Build a Physical AI model factory with NVIDIA Cosmos 3 on SageMaker HyperPod | Amazon Web Services
A Physical AI system, such as a robot or autonomous vehicle (AV) that translates real-world data into physical actions, can’t be built in a single training job. Instead, it takes a continuous pipeline: a loop of generating synthetic data, post-training perception and policy models, so the system understands its surroundings and can act, and evaluating both in closed-loop simulation. Running that pipeline continuously is the job of a Physical AI model factory, turning a stream of new real-world data into better models, round after round.
This post shows how to build a Physical AI model factory with NVIDIA Cosmos 3 on Amazon SageMaker HyperPod, covering:
- What is unique about Cosmos 3: a Mixture-of-Transformers (MoT) design with per-layer joint attention and a deliberate train-versus-inference asymmetry.
- Why the design choices map cleanly onto Amazon SageMaker HyperPod with Amazon Elastic Kubernetes Service (Amazon EKS).
- Cluster and shared multi-terabyte storage layer setup.
- Distributed post-training...
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