AI server components cover the discrete hardware units used to build or upgrade servers purpose-built for machine learning training, inference, and high-performance computing workloads. The category includes GPU and accelerator cards (NVIDIA H100, H200, A100, L40S, AMD Instinct series), high-bandwidth memory modules (DDR5, HBM), NVLink and NVSwitch fabric boards, OAM and SXM carrier boards, PCIe retimers and risers, and compatible server motherboards and chassis designed around multi-GPU topologies. Cooling hardware—direct liquid cooling plates, manifolds, and high-static-pressure fans—is also typically required when deploying accelerators with 300–700 W TDP envelopes.

Key buying considerations include PCIe generation and lane count (Gen 4 vs. Gen 5), form factor compatibility (SXM5 vs. PCIe HHHL/FHFL), inter-GPU bandwidth requirements (NVLink vs. PCIe fabric), and power delivery capacity (GPU power connectors, PSU wattage, and 80 PLUS rating). These components are used by teams building on-premises LLM training clusters, enterprise inference nodes, scientific simulation platforms, and rendering farms where GPU density and memory bandwidth are the primary performance constraints.