Physical AI developers working with world models face two distinct challenges: adapting models to their sensors, tasks, and data, and getting those models into applications reliably.

NVIDIA Cosmos addresses each. Cosmos is a platform of world foundation models and development tools for physical AI. Cosmos Framework provides agent skills and recipes for world model training, multimodal data processing, optimization and inference. Cosmos NIM microservices supports GPU-accelerated serving through application APIs.

This post walks through both. The simulation section shows how to use Cosmos Framework’s inference recipes and agent skills to generate synchronized camera video and LiDAR observations for AV, robot, and surgical scenarios, starting from released checkpoints you can run today. The deployment section shows how to choose a NIM configuration for your GPU, launch the container, verify readiness, and send your first inference request.

When to use Cosmos Framework for world model development

Cosmos Framework is an open and fully modular framework for world model training, model optimization, and inference to support a full model development lifecycle using new agent skills. It is expanding beyond Cosmos 3 to support NVIDIA Alpamayo, GR00T, Qwen, pi0.5, and other compatible Hugging Face models.