What happened
Nvidia, founded in 1993, initially developed Graphics Processing Units (GPUs) for 3D gaming, launching the GeForce 256 in 1999. A pivotal shift occurred in 2006 with CUDA, a software platform enabling developers to program GPUs for general-purpose parallel computing. This capability was later leveraged in 2012 by researchers using Nvidia GPUs to train AlexNet, revolutionising image recognition. This established GPUs as powerful tools for AI training, leading to the 2016 launch of the DGX-1, a machine purpose-built for AI.
Why it matters
This evolution established Nvidia's GPUs as the foundational computing architecture for artificial intelligence. For platform architects and investors, this demonstrates how specialised hardware, initially designed for graphics, became indispensable for processing massive datasets and training complex AI models, a task traditional CPUs performed much slower. The mechanism of parallel processing, unlocked by CUDA, directly enabled the scale and speed required for modern AI development, shifting computing paradigms and creating a new market for high-performance accelerators.
