PCB Blogger-China PCB Factory,HOYOGO

        AI computing, cloud data centers, and high-performance computing platforms handle large volumes of network, storage, and security-related data traffic. DPUs (Data Processing Units) can handle tasks such as network transmission, storage access, security, and infrastructure management, offloading some infrastructure processing workloads from host CPUs. As DPU network bandwidth and host interface speeds increase, high-speed interconnections on accelerator cards place greater demands on PCB signal transmission performance, manufacturing precision, and long-term reliability.   DPU accelerator cards typically integrate DPU chips, high-speed network interfaces, memory, power circuits, and other functional components, and connect to server systems through high-speed interfaces such as PCIe. Different products vary in network speeds, interface configurations, board architectures, and functional designs. PCB materials, stack-up structures, and interlayer interconn...

       AI training, inference, and large-scale data processing involve frequent read and write operations for models, datasets, and intermediate data, placing high demands on the data throughput and access efficiency of server storage systems. NVMe (Non-Volatile Memory Express) SSDs based on the PCIe interface are widely used in server and data center storage systems. In some AI servers, local NVMe SSDs are used for system storage, data caching, and temporary data read/write operations, providing high-speed local storage support for computing workloads.   AI servers can be configured with NVMe SSDs of different specifications and form factors according to system architecture and storage requirements. As PCIe data rates increase, meeting signal integrity requirements calls for tighter control of PCB material properties, circuit fabrication precision, impedance, and via structures. Different servers and NVMe storage devices vary in interfaces, board  structur...