GPU is a technology that was originally designed to support accelerated rendering of 3D graphics but which is now used for other computing purposes that require high-speed processing because of its extraordinary amount of computational capability.
GPU technology is ideally suited to AI/ML technologies. At the heart of both 3D graphics and AI/ML technologies is throughput: the ability to do many things at once. Traditional CPUs are optimized for low-latency, sequential task execution with complex branching logic. In contrast, GPUs are optimized for high-throughput, data-parallel workloads, making them ideal for workloads where the same operation is applied repeatedly to different chunks of data.
Computer graphics involves operations like calculating the color and lighting for millions of pixels or vertices. Each of these calculations is largely independent of the others, so they can be distributed across thousands of GPU cores and processed in parallel.
Within AI/ML, training a neural network involves performing large numbers of matrix operations (especially multiplications) across huge datasets. GPUs are optimized for exactly this kind of dense linear algebra.
In financial markets, GPUs accelerate real-time analytics for algorithmic trading, fraud detection, and predictive modeling by swiftly processing tick-level data and market microstructure signals. Similarly, in network monitoring, GPUs empower AI-driven anomaly detection and traffic classification at scale, making it possible to identify microbursts, latency spikes, and protocol violations with nanosecond precision.