Basic Information
| Attribute | Value |
|---|---|
| Organization | NVIDIA |
| Type | Commercial (Enterprise) |
| Launch Date | GTC 2025 (Cosmos Predict 2.5) |
| Current Version | Cosmos Predict 2.5, Cosmos Transfer |
| URL | developer.nvidia.com/cosmos |
| Availability | Enterprise APIs |
Capability Profile
| Capability | Support | Notes |
|---|---|---|
| Text-to-World | Full | Via Cosmos Predict |
| Image/Video-to-World | Full | Multi-modal input |
| Physics Simulation | Full | Purpose-built for Physical AI |
| USD Integration | Full | Native Omniverse integration |
| Real-time Streaming | Partial | ~20ms per frame |
| Auto Material Labeling | Full | Edify SimReady (1000 objects/minutes) |
Standards Compliance
| Standard | Compliance | Notes |
|---|---|---|
| OpenUSD | Full | Native Omniverse integration |
| glTF 2.0 | Partial | Via Omniverse export |
| PhysX | Full | Native physics integration |
Cosmos is the gold standard for USD integration in AI world generation.
Maturity Assessment
SRL Score: SRL-5 (Early Adoption)
Evidence:
- 300K+ users across 252 companies
- 2M+ downloads of Cosmos World Foundation Models
- Production deployments in robotics (1X, Figure AI, Agility)
- Production deployments in autonomous vehicles (Uber, Wayve)
Interoperability
| Aspect | Details |
|---|---|
| Export Formats | USD, video, sensor data formats |
| Import Formats | Text, image, video, movement data |
| API Availability | Yes (enterprise APIs) |
| Known Integrations | Omniverse, Isaac Sim, robotics partners |
Strengths
-
Purpose-built for physics-accurate simulation - Designed from the ground up for robotics and autonomous systems training
-
Native USD integration - Clean interchange with professional VFX and simulation pipelines
-
Edify SimReady automation - Reduces 40+ hours of manual material labeling to minutes (1000 objects/minute)
Limitations
-
Enterprise-focused - Not accessible to indie developers; requires significant business engagement
-
Significant infrastructure requirements - Estimated 8x H100 for full capability
-
Robotics-primary use case - Optimized for robotics training, not interactive gaming
Physical AI Architecture
Cosmos positions as the “operating system for Physical AI”:
- Cosmos Predict - Video generation from multimodal inputs
- Cosmos Transfer - Style and domain transfer
- Edify SimReady - Automatic physics material labeling
- Isaac Sim Integration - Robotics simulation connector
Production Deployments
| Partner | Use Case | Scale |
|---|---|---|
| 1X | Humanoid robot training | Production |
| Figure AI | General robotics | Production |
| Agility | Digit robot environments | Production |
| Uber | AV simulation | Production |
| Wayve | AV training data | Production |
MSF Relevance
NVIDIA Cosmos demonstrates enterprise-grade AI generation with strong USD adoption. It serves as a model for how AI generation can integrate with existing standards when backed by a major platform company.
Key lessons for MSF:
- USD integration is achievable and valuable
- Enterprise use cases (robotics, simulation) drive standards adoption
- Physics-aware generation requires standards for material properties
- The MSF definition of metaverse (including machines as consumers) positions Cosmos as in-scope