Zhengyu edge computing Server Multi scenario Coverage Flexible Configuration

hengyu Edge Computing Server - Flexible Configuration for Multiple Scenarios

Zhengyu Edge Computing Server is designed for edge computing scenarios with a large number of adaptive designs, such as shorter chassis depth, wider temperature adaptability, and front maintenance of PCIe devices.

The Zhengyu Edge Computing Server can be equipped with Intel Xeon D series CPUs, supporting up to 512GB DDR4 DIMM, 2 PCIe 3.0 x16 (FH3/4L) slots, 2 PCIe 3.0 x8, 1 PCIe 3.0 x16 (FH3/4L) slot, and up to 4 2.5-inch SATA hard drives. Despite limited space, it still has ideal scalability, integrating computing, storage, and AI acceleration features, and is suitable for applications in industrial Internet, intelligent manufacturing, connected vehicles, 5G Internet, and other scenarios.

New Architecture

Supports the latest generation of Intel Xeon D series processors, with a maximum of 20 cores and equipped with 2 10G SFP+ fiber network interfaces via SoC, 2 10GBase-T RJ45 electrical ports via Intel X557, 8 1GbE with Intel I350-AM4 electrical ports, and 1 1GbE with Intel I210 electrical ports, reducing remote operation latency and increasing work efficiency.

Special Design

Designed with a large number of adaptive designs for edge computing scenarios, to ensure stable edge computing operation, the fan can be replaced without opening the chassis, and the management interface and PCIe devices are all designed for front maintenance, making it easy for maintenance personnel to quickly maintain the server. It has the characteristics of high temperature resistance, dust-proof, and corrosion resistance, and can meet various harsh environmental requirements.

Edge Intelligence

The server supports GPU cards, and with the increase of real-time data and the diversification of data forms, intelligent edge computing with AI capabilities has emerged. By integrating AI and edge computing technologies and deploying edge computing devices, data processing can be ensured to be real-time and local, making it very suitable for AI inference, acceleration, video encoding and decoding, and other application scenarios.

Application Industries

Cloud computing: By placing computing and storage resources closer to the terminal devices, edge computing servers can reduce the latency of data transmission and provide more efficient data processing. For example, when users access video streams through a cloud platform, the edge computing server can cache the video data to the edge node for fast data transmission and processing, providing a better user experience.

Internet: Due to the low latency of edge computing servers, they are crucial for applications and services that require fast response, such as IoT devices, smart homes, online games, and video streaming, enabling fast data processing and timely response.

Big data: Big data usually requires a large amount of computing resources and storage space. Edge computing servers can transfer some data processing and storage tasks from the data center to the edge location, reducing the burden of data transmission and centralized computing. This can significantly improve data processing efficiency and speed, and reduce the demand for network bandwidth.

Artificial intelligence: Edge computing servers can place AI models and algorithms in the edge location, allowing data to be processed and analyzed locally, reducing dependence on cloud computing resources and network transmission. This is important for AI applications that require real-time response, such as intelligent monitoring, intelligent transportation, and AI applications on edge devices.

Virtualization: By virtualizing computing and storage resources to the edge location, resource dynamic allocation and management can be achieved, improving system flexibility and scalability.