AI INFRASTRUCTURE
GPU Servers Explained
A technician-level introduction to servers built around accelerators for AI and other highly parallel workloads.
CPU and GPU roles
CPUs are general-purpose processors; GPUs contain many processing units suited to highly parallel workloads. Modern AI systems often use both.
Server architecture
GPU servers may include multiple accelerators, high-speed interconnects, large memory/storage capacity, high-power supplies, and specialized networking.
Physical considerations
Weight, power draw, airflow or liquid connections, cable density, and service procedures can differ from conventional servers.
Network importance
Distributed AI workloads depend on fast communication between systems, making network design and optics/cabling critical.
Technician discipline
Follow approved lift, power, ESD, cooling, cabling, firmware, replacement, and validation procedures for the specific platform.
KEEP BUILDING
Turn this topic into a skill
Use TechLoomix's career tools, technical calculators, learning paths, and related guides to practice what you learned.