power
# - Network connectivity to AWS
# - Long-term commitment (3+ years typical)
# Once deployed, use same APIs as cloud:
import torch_neuronx
# Model runs on local Trainium chips
model = torch_neuronx.trace(my_model, example_input)
output = model(input_data)
2026 Outlook
Expect AI Factories to include Trainium3 chips when they launch, offering even better performance for on-premises AI training and inference.
