What is physical AI—and why now?
Physical AI refers to AI systems that not only operate in software environments, but also see, feel, reason, and act in the physical world. Unlike pure agent or business AI, these systems have to cope with real sensors, bodies in space and time, dynamic environments, and unforeseen situations. NVIDIA sees this as a turning point, and the technologies are now ready for real-world application scenarios in production or logistics, communication, service robotics, etc.
For sysGen customers, this means that anyone who wants to master physical AI needs a complete development pipeline—not just models, not just data, but training, simulation, inference, and deployment in a continuous workflow.
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NVIDIA proposes a trio of architectures covering all phases:
| Phase | System / Hardware | Purpose & Relevance |
|---|---|---|
| Training | NVIDIA DGX | Large models are trained on massive datasets: Robot Foundation Models, policies, perception models, etc. High computational demand – DGX provides the necessary GPU power. |
| Simulation & Synthetic Data | Omniverse + Cosmos on RTX PRO Servers | Simulating virtual environments, generating synthetic data, creating digital twins, and testing robotics policies in a safe environment. Crucial for identifying edge cases before deploying in the real world. |
| Inference / On-Robot Deployment | Jetson AGX Thor | Real-time processing of sensors, visual perception, language, and control directly on the robot. Energy-efficient, compact, and robust. |
Advantages:
- Speed and safety: Simulation enables early testing, while digital twins help identify errors before they become costly in hardware.
- Cost efficiency: Synthetic data saves on data collection; centralized training and decentralized inference optimize resources.
- Scalability and flexibility: This modular architecture can cover a wide range of applications, including manufacturing, logistics, and service.
Challenges:
- Domain transfer: From simulation model to real environment – differences in lighting conditions, physical effects, etc.
- Latency & real time: Inference on the robot requires efficient models and optimized hardware.
- Complexity in integration & maintenance: More systems mean more interfaces, tools, training, and maintenance.
Three Systems. One Solution. Future-Ready Robotics.
NVIDIA DGX Systems
NVIDIA DGX Systems are high-performance platforms for AI computing, purpose-built for training and deploying state-of-the-art AI models. They combine the latest NVIDIA GPUs and CPUs with optimized software stacks to deliver maximum compute power and efficiency. With solutions ranging from the DGX Spark for developers to the DGX GB200 for enterprises, DGX systems scale from research to full enterprise infrastructure.
Highlights:
- Extreme performance powered by the NVIDIA Grace Blackwell architecture.
- Optimized for training, inference, and deployment of AI models.
- Scalable solutions, from compact developer setups to supercomputing.
- Integrated NVIDIA AI software for seamless workflows.
Use cases: research, generative AI, large language models (LLMs), and high-performance computing (HPC).
NVIDIA AGX Systems are high-performance AI platforms designed for demanding applications in robotics, edge computing, and autonomous machines. They combine cutting-edge GPU and CPU architectures with the NVIDIA software ecosystem to run complex AI models in real time. With Jetson AGX Orin, developers get compact, energy-efficient modules for embedded AI, while Jetson Thor, the next generation, delivers even greater compute power for advanced robotics and multimodal AI. In addition, DRIVE AGX provides a specialized platform for autonomous driving.
Highlights:
- Powerful AI performance: up to 275 TOPS (AGX Orin) and over 2000 TFLOPS (Jetson Thor).
- Flexible power consumption ranging from 15 W to 130 W, depending on the application.
- Compact form factor with extensive I/O connectivity for sensors and peripherals.
- Scalable product family: from edge devices to autonomous vehicle solutions.
- Seamless integration with NVIDIA software stacks such as JetPack, Isaac, and DRIVE.
Use cases: autonomous machines, robotics, industrial automation, edge AI, smart cities, and autonomous driving.
NVIDIA Omniverse is a platform for 3D simulation, real-time rendering, and collaborative workflows. It unifies design, visualization, and simulation in a single environment. Powered by OpenUSD and NVIDIA RTX, Omniverse enables seamless data exchange between 3D tools and delivers physically accurate, photorealistic scenes.
Highlights:
- Real-time collaboration across multiple users.
- Physically based simulations, including lighting, materials, smoke, fire, and motion.
- Compatibility with a wide range of 3D tools via Connectors.
- Available for both cloud and on-premises deployment.
Use cases: digital twins, robotics, architecture, media, and product design.
Conclusion & Outlook
The combination of training (DGX), simulation/synthetic data (Omniverse & Cosmos on RTX PRO servers), and on-robot inference (Jetson AGX Thor) forms a continuous ecosystem. This makes physical AI not only theoretically conceivable, but also practically feasible—across industries. sysGen is ready to help you realize your projects with this technology.
Outlook: In the coming years, we can expect:
- Simulations to become even higher quality (e.g., in physics, material rendering, interaction).
- Models to become lighter and more efficient, making onboard inference even more powerful.
- The integration of multimodality (seeing, hearing, feeling, speech) to become the norm.
If you are interested in how you can use these technologies in your company, please contact us.