AI servers evolve for faster speed, higher performance and larger capacity, so PCB parameters and performance improve accordingly.
The prior article introduced PCB types and AI PCB application areas primarily. In this article, we will specifically introduce the improvement of PCB performance under AI computing power.
(1) PCIe Models:
GPU cards are installed through the PCIe slots on the server. GPU cards are interconnected via the PCIe bus, an internal bus and a computer expansion bus standard.
A high-speed serial bus with high bandwidth. It connects motherboard peripherals like graphics and network cards. PCIe is not limited to motherboards; it is also used for interconnections between many chips.
(2) NVLink/SXM Models:
NVIDIA’s solution for high-performance GPU interconnection. It uses a proprietary protocol laid on the circuit board, similar to how GPUs are mounted—directly on the circuit board. GPUs are interconnected via NVLink links.
(3) Value Quantity:
The value of components such as PCB, memory, power supply, and SSD all increases. When upgrading from a regular server to an AI training server, the value of components such as memory, SSD, PCB, and power supply increases several times over. This is driven by the extreme demands of AI training on hardware performance:
Memory:
AI training handles massive datasets and complex neural network models simultaneously. Requirements for memory capacity, bandwidth, and speed have thus increased geometrically.
Regular servers usually use tens of GB of DDR4 memory.AI training servers require hundreds of GB or higher DDR5 (or advanced) memory.This supports high-concurrency data exchange in multi-GPU parallel computing.
Its value increases several times over with the upgrade in capacity and specifications.
SSD:
AI training needs frequent dataset loading and result caching.Its storage speed and capacity are much higher than regular servers.
Typical server SSDs are mostly 100GB-level SATA interfaces.AI training servers use terabyte-level NVMe SSDs (or SCM).These satisfy read/write demands of several gigabytes per second.
This performance upgrade of storage media directly drives a significant increase in its value.
PCB (Printed Circuit Board):
AI training servers integrate multiple high-power GPU cards and high-speed interconnect modules, placing stringent requirements on the number of PCB layers, materials, and signal integrity. Typical server PCBs are mostly low-layer, conventional board materials, while AI servers require high-multilayer PCBs (even rigid-flex PCBs) and high-speed copper-clad laminates to ensure the stability of high-speed signal transmission and high-current power supply. The increased complexity of the manufacturing process and the higher material costs multiply its value.
Power Supply:
AI training servers consume extremely high power (up to several kilowatts per unit), requiring high-power, high-efficiency power supply modules. While typical server power supplies operate at a few hundred watts, AI server power supplies jump to over 2000W and must meet the 80PLUS Titanium efficiency standard to minimize energy loss. This upgrade in power and performance significantly increases their value.
This increased value of components is essentially a result of the “high computing power, big data, and high power consumption” characteristics of AI training forcing hardware performance upgrades. It is also one of the core reasons why the hardware cost of AI training servers is significantly higher than that of ordinary servers.
Customized tech upgrades for AI scenarios drive changes.They boost AI server PCB material costs and process complexity.In turn, this multiplies the value of AI server PCBs several times.
This is not only the result of PCB’s own technological iteration but also a reflection of its value as the core carrier of AI computing power.
In today’s era of full AI penetration, computing power competition has become a key determinant of enterprises’ core competitiveness. As the “neural hub” of AI servers, PCB performance upgrades directly determine the efficiency of AI computing power release. We specialize in the R&D of AI-specific PCBs, leveraging high-layer design, high-speed substrate application, and precise process control to provide stable, efficient interconnection solutions for AI servers. Partner with us to seize the computing power advantage in the intelligent era.
