reComputer J1010 -Edge AI Computer with NVIDIA® Jetson Nano 4GB

https://www.ram-e-shop.com/web/image/product.template/8640/image_1920?unique=8fdeeb8 NVIDIA.RECOMPUTER.J1010

A hand-sized edge AI box with 0.5 TFLOPs of power, built with the NVIDIA Jetson Nano 4GB

22,500.00 EGP 22500.0 EGP 22,500.00 EGP

22,500.00 EGP

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Internal Reference: NVIDIA.RECOMPUTER.J1010

reComputer J1010 Edge AI Computer (NVIDIA Jetson Nano 4GB / Seeed Studio)

Product Description

The reComputer J1010 by Seeed Studio is a compact, production-ready Edge AI computer powered by the NVIDIA Jetson Nano 4GB production module. Engineered for real-world Edge AI applications, object detection, computer vision, and automated robotics, it combines an industrial-grade carrier board and aluminum enclosure with NVIDIA JetPack software stack support.

Delivering up to 472 GFLOPS of AI computing performance, the reComputer J1010 is equipped with a Quad-Core ARM Cortex-A57 CPU and a 128-core NVIDIA Maxwell GPU. Pre-installed with NVIDIA JetPack system software on built-in 16GB eMMC flash storage, it provides rich expansion interfaces including Gigabit Ethernet, USB 3.0, HDMI, MIPI-CSI camera connector, and M.2 Key E for wireless connectivity. It is widely used in smart cities, autonomous mobile robots (AMR), industrial quality control, intelligent video analytics, and AI education.

Key Features

  • NVIDIA Jetson Nano Powered: Driven by the NVIDIA Jetson Nano 4GB production module delivering 472 GFLOPS of AI compute performance.
  • Compact Aluminum Enclosure: Housed in an industrial black aluminum enclosure with passive/active cooling design for real-world deployment.
  • Built-In eMMC Storage: Equipped with 16GB eMMC storage pre-loaded with JetPack system software for reliable high-speed booting.
  • Rich Peripheral Interfaces: Includes Gigabit Ethernet, USB 3.0 Type-A, USB 2.0, HDMI 2.0 output, and MIPI-CSI camera connector.
  • M.2 Key E Expansion: Features an M.2 Key E slot for adding Wi-Fi and Bluetooth wireless expansion modules.
  • Full JetPack Stack Support: Fully compatible with NVIDIA JetPack SDK, CUDA, cuDNN, TensorRT, and DeepStream SDK for AI vision pipelines.

Product Specifications

  • Manufacturer: Seeed Studio
  • Model: reComputer J1010 (Product SKU 110061361)
  • AI Module: NVIDIA Jetson Nano 4GB (Production Module)
  • AI Performance: 472 GFLOPS
  • CPU: Quad-Core ARM Cortex-A57 MPCore Processor operating at 1.43 Gigahertz
  • GPU: 128-core NVIDIA Maxwell Architecture GPU
  • System Memory: 4 Gigabytes 64-bit LPDDR4 (25.6 Gigabytes per second)
  • On-Board Storage: 16 Gigabytes eMMC 5.1 Flash Storage (Expandable via MicroSD slot)
  • Video Output: HDMI 2.0 (up to 4K resolution at 60 Hertz)
  • Networking: 1 Port Gigabit Ethernet (10/100/1000 Megabits per second, RJ45)
  • USB Ports: 1 Port USB 3.0 Type-A, 2 Ports USB 2.0 Type-A, 1 Port Micro-USB (Device Mode / Power)
  • Camera Interface: 1 Port MIPI-CSI 2-lane connector
  • Expansion Slot: 1 Slot M.2 Key E (2230) for Wi-Fi / Bluetooth modules
  • GPIO Header: 40-pin expansion header (UART, SPI, I2C, I2S, PWM, GPIO)
  • Power Supply Input: 5 Volts DC at 3 Amperes via Type-C connector or Micro-USB
  • Operating Temperature Range: minus 10 degrees Celsius to plus 60 degrees Celsius

Interface and Port Connections

When viewing the rear and side connector panels of the reComputer J1010 enclosure:

  • HDMI Port: Video output to external 4K monitor or display panel.
  • Gigabit Ethernet (RJ45): Wired network connection for high-speed data transfer and remote access.
  • USB 3.0 Port: High-speed USB input/output for external SSD storage or HD USB cameras.
  • USB 2.0 Ports: Peripheral connections for keyboard, mouse, or serial adapters.
  • MIPI-CSI Port: Internal ribbon cable connection for Raspberry Pi Camera Module v2 or IMX219 sensors.
  • Power Input (Type-C / Micro-USB): 5 Volts DC power input terminal.

Power Supply and Thermal Requirement

The reComputer J1010 requires a stable 5 Volts DC power supply capable of delivering at least 3 Amperes (15 Watts). Using undersized power adapters or low-quality cables will cause brownouts, CPU throttling, or system crashes during heavy GPU/AI inference workloads. Ensure adequate airflow around the aluminum housing when running continuous high-load CUDA tasks.

Datasheet