Core Insights - NVIDIA has launched the Jetson Thor, a new robotic computing platform that integrates server-level computing power into robots, achieving an AI performance of 2070 TFLOPS, which is 7.5 times higher than the previous generation Jetson Orin, with a 3.5 times improvement in energy efficiency [1][3][4]. Performance and Specifications - Jetson Thor features a massive 128GB memory configuration, unprecedented in edge computing devices [2]. - The platform is built on the Blackwell GPU architecture, supporting multiple AI models simultaneously on edge devices [6]. - The Jetson AGX Thor developer kit is priced at $3499 in the U.S. (approximately 25,000 RMB), while the T5000 module is available for $2999 for bulk purchases [8][9]. Technical Features - The Jetson Thor includes advanced specifications such as a GPU with 2560 CUDA cores and 96 fifth-generation Tensor Cores, and a CPU with 14 Arm Neoverse V3AE cores, significantly enhancing real-time control and task management capabilities [11][13]. - It supports high bandwidth with 128GB LPDDR5X memory and 273GB/s memory bandwidth, crucial for large Transformer inference and high-concurrency video encoding [13]. - The platform can achieve a response time of 200 milliseconds for the first token and generate over 25 tokens per second, enabling real-time human-robot interaction [16]. Industry Adoption - Several Chinese companies, including Unisound Medical and Youbik, are integrating Jetson Thor into their systems, highlighting its impact on robot agility, decision-making speed, and autonomy [19]. - Boston Dynamics is incorporating Jetson Thor into its Atlas humanoid robot, allowing it to utilize computing power previously only available in servers [20]. - Agility Robotics plans to use Jetson Thor as the core computing unit for its sixth-generation Digit robot, enhancing its logistics capabilities [21]. Software and Development - Jetson Thor is optimized for various AI frameworks and models, supporting NVIDIA's Isaac for simulation and development, and Holoscan for sensor workflows [14]. - The platform facilitates a continuous training-simulation-deployment cycle, ensuring ongoing upgrades to robotic capabilities even after deployment [25]. Future Outlook - NVIDIA emphasizes the need for a triad of computing systems for effective physical AI and robotics: a DGX system for training, an Omniverse platform for simulation, and the Jetson Thor as the robot's brain [23].