| Specification | Details |
|---|
| Product Name | NVIDIA Jetson Y-C8-DEV-ORIN64G Development System |
| Module | NVIDIA Jetson Orin 64GB SoM |
| AI Performance | Up to ~200 TOPS (INT8) |
| GPU | NVIDIA Ampere GPU with 2048 CUDA cores and 64 Tensor Cores |
| CPU | 12-core Arm® Cortex-A78AE v8.2 64-bit |
| CPU Max Frequency | Up to 2.0 GHz |
| Memory | 64 GB LPDDR5, 256-bit, high-bandwidth memory |
| Storage | 256 GB eMMC onboard; expandable via NVMe M.2 |
| PCIe | PCIe Gen4 lanes for expansion cards |
| USB Ports | Multiple USB 3.2 and USB 2.0 ports |
| Camera Interface | Multiple MIPI CSI-2 lanes |
| Video Encode/Decode | Multi-stream 4K60 encode; 8K decode support |
| Display Output | HDMI 2.1, DisplayPort 1.4, eDP 1.4 |
| Networking | 1 × Gigabit Ethernet |
| I/O Interfaces | UART, SPI, I2C, CAN, GPIO, PWM, Audio, DMIC |
| Power Supply | 12–24V DC input; supports active cooling |
| Power Consumption | 25–50 W typical depending on workload |
| Form Factor | SO-DIMM style Jetson Orin module on a full-featured development carrier board |
| Software Support | NVIDIA JetPack SDK (Linux + AI libraries) |
| Development Focus | AI, robotics, autonomous systems, edge computing, industrial AI |
Description:
The NVIDIA Jetson Y-C8-DEV-ORIN64G Development System is a high-end AI development platform engineered for enterprise-grade edge computing, autonomous machines, robotics, and industrial AI applications. It features the Jetson Orin 64GB System-on-Module, combining a 12-core Arm® Cortex-A78AE CPU with a NVIDIA Ampere GPU featuring 2048 CUDA cores and 64 Tensor Cores, delivering up to 200 TOPS of AI performance for demanding real-time inference and deep learning tasks.
The development system includes 64 GB of LPDDR5 memory and 256 GB of eMMC storage, along with support for NVMe expansion, allowing developers to efficiently manage large AI datasets. Its carrier board exposes multiple high-speed interfaces, including USB 3.2, Gigabit Ethernet, MIPI CSI-2 camera connectors, HDMI, DisplayPort, and eDP outputs, providing flexibility for integrating cameras, sensors, and displays.
Embedded interfaces such as UART, SPI, I2C, CAN, GPIO, and PWM enable seamless hardware integration. The system operates efficiently at 25–50 W, supporting active cooling for sustained performance. With NVIDIA JetPack SDK, developers have access to a full Linux environment and optimized AI libraries like CUDA, TensorRT, and cuDNN, allowing rapid prototyping and deployment of intelligent applications at the edge. This makes it an ideal solution for advanced AI development in robotics, autonomous vehicles, industrial automation, and smart systems.
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