This article from Jon Peddie Research traces the transformation of the graphics processing unit (GPU). Originally designed for fixed-function rendering pipelines, early GPUs handled tasks such as rasterizing triangles, texture mapping, and lighting.
With the introduction of programmable shaders around the early 2000s, GPUs opened up to custom parallel workloads. Developers began using vertex and pixel shaders (and later compute shaders) to run broad classes of vector arithmetic operations.
The shift toward general-purpose GPU computing picked up momentum when programming frameworks such as CUDA (by NVIDIA) and OpenCL emerged, enabling C/C++-style kernels on GPU hardware. By 2009, researchers had begun training neural networks on GPUs, cutting training time from weeks to days.
A major inflection came with the 2012 AlexNet breakthrough, which demonstrated the power of deep convolutional neural networks (CNNs) on GPUs for image recognition tasks. Hardware followed the software: tensor cores, mixed-precision arithmetic, high-bandwidth memory (HBM), and advanced interconnects became standard. Training now targets throughput and memory capacity; inference emphasizes latency, efficiency, quantization, and sparsity.
The article also highlights the convergence of graphics, AI training, inference, and edge deployments. Modern GPUs now operate in data centers, in edge devices, in vehicles, and across IoT. They coexist with neural processing units (NPUs), compute-in-memory (CIM) chips, neuromorphic accelerators, and CPUs. Gaming hasn’t been left behind: features originally designed for AI (tensor units) feed consumer applications such as frame-generation and image upsampling via DLSS/FSR, bridging the gap between professional AI and consumer graphics.
The GPU’s journey from a niche graphics engine to a central pillar of AI infrastructure underscores how computing demands have shifted. What matters now is parallel math, data movement, and memory bandwidth, not just frames per second. For engineers in the technology and infrastructure space, this means the tools, architectures, and software stacks of graphics are now integral to AI at scale.