NVIDIA Cosmos Opens New Tools to Train, Evaluate and Deploy World Models

To help developers rapidly build world models , which help physical AI systems understand and simulate the real world, NVIDIA has expanded the Cosmos platform with new training tools, simulation checkpoints and Cosmos Data Factory blueprints.

Ranking top five on six different open models leaderboards, the NVIDIA Cosmos platform gives world model builders the frontier open models — as well as data, training and optimization tools and recipes — to scale training data, adapt models, test behavior and optimize for performance before deployment.

Building world models begins with multimodal, multisensory data.

With training data prepared, developers may train their own architecture or start with a pretrained model.

Developers and agents can now adapt open models like GR00T , Alpamayo , QWEN, Pi0.5 and other compatible Hugging Face models through fine-tuning and reinforcement learning, optimize the model with distillation and quantization, and deploy with high-performing inference.

To develop a world model specialized for egocentric manipulation, robotics research lab MicroAGI post-trained Cosmos 3 on videos of humans manipulating objects and distilled its 32-billion-parameter reasoner down to an 8-billion-parameter Nano model with no loss in accuracy.

ANYbotics generated over 1,500 synthetic video clips with Cosmos Data Factory blueprints, curating real-world data and validating outputs with Encord’s platform, to train its industrial-inspection robots to recognize liquid spills and other changes between facility visits.

Developers can label data and post-train Cosmos 3 on Roboflow for their specific environments and tasks, then deploy those models to RoboFlow hosted or on-premises NVIDIA GPUs to bring video reasoning into factories and warehouses.

New Cosmos 3 Nano model variants for simulation enable developers to test autonomous policies in repeatable, hard-to-capture, multiview multisensor scenarios. Accompanying code allows developers to adapt the models to train and evaluate their own policies for autonomous vehicles and bimanual robots in interactive simulations before real-world testing.

NVIDIA Cosmos Data Factory brings together the tools and skills for labeling, curating, augmenting and evaluating data at scale for training physical AI. Cosmos Curator, a GPU-accelerated solution for curating video training datasets, has now added support for multimodal multisensory inputs and processing blocks.

Cosmos Data Factory now offers the following five cloud-native blueprints and is available through GitHub , Microsoft Azure , Nebius and CoreWeave :

Cosmos Data Factory accelerates data curation, processing and synthetic data generation with agentic skills.

Companies are already putting Cosmos Data Factory blueprints to work to speed their physical AI development workflows.