NVIDIA Brings Physical AI Through NIM Microservices for Digital Environments
NVIDIA has introduced new NVIDIA NIM microservices and the NVIDIA Metropolis reference workflow, significantly advancing generative physical AI. These developments, announced at SIGGRAPH, include three fVDB NIM microservices supporting NVIDIA’s deep learning framework for 3D worlds and USD Code, USD Search, and USD Validate microservices for working with Universal Scene Description (OpenUSD). These tools enable developers to integrate generative AI copilots and agents into USD workflows, expanding the capabilities of 3D worlds. Physical AI with Visual AI Agents Physical AI, which uses advanced simulations and learning methods, is transforming sectors like manufacturing and healthcare by enhancing the ability of robots and infrastructure to perceive, reason, and navigate. Interestingly, NVIDIA chief Jensen Huang had termed the next wave of AI as Physical AI. NVIDIA offers a range of NIM microservices tailored to specific models and industries, supporting speech and translation, vision and intelligence, and realistic animation. Visual AI agents, powered by vision language models (VLMs), are increasingly deployed in hospitals, factories, and cities. In Palermo, Italy, NVIDIA NIM-powered agents help manage traffic efficiently. They have deployed visual AI agents using NVIDIA NIM to uncover physical insights that help them better manage roadways. Companies such as Foxconn and Pegatron also use the same to design and operate virtual factories, improving safety and efficiency. Bridging the Simulation-to-Reality Gap NVIDIA’s physical AI software, including VLM NIM microservices, facilitates a “simulation-first” approach, crucial for industrial automation projects. These tools enable the creation of digital twins, simulating real-world conditions for better AI model training. Synthetic data from these simulations can replace costly and hard-to-obtain real-world datasets, enhancing model accuracy and performance. NVIDIA’s NIM microservices and Omniverse Replicator are key in building these synthetic data pipelines.
Jensen Huang’s vision of ‘Physical AI’ is getting real through NIM and visual AI agents.
Jensen Huang’s vision of ‘Physical AI’ is getting real through NIM and visual AI agents.
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["simulation", "synthetic data", "AI", "data pipeline", "Git", "microservices", "automation", "deep learning", "generative AI", "copilots", "NVIDIA", "R"]
["AI", "deep learning", "generative AI", "copilots", "microservices", "R", "Git", "data pipeline", "synthetic data", "automation"]
As a rare blend of engineering, MBA, and journalism degree, Vandana Nair brings a unique combination of technical know-how, business acumen, and storytelling skills to the table. Her insatiable curiosity for all things startups, businesses, and AI technologies ensures that there's always a fresh and insightful perspective to her reporting. She now hosts her tech segment 'Point Break' on AIM Tv.