At a glance
- 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…
# NVIDIA Expands Cosmos Platform for Physical AI World Model Development
NVIDIA has expanded its Cosmos platform with new training tools, simulation checkpoints, and data factory blueprints designed to help developers build world models that allow physical AI systems to understand and simulate real-world environments. The platform ranks in the top five on six different open models leaderboards and provides frontier open models alongside data, training, and optimization tools.
Building Training Data with Cosmos Data Factory
World model development begins with multimodal, multisensory data. NVIDIA Cosmos Data Factory brings together tools and skills for labeling, curating, augmenting, and evaluating data at scale for physical AI training. Cosmos Curator, a GPU-accelerated solution for curating video training datasets, now supports multimodal multisensory inputs and processing blocks.
Cosmos Data Factory offers five cloud-native blueprints available through GitHub, Microsoft Azure, Nebius, and CoreWeave:
- Autonomous Vehicle Dataset Generation: Curates and labels driving data, then adds synthetic examples of hard-to-capture scenarios to expand training coverage.
- Image Attribute Augmentation: Varies object appearance to improve image search.
- Event Video Generation: Turns still images into synthetic videos of rare or unsafe events.
- Defect Image Generation: Generates printed circuit board, metal, and glass defect images for inspection models.
- Video Data Augmentation: Changes weather and lighting in existing footage while preserving scene structure.
The platform accelerates data curation, processing, and synthetic data generation with agentic skills.
Porsche is leading development of a shared autonomous vehicle data flywheel across Volkswagen Group brands, using Cosmos Data Factory through Voxel51 on Microsoft Azure. The effort is expected to reduce repeated physical test drives by up to 90% and shorten development cycle times by up to 85%.
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 industrial-inspection robots to recognize liquid spills and other changes between facility visits.
Ultralytics, a computer vision company, retrained its YOLO model with real images and targeted synthetic defects generated using Cosmos Data Factory, improving defect localization and recall.
Training, Adapting, and Optimizing Models
With training data prepared, developers may train their own architecture or start with a pretrained model. At GTC Berlin, NVIDIA extended Cosmos Framework training, optimization, and deployment tools and recipes as agent skills to more open models, along with Cosmos 3, giving world model builders a faster path from experimentation to deployment.
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. Cosmos Framework is an open, modular framework of model training and optimization tools and skills to accelerate world model development.
Robotics research lab MicroAGI post-trained Cosmos 3 on videos of humans manipulating objects to develop a world model specialized for egocentric manipulation. The lab distilled its 32-billion-parameter reasoner down to an 8-billion-parameter Nano model with no loss in accuracy.
Developers can label data and post-train Cosmos 3 on Roboflow for specific environments and tasks, then deploy those models to Roboflow hosted or on-premises NVIDIA GPUs to bring video reasoning into factories and warehouses.
Testing Policies and Running Models Faster
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.
Developers can deploy core Cosmos models using updated NVIDIA Cosmos 3 NIM microservices, which deliver up to 2x faster inference with no quality loss in validated configurations, while continuous video analysis enables sub-second alerts.
For vision AI developers, the new NVIDIA Metropolis Blueprint for video search and summarization (VSS) 3.3, built on Cosmos NIM microservices, turns one prompt into an efficient video AI application and helps teams monitor 40% more video streams with the same GPU.
ADNOC uses Fogsphere’s MistIQ agentic platform, built with Cosmos and the NVIDIA Metropolis VSS Blueprint, to detect safety hazards in real time across 65 sites. For rare events, Fogsphere also uses the Cosmos Data Factory to generate synthetic training data, improving detection without needing to physically stage dangerous situations.
Operator Implications
The Cosmos platform is fully modular and open. Developers can use Cosmos Data Factory to generate training datasets, Cosmos Framework to train and adapt world models, then deploy supported Cosmos models through NIM microservices. The availability of blueprints across multiple cloud providers—GitHub, Microsoft Azure, Nebius, and CoreWeave—gives infrastructure operators flexibility in deployment architecture while the NIM microservices offer a standardized path for inference at scale.
MegaWatt Intelligence Desk
Hyperscale & Grid Analyst. `More stories by $MegaWatt Intelligence Desk →`
