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100 TOPS Embedded Nvidia Ai Module Jetson Orin NX Module 16GB

    Buy cheap 100 TOPS Embedded Nvidia Ai Module Jetson Orin NX Module 16GB from wholesalers
     
    Buy cheap 100 TOPS Embedded Nvidia Ai Module Jetson Orin NX Module 16GB from wholesalers
    • Buy cheap 100 TOPS Embedded Nvidia Ai Module Jetson Orin NX Module 16GB from wholesalers
    • Buy cheap 100 TOPS Embedded Nvidia Ai Module Jetson Orin NX Module 16GB from wholesalers
    • Buy cheap 100 TOPS Embedded Nvidia Ai Module Jetson Orin NX Module 16GB from wholesalers

    100 TOPS Embedded Nvidia Ai Module Jetson Orin NX Module 16GB

    Ask Lasest Price
    Brand Name : NVIDIA
    Model Number : Jetson orin NX 16G module
    Price : To be discussed
    Payment Terms : L/C, D/A, D/P, T/T
    Delivery Time : 15-30 work days
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    100 TOPS Embedded Nvidia Ai Module Jetson Orin NX Module 16GB


    100 TOPS Embedded Nvidia Ai Module Jetson Orin NX Module 16GB

    NVIDIA Jetson AGX Orin NX 16GB Module

    NVIDIA Jetson Orin NX modules deliver up to 100 TOPS of AI performance in the smallest Jetson form factor, with power configurable between 10W and 25W. This gives you up to 3X the performance of Jetson AGX Xavier and up to 5X the performance of Jetson Xavier NX.



    AI is Now Everywhere

    As businesses across industries grapple with vast amounts of data, more complex operations, and more dynamic markets, edge AI is playing a growing role in helping them rapidly respond. Through a combination of computing power, AI technology, data analytics, and advanced connectivity, the edge extends compute capabilities from data centers out to the edge of networks, allowing organizations to act quickly on data where it’s captured. Reducing the distance between where data is captured and where it’s processed not only alleviates data transit costs, but also improves latency, bandwidth utilization, and infrastructure costs.

    NVIDIA Jetson AGX Orin NX 16GB Module Technical Specifications

    Brand

    NVIDIA

    Module

    NVIDIA Jetson AGX Orin NX 16GB

    AI Performance

    100 TOPS

    GPU

    1024-core NVIDIA Ampere architecture GPU with 32 Tensor Cores

    GPU Max Frequency

    918 MHz

    CPU

    8-core Arm® Cortex®-A78AE v8.2 64-bit CPU
    2MB L2 + 4MB L3

    CPU Max Frequency

    2 GHz

    DLA Max Frequency

    614 MHz

    DL Accelerator

    2x NVDLA v2

    Vision Accelerator

    1x PVA v2

    Safety Cluster Engine

    -

    Memory

    16GB 128-bit LPDDR5
    102.4GB/s

    Storage

    -
    (Supports external NVMe)

    Camera

    Up to 4 cameras (8 via virtual channels***)
    8 lanes MIPI CSI-2
    D-PHY 2.1 (up to 20Gbps)

    Video Encode

    1x 4K60 (H.265)
    3x 4K30 (H.265)
    6x 1080p60 (H.265)
    12x 1080p30 (H.265)

    Video Decode

    1x 8K30 (H.265)
    2x 4K60 (H.265)
    4x 4K30 (H.265)
    9x 1080p60 (H.265)
    18x 1080p30 (H.265)

    PCIe

    1 x4 + 3 x1
    (PCIe Gen4, Root Port, & Endpoint)

    USB

    3x USB 3.2 Gen2 (10 Gbps)
    3x USB 2.0

    Networking

    1x GbE

    Power

    10W - 25W

    Display

    1x 8K30 multi-mode DP 1.4a (+MST)/eDP 1.4a/HDMI 2.1

    Other I/O

    3x UART, 2x SPI, 2x I2S, 4x I2C, 1x CAN, DMIC & DSPK, PWM, GPIOs

    Mechanical

    69.6mm x 45mm
    260-pin SO-DIMM connector











































    Addressing the Requirements of Edge AI

    AI at the edge comes with a unique set of requirements. Edge systems, dispersed across vast physical distances, lack the centrality that a data center presents. Software or system updates either need to be performed manually or to be centrally managed to easily deploy, manage, and scale software across vast fleets of devices. Moreover, the security requirements for edge computing infrastructure differ from cloud or data center computing models. Edge locations lack the physical security that data centers have, so an end-to-end security model that protects both the application intellectual property and the sensor data is paramount for a successful deployment.



































































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