GPU Computing

GPU computing uses graphics processing units—originally designed for rendering 3D graphics in games—for general-purpose parallel computation. This capability has made GPUs the essential hardware for both the gaming industry and the AI revolution.

The connection between gaming and AI is more than incidental. GPUs excel at the matrix multiplication operations that dominate both 3D rendering and neural network training. NVIDIA recognized this dual-use potential early, investing in CUDA (a general-purpose GPU programming framework) alongside its gaming hardware. The result: NVIDIA became one of the world's most valuable companies as demand for AI training and inference compute exploded.

Modern GPUs serve multiple roles simultaneously. They render the ray-traced graphics in AAA games. They train foundation models with billions of parameters across thousands of interconnected chips in data centers. They run AI inference at the edge for real-time applications. And through WebGPU, they bring near-native GPU computing to web browsers.

The AI boom has created unprecedented demand for GPU compute. Training frontier large language models requires clusters of thousands of high-end GPUs running for months. Inference—actually running the trained models—requires its own massive GPU infrastructure, especially as AI agents operate continuously rather than in brief request-response cycles. This demand is driving the largest infrastructure buildout in computing history.

Competition is intensifying. AMD, Intel, Google (with TPU chips), and custom silicon from Amazon and Microsoft are all challenging NVIDIA's dominance. But NVIDIA's ecosystem—CUDA, cuDNN, TensorRT, and deep integration with every major AI framework—creates a software moat that pure hardware competition struggles to overcome.