GPUs: The Hardware Behind AI

GPUs: The Specialized Hardware Powering the AI Revolution

Graphics Processing Units (GPUs) were originally engineered to calculate matrix mathematics and rasterize 3D geometry for video games. Today, their massively parallel architecture has made them the indispensable engine powering modern deep learning, scientific simulation, and large language model training.

CPUs vs. GPUs: Latency vs. Throughput

A modern central processing unit (CPU) features a small number of powerful cores (typically 8 to 64) optimized for ultra-low latency, complex branching logic, and single-threaded performance. In contrast, a modern GPU contains thousands of smaller, energy-efficient arithmetic logic units (ALUs) engineered for massive parallel throughput, capable of executing billions of floating-point operations (FLOPs) simultaneously.

Specialized AI Hardware Features

• Tensor Cores: Dedicated hardware units optimized for mixed-precision matrix multiplication (FP16, BF16, FP8, and INT8), performing fused multiply-accumulate operations in a single clock cycle.
• High-Bandwidth Memory (HBM3e): Stacked memory dies connected directly to the GPU silicon via silicon interposers, providing up to 8 Terabytes per second of memory bandwidth to feed hungry attention calculations.
• NVLink Interconnects: Ultra-fast chip-to-chip interconnects enabling thousands of GPUs in a data center cluster to function as a unified virtual accelerator with terabytes of shared addressable memory.