Improvements in AI performance did not begin prior to the advent of the GPU; rather, it was the emergence of the GPU that truly ignited the breakthroughs in modern AI—particularly deep learning.
GPUs represent the critical turning point in the AI boom.
GPUs marked a pivotal turning point in the AI boom: in 2012, University of Toronto students used two NVIDIA graphics cards to run AlexNet, achieving image recognition capabilities that far surpassed traditional algorithms—a milestone regarded as the "technological singularity" of deep learning. Prior to this, AI largely relied on a "rule-based" paradigm; the parallel computing power of GPUs made it possible to "discover patterns autonomously from massive datasets."
AI existed before GPUs, but faced significant bottlenecks. Early researchers realized that machine learning algorithms shared matrix-based computational characteristics with graphics processing, leading to the insight that GPUs could replace CPUs for more efficient computation. However, CPU-based computing struggled to scale due to limitations in availability and performance.
The reason GPUs are irreplaceable lies in their architecture: while CPUs excel at complex logic but lack parallel processing capabilities, GPUs feature numerous computing cores specialized for repetitive tasks such as matrix operations and convolutions—enabling them to train models like ResNet more than ten times faster than CPUs.