<p>NVIDIA's Cosmos3-Edge, Cosmos3-Nano, and Cosmos3-Super models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three models form the Cosmos 3 family of open, frontier omnimodal world models for physical AI, enabling customers to build robots, autonomous vehicles, and vision AI that perceive, reason, plan, and act in the physical world.</p>
<p>These models address different physical AI challenges with specialized capabilities:</p>
<p><b>Cosmos3-Edge</b> is engineered for <b>on-device robot control and real-time visual reasoning</b> on edge hardware. This 4B-parameter omni-model (with a 2B Nemotron-based reasoner) operates at robot-control resolution (640×360), delivering real-time reasoning and generating 32 actions per inference at 15 Hz on NVIDIA Jetson Thor. It supports 256p and 480p video at 12–30 FPS, bringing frontier physical AI capabilities directly to embedded systems.</p>
<p><b>Cosmos3-Nano</b> excels in <b>physics-aware world generation and physical reasoning</b> as a compact 16B-parameter omnimodal model. It processes combinations of text, image, video, audio, and action trajectories to produce corresponding outputs, enabling robots and vision AI agents to reason using prior knowledge, physics understanding, and common sense. It supports chain-of-thought reasoning over text, images, and video with resolutions up to 720p.</p>
<p><b>Cosmos3-Super</b> provides the <b>highest-fidelity world generation and simulation</b> in the Cosmos 3 family at 64B parameters. It jointly processes and generates language, images, video, audio, and action sequences within a unified Mixture-of-Transformers architecture, supporting resolutions up to 720p across multiple aspect ratios. Ideal for large-scale simulation, synthetic data generation, and policy learning workflows.</p>
<p>With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.</p>
<p>To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html">Amazon SageMaker JumpStart documentation</a>.</p>