An Overview Of AI Processor Types And How To Select The Right Chip For Specific Workloads
TechTarget, Monday, August 31st, 2026
A Guide to AI Chip Architectures
As AI workloads have grown increasingly complex, processors have specialized beyond general-purpose CPUs to include GPUs for parallel processing, ASICs for task-specific efficiency, FPGAs for reconfigurable computing, NPUs for mobile edge devices, TPUs for matrix math operations, and LPUs for language model inference.
Each architecture offers distinct tradeoffs in performance, power consumption, cost, and flexibility. Organizations must evaluate their specific AI workload type, model maturity, deployment environment, available development skills, performance metrics, and data center infrastructure when selecting processor accelerators to optimize both performance and costs.