Term tag: Hardware

AI Data Center

Why AI facilities are measured in megawatts rather than servers — power density, liquid cooling, and the grid connection that has become the real constraint.

CUDA

NVIDIA's software layer for GPUs — the programming model that turned graphics cards into AI hardware, and the real reason rivals with comparable silicon lose.

Distributed Training

How one model is trained across thousands of GPUs — data, tensor and pipeline parallelism, and why the network rather than the chip becomes the bottleneck.

FLOPs (Floating Point Operations)

The unit AI compute is measured in — how a training run's FLOP budget is counted, why FLOP and FLOPS are not the same, and why regulators use the number.

NVIDIA GPU for AI

Why AI training runs on NVIDIA rather than cheaper silicon — tensor cores, NVLink, and the software that decides how much of the datasheet you actually get.