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Robotics & Physical AI3 min read

NVIDIA says robotaxi leaders use its stack for $400B market

NVIDIA reports that its modular full-stack robotaxi pipeline, known as the "three-computer solution," is being adopted by major global robotaxi programs. The company projects the robotaxi market will reach $400 billion by 2035, with over 6 million commercial vehicles in operation. This growth requires significant computing power for data preparation, model training, simulation, and real-time…

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Key points

  • NVIDIA projects the robotaxi market will reach $400 billion by 2035 with over 6 million vehicles.
  • The "three-computer solution" includes DGX for training, Omniverse for simulation, and DRIVE AGX for in-vehicle use.
  • Uber, Waymo, Zoox, and Mercedes-Benz are among the companies adopting NVIDIA's robotaxi technology stack.

The solution comprises three parts: NVIDIA DGX for training driving models, NVIDIA Omniverse and Cosmos for simulation and validation, and NVIDIA Hyperion with DRIVE AGX for in-vehicle computing. The Alpamayo portfolio, which includes open inference VLA models, reportedly improved trajectory prediction accuracy by 43% in one evaluation. The in-vehicle system uses a Blackwell-based dual DRIVE AGX Thor SoC with 14 cameras, 9 radars, 3 LiDARs, and 12 ultrasonics to achieve L4 driving.

NVIDIA states that nearly every major commercial program uses its stack. Uber is expanding its Hyperion fleet to 28 cities by 2028. Other adopters include Waymo, Zoox, Pony.ai, WeRide, and Mercedes-Benz. Tesla uses NVIDIA supercomputers for training its autonomous driving networks. The company positions its infrastructure as essential for the large-scale deployment of driverless fleets.

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Full story from note.com · by TechSage · via Search: NVIDIAOpen source ↗

Introduction to the NVIDIA Official Blog Post: 'Physical AI Takes the Wheel: How the World’s Robotaxi Leaders Are Building With NVIDIA Technologies'

note.com · 24 October 2026

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This text was published by note.com and written by TechSage. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗

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The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.

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