Comparative Analysis of AI Server Types and Guidelines for Selecting GPU Servers

Different types of AI servers are compared using a two‑dimensional chart that plots performance on the horizontal axis and programmability/flexibility on the vertical axis, covering CPU, GPU, FPGA, TPU and ASIC.

ASIC offers the highest performance but the lowest programmability, while CPUs are the most flexible but have the weakest performance; GPUs sit in the middle, followed by FPGA and TPU.

When choosing a server, factors such as power consumption, cost, performance, real‑time requirements, precision, memory capacity, bus standards and cooling must be weighed. For fixed, simple algorithms ASICs are attractive; for training or general workloads GPUs are preferred.

The article then outlines basic principles for selecting GPU servers, describing common GPU families (NV‑Link, traditional bus, PCI‑e) and representative models such as NVIDIA V100 (NV‑Link), P40, P4, T4, and their typical deployment scenarios.

NV‑Link‑based servers include NVIDIA’s DGX supercomputers and partner‑designed NV‑Link servers, while PCI‑e servers can be OEM (e.g., Sugon, Inspur, Huawei) or non‑OEM solutions.

Selection criteria also involve business needs: precision (single vs double), memory size for data‑intensive tasks, required bus interface, edge vs central inference, throughput, user expertise, and accompanying software/services.

Finally, the maturity of the GPU cluster ecosystem and engineering efficiency—such as the integrated OS, drivers, Docker support in DGX systems—are highlighted as important for operational productivity.