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Nvidia Unveils Affordable Generative AI Supercomputer: The Jetson Orin Nano Super



Nvidia has launched the Jetson Orin Nano Super, an affordable development board aimed at developers and hobbyists looking to experiment with generative AI. The device boasts impressive specifications, including 67 TOPS and 102GB/s of memory bandwidth.

  • The Nvidia Jetson Orin Nano Super is a new development board for generative AI experiments.
  • It features six Arm Cortex-A78AE cores, an Nvidia GPU with 1024 CUDA cores and 32 tensor cores, and packs 67 TOPS at INT8 performance.
  • The device boasts improved specs over its predecessor, including a 70% uplift in performance and 50% more memory bandwidth.
  • The board has an affordable price tag starting at $249, making it attractive for AI experimentation without breaking the bank.
  • It offers a range of connectivity options, including gigabit Ethernet, DisplayPort, USB 3.2 Gen 2 ports, and expansion headers.
  • Nvidia provides extensive software support for the Jetson Orin Nano Super, including its GPU software suite and support for robotics or vision processing workloads.



  • Nvidia, a leader in the field of artificial intelligence (AI) and graphics processing units (GPUs), has recently announced the launch of its new development board, the Jetson Orin Nano Super. This diminutive dev kit is aimed at developers and hobbyists looking to experiment with generative AI. The device boasts impressive specifications, including six Arm Cortex-A78AE cores, an Nvidia GPU based on its older Ampere architecture with 1024 CUDA cores and 32 tensor cores.

    In terms of performance, the Jetson Orin Nano Super packs 67 TOPS (tera operations per second) at INT8, which is faster than the NPUs in any of Intel, AMD, or Qualcomm's latest AI PCs backed by 8GB of LPDDR5 memory capable of 102GB/s of memory bandwidth. According to Nvidia, these specs reflect a 70 percent uplift in performance and 50 percent more memory bandwidth than its predecessor.

    The design of the Orin Nano appears to be identical to the original Jetson Orin Nano, but with some notable improvements. The board features six Arm Cortex-A78AE cores along with an Nvidia GPU based on its older Ampere architecture with 1024 CUDA cores and 32 tensor cores. This design provides a solid foundation for developers looking to build their own generative AI projects.

    One of the standout features of the Jetson Orin Nano Super is its affordable price tag, which starts at $249 compared to the original's $499 price. This makes it an attractive option for those who want to experiment with AI without breaking the bank.

    In addition to its impressive performance and affordability, the Jetson Orin Nano Super also features a range of connectivity options, including gigabit Ethernet, DisplayPort, four USB 3.2 Gen 2 type-A ports, USB-C, dual M.2 slots with M and E keys, along with a variety of expansion headers.

    For developers looking to get started with AI, Nvidia has provided extensive software support for the Jetson Orin Nano Super. This includes broad support for Nvidia's GPU giant's software suite, including Nvidia Isaac, Metropolis, and Holoscan, to name a few. Additionally, the dev kit also supports up to four cameras for robotics or vision processing workloads.

    Nvidia is also rolling out a software update to its older Jetson Orin NX and Nano system on modules, which it says should boost GenAI performance by 1.7x, making it an attractive option for those who have already invested in the Orin Nano dev kit.

    In conclusion, the Nvidia Jetson Orin Nano Super is a formidable development board that offers impressive performance, affordability, and extensive software support for developers looking to experiment with generative AI. With its robust features and competitive pricing, this device is sure to be a hit among enthusiasts and professionals alike.




    Related Information:

  • https://go.theregister.com/feed/www.theregister.com/2024/12/17/nvidia_jetson_orin/


  • Published: Tue Dec 17 15:37:56 2024 by llama3.2 3B Q4_K_M











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