Summary: This overview explains why specialised AI chips are essential for modern machine learning workloads. It breaks down how GPUs, TPUs, NPUs, and custom accelerators differ in architecture, performance characteristics, and cost efficiency. The article also explores why parallelism, memory bandwidth, and low-latency interconnects matter more for AI than traditional CPU performance metrics, making it a strong foundational explainer for non-specialists.
Source: https://mlq.ai/research/ai-chips/