Build a Physical AI model factory with NVIDIA Cosmos 3 on SageMaker HyperPod
Physical AI model factories demand persistent, resilient compute. Here's how to architect one on SageMaker HyperPod.
Why it matters
A vendor tutorial on running continuous ML pipelines (synthetic data, training, evals) at scale on managed GPU clusters—relevant to practitioners building physical AI systems who need to understand cluster economics and goodput optimization.
The key facts
11 to knowNVIDIA Cosmos 3 used for physical AI workloads
SageMaker HyperPod persistent cluster architecture
Amazon EKS deployment
Synthetic data generation + post-training + closed-loop evaluation pipeline
GPU goodput optimization as primary metric
Model factory (continuous pipeline, not single-job training) framework
NVIDIA Cosmos 3 used for physical AI model factory pipeline
Workload includes: synthetic data generation, post-training, closed-loop evaluation
Infrastructure: Amazon SageMaker HyperPod on EKS (persistent, resilient cluster)
Success metric: GPU goodput (not just raw throughput)
Published Sept 4, 2026 (vendor blog/how-to format)
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: Building a Physical AI system takes a continuous pipeline, not a single training job. This post shows how to run that model factory (synthetic data generation, post-training, and closed-loop evaluation with NVIDIA Cosmos 3) on a persistent, resilient Amazon SageMaker HyperPod cluster on Amazon EKS,…