GPU / VPU Accelerated Computing

Industrial Computing

Let us help you select the right system to drive your robotics, deep learning, video analytics, and machine vision applications. GPU Computing is redefining the limits of artificial intelligence, autonomous vehicles, drones, and robots.

CoastIPC can help you build the right GPU computing platform for computer-intensive applications such as deep learning and artificial intelligence, machine vision, robotics, and video analytics. Whether you need an edge GPU platform equipped with dual GPU slots or a GPU NVIDIA system ready to go out of the box, CoastIPC can deliver. We carry industrial PCs from leading manufacturers, offering systems featuring the NVIDIA Jetson, NVIDIA Tegra, NVIDIA Tegra X2, NVIDIA Nano, and NVIDIA AGX Xavier and more. 

We can also help with your vision processing unit (VPU) or Tensor Processing Unit (TPU) hardware. CoastIPC offers AI inference systems with expansion for VPU cards and TPU cards, or we can equip your device with a VPU/TPU to make it plug-and-play ready. Contact us today to learn more about our GPU, TPU, and VPU computing options.

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  1. MIC-770 Advantech MIC-770 Compact Fanless System with 8th Gen Intel Core i CPU

    Advantech MIC-770 Compact Fanless System with 8th Gen Intel Core i CPU

    MIC-770

    Main Features

    • Intel® 8th/9th Gen Core™ i CPU socket-type (LGA1151) with Intel® Q370/H310 chipset
    • Wide operating temperature (-10 ~ 50 °C)
    • ƒVGA and HDMI output
    • ƒ2 x GigaLAN, 2 x USB 3.1 and 6 x USB 3.0
    • 2 x RS-232/422/485 and 4 x RS232 serial ports (Optional)
    • 1 x 2.5" HDD/SSD, and 1 x mSATA
    • 9 ~ 36 VDC input power range
    • Supports FlexIO and iDoor technology, flexible configure additional HDMI, DVI, Comport, DIO, Remote switch IO
    • Supports Advantech i-Modules
    • Supports Advantech SUSIAPI and embedded software APIs
    Learn More
    PDF Document - MIC-770 User Manual
    MIC-770 User Manual
    (Size: 3.2 MB)
    PDF Document - MIC-770 Data Sheet
    MIC-770 Data Sheet
    (Size: 830.4 KB)

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