# 🚀 The Rise of AI Hardware: Why GPUs and Custom Chips Dominate AI

Over the past decade, AI progress hasn’t just been about better algorithms. It has been powered by a revolution in hardware. The shift from CPU → GPU → custom AI chips has fundamentally changed how we train and deploy machine learning systems. Let’s break down what’s actually happening behind the scenes. 1 Why GPUs Became the Backbone of AI Traditional CPUs are designed for sequential processing — executing one task after another efficiently. But AI workloads look very different. Training neural networks involves massive operations like: Matrix multiplication Tensor operations Convolutions Vector transformations These calculations can be executed simultaneously across thousands of data points. GPUs are built exactly for this. They contain thousands of smaller cores that can run operations in parallel, making them ideal for large-scale machine learning computations. ( Medium ) This architecture allows GPUs to train deep learning models 10× to 100× faster than CPUs in many workloads. ( M