Kontron: Embedded Server ZINC CUBE SKD for industrial applications  

Kontron, a leading global provider of IoT/Embedded Computing Technology (ECT), has introduced the new ZINC CUBE SKD Embedded Server. It is ideally suitable as a compact design EmbeddedServer for industrial applications thanks to its high performance, large memory, and ruggedised design offering high resistance to shock and vibration. It is tailored for compute-intensive applications required for processing and analysing large amounts of data, as in machine learning or artificial intelligence. Owing to its modular concept, it is also highly scalable, providing a perfect base for edge and fog computing concepts. The ZINC CUBE SKD is available with powerful Intel Xeon D-2100 processors and each DIMM socket can be equipped with up to 32 GByte DDR4 RAM.

The ZINC CUBE SKD was designed as a platform for industrial applications, machine learning and artificial intelligence. The Embedded Server is based on Intel Xeon D-2100 processors (12C 75W / 8C 65W / 4C 60W) with four to 12 CPU cores. Its eight DIMM sockets support up to 256 GB ECC memory. Additionally, the ZINC CUBE SKD comes with eight 2.5-inch SATA SSD/HDD drive trays as well as one internal M.2 2280 drive, all of which support RAID functionality. The interfaces include two USB 3.0 at the front as well as two USB 3.0 and two USB 2.0 at the back. Four 10 Gigabit SFP+ LAN Ethernet interfaces provide high data throughput. System extensions can be implemented through one double-wide PCIe x16 slot and two PCIe x8 slots. The Kontron ZINC CUBE SKD is certified for Windows 10, Windows Server and Linux Server.

Norbert Hauser, vice president Marketing of Kontron S&T AG: “The rise in Industry 4.0 concepts drives demand for powerful, scalable and robust Embedded Servers tailored for use in a production environment. In fog and edge architectures, they handle analysis and management of the generated data. The ZINC CUBE SKD Embedded Server offers users a reliable and predictable platform to take their production to the next level through machine learning and AI concepts, paving the way for greater efficiency and new business models.”


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