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Frequently Asked Questions

This document contains comprehensive FAQ information compiled from multiple sources.

General Questions

What is NVIDIA Cosmos?

NVIDIA Cosmos is a world foundation model (WFM) development platform for physical AI applications. The platform includes pre-trained multimodal models for generating world states as videos and physical AI reasoning, along with tokenizers, guardrails, data-processing pipelines, and post-training scripts.

What are the main components of Cosmos?

Cosmos World Foundation Models (WFMs)

Cosmos world foundation models (WFMs) are pretrained generative AI models for virtual world generation to advance physical AI. The WFM family includes:

  • Cosmos Predict for generating future world states as videos
  • Cosmos Transfer for conditioned synthetic data
  • Cosmos Reason for physical AI reasoning

These models are fully customizable to develop specialized physical AI models.

Cosmos Curator

GPU-accelerated video preprocessing and curation toolkit for preparing high-quality datasets.

Cosmos Tokenizer

To efficiently convert visual data into tokens.

Who is Cosmos designed for?

Cosmos is designed for developers and ISVs working in the following domains:

  • Robotics
  • Autonomous vehicles
  • Simulation
  • Computer vision applications

What are the technical capabilities of the Cosmos platform?

The Cosmos platform provides the following capabilities:

  • Pre-trained world foundation models (WFMs) for immediate deployment
  • GPU-accelerated data processing and curation tools
  • Post-training frameworks for domain-specific adaptation
  • CUDA-optimized inference and training pipelines
  • Synthetic data generation for physical AI model training

Models

What are the main use cases for Cosmos?

Data Curation

Cosmos Curator provides video data processing and search capabilities for large-scale real or synthetic datasets used in physical AI model training.

Synthetic Data Generation

Cosmos models support synthetic data generation through:

  • Cosmos Predict: Generates synthetic data from text prompts or image pairs, producing predictive or interpolated frames
  • Cosmos Transfer: Processes 3D scenes from Omniverse to generate photorealistic data across diverse environments and lighting conditions
  • Cosmos Reason: Evaluates synthetic data quality and generates captions for data curation

Post-training

Cosmos WFMs support customization for domain-specific applications through post-training, enabling modification of:

  • Output type and format
  • Output quantity and resolution
  • Output quality and style
  • Output perspective and viewpoint

How do Cosmos models differ from other video foundation models?

Cosmos world foundation models are designed specifically for physical AI applications. The models are openly available and customizable, with Cosmos Predict and Cosmos Reason supporting post-training for autonomous vehicle, robotics, and vision-action generation models.

What is Policy Initialization?

Policy Initialization is the process of developing a policy model from the world foundation model (WFM) by modifying its output head. A Policy model maps observed states (e.g. video) to actions. It can be initialized by post-training a Cosmos world foundation model with a new output head tailored for action selection (from video head → action head).

What is Policy Evaluation?

Policy Evaluation is the process of assessing a trained policy model. It can be done by conditioning with/seeding specific inputs (e.g. actions or instructions) and analyzing model output. This step ensures that the model correctly maps states to actions and performs as expected in real-world or simulated environments.

Can Cosmos be used for creative content generation?

Cosmos models can generate video content under the NVIDIA Open Model License, but the platform is primarily designed for physical AI applications rather than creative content generation.

What is multiverse simulation?

Multiverse simulation involves generating multiple future outcomes from a given state. Cosmos, integrated with NVIDIA Omniverse, enables simulation of multiple scenarios for tasks such as predictive maintenance and autonomous decision-making.

Is Cosmos Reason a VLM, VLA, or MLLM?

Cosmos Reason 1 is a physical reasoning engine designed to analyze real-world scenarios through natural language explanations. It functions as a Vision Language Model (VLM) or a Multi-Modal Large Language Model (MLLM) with chain-of-thought reasoning built-in. Unlike Vision-Language-Action (VLA) models, which map sensory inputs to executable actions, Cosmos Reason 1 employs hierarchical ontologies for space, time, and physics to generate text-based reasoning traces about safety, causality, and object interactions.

What does Cosmos Reason output?

While VLAs output motor commands, Cosmos Reason 1 produces insights in text like "The slope exceeds the vehicle's tilt tolerance" that require translation layers for robotic execution. The models can do high-level planning and explainable safety checks but cannot directly control actuators or navigate dynamic environments.

Technical Details

What is 3D consistency, and how is it tested?

3D consistency measures how well models maintain spatial alignment in 3D scenes. Cosmos testing methodology:

Test Setup: Static scenes from 500 curated videos using the following metrics:

  • Geometric consistency (e.g. Sampson error, pose estimation)
  • View synthesis consistency (PSNR, SSIM, LPIPS)

Key Metrics:

  • Sampson error: Lower values indicate better geometric accuracy
  • Pose estimation success rate: Higher percentages reflect better camera alignment
  • PSNR and SSIM: Higher scores indicate higher quality in synthesized views
  • LPIPS: Lower values indicate better perceptual similarity

What is physics alignment, and how is it evaluated?

Physics alignment tests models' ability to simulate physical dynamics like gravity and collisions.

Evaluation Method: Controlled scenarios in virtual environments, assessed with multiple metrics

Key Metrics:

  • PSNR: Higher values show less noise and better pixel accuracy
  • SSIM: Higher values reflect better visual fidelity
  • DreamSim: Evaluates semantic consistency of objects and motion
  • IoU: Measures overlap between predicted and actual object regions for alignment with physical expectations

What precision strategy do the Cosmos world foundation models use?

Training Strategy: Mixed-precision approach

  • Maintains copies of weights in FP32 and BF16
  • Gradients computed in BF16 only
  • Final storage and inference in BF16

Current Limitations:

  • The repositories do not currently support FP8
  • FP8 and FP4 training capabilities are work in progress

Infrastructure Requirements for Post-Training

Minimum Setup

For Cosmos Reason 1-7B:

  • SFT Training: Minimum 2x 80GB GPUs
  • RL Training: Minimum 4x 80GB GPUs

General Requirements:

  • NVIDIA GPU with sufficient memory
  • CUDA toolkit compatibility
  • High-speed interconnects for distributed training

Distributed Training Requirements

Networking:

  • Recommended: InfiniBand or RoCE for efficient communication
  • Supported: AWS EFA
  • Essential: High-bandwidth, low-latency connections for multi-GPU setups

Optimization Strategies

Pipeline Optimization:

  • Ray-based pipeline allows specification of GPU types.
  • Dynamic hardware detection supported.
  • Mixed GPU types can be leveraged for different pipeline stages.
  • Telemetry ensures efficient resource utilization.

Memory Management:

  • Requirements vary by model size and dataset characteristics.
  • Prompt length and chain-of-thought length affect memory needs.
  • Horizontal scaling supported for large deployments.

Performance Characteristics

Compression and Quality

Video Compression Impact:

  • Higher compression rates can affect generation quality.
  • Temporal compression with reduced tokens may lower quality
  • Optimal settings depend on specific application requirements.
  • Testing recommended to find best trade-offs.

Processing Performance

Cosmos Curator Performance:

  • GPU-accelerated processing compared to CPU-based pipelines.
  • Optimized for large-scale video processing workloads.

Tokenizer Performance:

  • Optimized compression and processing for video data
  • Supports both training and inference workloads.

Model Architecture Details

Training Configuration

Layer Management:

  • No layers are frozen during SFT and RL training.
  • Full model fine-tuning approach.
  • No specific attention mechanism modifications required.

Memory Recommendations:

  • Model size determines base memory requirements.
  • Dataset characteristics (video length, resolution) affect memory needs.
  • Multi-GPU training recommended for larger models.

Input Specifications

Video Input Guidelines:

  • Recommended FPS: 4 frames per second
  • Token Budget: Centered at 8k tokens, randomized within the [6k, 10k] range
  • Total Pixels: Approximately 8k × 28 × 28 × 2
  • Generalization: Model can handle inputs outside training bounds with potential quality degradation

Scalability Considerations

Database Scaling

Vector Database Performance:

  • Horizontal scaling for large datasets
  • Optimized indexing for bulk data ingestion
  • Resource-efficient scaling (spin down after ingestion)
  • Fast search times maintained at scale

GPU Resource Management

Dynamic Allocation:

  • Ray-based pipeline supports multiple GPU types.
  • Dynamic hardware detection and optimization
  • Efficient resource utilization through telemetry
  • Mixed hardware configurations supported.

Licensing & Availability

What is the licensing model for Cosmos models?

Cosmos world foundation models are available under the NVIDIA Open Model License Agreement, which permits the following:

  • Commercial use without payment requirements
  • No company size restrictions
  • Synthetic data generation
  • Post-training and derivative model development
  • Model distribution and modification

Enterprise Options

Open Source vs Enterprise

  • Model weights and scripts: Open source and free
  • Basic development tools: Available under permissive licenses
  • Enterprise features: Available through NVIDIA AI Enterprise (NVAIE)

NVIDIA AI Enterprise Features

  • Optimized NIMs for enhanced inference performance
  • Advanced NeMo features and maintenance
  • Professional support and updates
  • Production deployment tools

License Compatibility

Existing NVIDIA Omniverse Enterprise (NVOVE) licenses can be used for Cosmos entitlements.

Getting Support

Community Resources

  • GitHub Issues: Report bugs and request features in relevant repositories
  • Documentation: Comprehensive guides in each repository
  • Examples: Reference implementations and tutorials
  • Community Forums: Engage with other developers

Official Channels

  • NVIDIA Developer Portal: Latest updates and announcements
  • build.nvidia.com: Try models and access NIMs
  • NVIDIA AI Catalog: Enhanced text prompt tools and specialized models

Enterprise Support

For enterprise deployments:

  • NVIDIA AI Enterprise subscriptions include professional support
  • Dedicated technical assistance for production deployments
  • Regular updates and optimizations
  • Custom integration guidance

License Terms

The NVIDIA Open Model License Agreement covers:

  • Commercial usage rights
  • Distribution permissions
  • Modification allowances
  • Attribution requirements

General Questions

Q: Do I need to pay to use Cosmos models?

A: No, the core models are freely available under the NVIDIA Open Model License.

Q: Can I use Cosmos for commercial applications?

A: Yes, commercial use is permitted without restrictions.

Q: What if I need enterprise-grade support?

A: NVIDIA AI Enterprise provides optimized tools and professional support.

Q: Are there any usage restrictions?

A: Use must comply with the NVIDIA Open Model License Agreement and applicable laws.

Q: Can I modify and redistribute the models?

A: Yes, modification and redistribution are permitted under the license terms.