In Depth
GPUs have become the backbone of modern AI because their massively parallel architecture is ideally suited for the matrix operations that dominate deep learning. A single GPU can contain thousands of cores that work simultaneously, performing the billions of multiply-accumulate operations needed for neural network training and inference orders of magnitude faster than CPUs.
NVIDIA dominates the AI GPU market with its data center products, from the A100 to the H100 and B200 generations. Each generation improves not just raw computation but also memory bandwidth, interconnects, and specialized tensor cores optimized for AI workloads. AMD and Intel are competitive alternatives, while cloud providers offer GPU access through services like AWS, Google Cloud, and Azure.
GPU availability has become one of the most significant bottlenecks in AI development. Major model training runs require thousands of GPUs running for months, and demand consistently outstrips supply. This has driven innovation in GPU-efficient techniques (quantization, pruning, Flash Attention) and alternative hardware like TPUs and custom AI accelerators. For businesses, GPU strategy, whether to buy, rent cloud instances, or use inference APIs, is a critical decision in AI deployment.