How to select edge inference servers for enterprise data centers? Using a port intelligent tallying project as a sample, this article breaks down the selection logic from pain points and solutions to data results, and offers evaluation criteria for source computing infrastructure manufacturers.

Event Overview

A port container terminal previously relied on manual tally verification, which was inefficient and error-prone. The high-temperature, high-humidity, salt-spray corrosive environment imposed strict requirements on equipment reliability. During selection, the project team required an enterprise data center edge inference server capable of on-site video intelligent analysis at the edge to reduce cloud bandwidth and latency. Stonbel provided integrated support including wide-temperature industrial storage, AI compute nodes, and explosion-proof enclosures, bringing inference capability down to the operation site.

Key Points

The enterprise data center edge inference server is based on a domestic AI acceleration chip, offering INT8 computing power of 22/16 TOPS and FP16 computing power of 11/8 TFLOPS, LPDDR4X 8GB/4GB memory, support for 16-channel 1080P encoding/decoding, power consumption of only 25W (diskless)/40W (with disk), operating temperature range of -40~70°C (diskless)/-40~60°C (with disk), and dimensions of 45×235×220 mm. Compared with general GPU edge solutions, it is better suited for video intelligent analysis scenarios in terms of power consumption and compute density, and domestic adaptation also reduces replacement costs for Xinchuang projects.

Official Statement and Follow-up

Project results show that tally efficiency improved 3 times, manual verification workload reduced by 70%, recognition accuracy reached 98.5%, and annual equipment failure rate was below 1%. The case was rated as a smart port demonstration project. Stonbel stated that selection of enterprise data center edge inference servers should prioritize wide-temperature specifications, encoding/decoding channel count, and domestic adaptation capability. Going forward, Stonbel will continue to improve unified cloud-edge collaborative management and 7×24 original factory service network to help customers shorten project deployment cycles.

Q1: How to choose an enterprise data center edge inference server? What parameters matter most?

A: First check whether computing power and power consumption match the scenario. For video intelligent analysis, INT8 computing power of 22/16 TOPS and 16-channel 1080P encoding/decoding can meet multi-camera edge inference needs; power consumption of 25W (diskless)/40W (with disk) facilitates deployment without a dedicated equipment room. Also confirm whether the operating temperature covers -40~70°C (diskless) and whether it is compatible with domestic CPUs and operating systems to meet Xinchuang compliance requirements.

Q2: What is the difference between an edge inference server and a general GPU edge solution?

A: They serve different purposes. The enterprise data center edge inference server consumes about 25~40W, has good domestic Xinchuang adaptation, and suits video intelligent analysis scenarios such as factories, intersections, and stations. General GPU solutions have a broader ecosystem and suit general AI inference, but power consumption and domestic replacement costs are higher. If the project mainly involves multi-channel video structuring and requires wide-temperature operation, prioritize evaluating the compute density and stability of the edge inference architecture.

From port tallying to industrial production lines, the value of edge inference servers in enterprise data centers lies in moving intelligent analysis closest to the data. When selecting, focus on three main lines: wide temperature range, computing power density, and domestic adaptation, then evaluate long-term O&M costs combined with the manufacturer's service network to ensure edge computing truly supports 7×24 highly reliable operation.