The question sounds straightforward, but the answer depends on a chain of decisions — model size, inference pattern, concurrency requirements, latency targets, and whether you are running training workloads alongside serving. Getting infrastructure sizing wrong in either direction is. Similar to the regular server configuration, artificial intelligence servers also include a CPU (central processing unit), GPU (graphics processing unit), RAM (memory), and storage (SSD or NVMe). Each of these components offers distinct. An AI server is more than just a high-powered version of a regular server. It's a specialized system built from the ground up to excel at one thing: running artificial intelligence workloads. This includes compute-heavy tasks like training large language models, processing real-time predictions. Your AI server CPU requirements: 4–16 vCPU (or more for parallel ETL), RAM sized at 2–3× the largest dataset in memory, and NVMe sustained read/write above your data loader rate. When you build an AI server for this profile, start with RAM and storage planning; if the pipeline is input/output. Modern CPUs typically contain multiple cores—anywhere from 8 to 128 in server configurations—allowing some parallel processing. GPUs significantly accelerate AI workloads like deep learning. Optimize networks to minimize latency for faster AI.