AI, Machine Learning and Deep Learning all relies on data. DPUs are important computing components to improve performance, efficiency, and cost-effectiveness of data processing tasks, particularly for applications that require high-speed data processing or real-time decision-making. DPUs are used in a wide range of applications, including data centers, cloud computing, edge devices, autonomous vehicles, to name a few.

Utilizing DPUs effectively not only offloads workloads from other parts of the data computing system, but also leads to higher performance. This is because DPUs are vital components in specialized hardware units that accelerate specific data processing tasks and provide higher performance and energy efficiency than traditional general-purpose processors like CPUs or GPUs. White-box DPU comes in different variants based on the different use cases and applications that each variant is targeting. This brings great flexibility across the market.

• Specifications

Processor:	1 x Marvell CN106XX ARM Neoverse N2
Storage:	1x M.2 (Key M) NVMe SSD 1x eMMC 5.1 (8 to 64GB)
Power:	Goldfinger (Max.: 75W) as primary ATX connector for additional power input (75W)
OS Support:	Ubuntu 20.04 (Linux Kernel: 5.4)
Expansion:	1x M.2 M Key for SSD (PCIe 5.0 x2; FF: 2230)
Ethernet:	2 x QSFP56 100 GbE Port(s) on board Up to 2 x Ethernet Port(s)
USB:	1 x USB 3.0 Port(s)
Storage Temperature:	Temperature: -10 - 70°C (14 - 158°F)
Operating Temperature:	Temperature: 0 - 40°C (32 - 104°F)
Dimension:	111.15mm x 167.65mm x 20.32mm (4.446" x 6.706" x 0.8128")
Console:	1x 1GbE/100MbE RJ45 (MCU) 1x USB Type-C Console
Memory:	2 x DDR5 ECC (Up to 32GB)

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