Term tag: Scaling

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.

Cloud Computing

Cloud computing is the on-demand delivery of computing power, database storage, applications, and other IT resources via the internet with pay-as-you-go.

Distributed Computing

Distributed computing splits work across separate machines that talk only by messages. Why partial failure, not speed, is the defining problem.

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.

Mixture-of-Experts (MoE)

The architecture behind "671B total, 37B active": hundreds of expert sub-networks, a router that picks a few per token, and a memory bill that never shrinks.

Model Size

Model size is a model's parameter count. What that one number predicts — memory, cost per token, capability — worked on real models from 110M to trillions.

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.

Scalable AI

Scaling an AI system means three different things — bigger models, faster training, more concurrent users — and each axis hits a different hard limit.

Vision Transformer (ViT)

A Vision Transformer (ViT) cuts an image into fixed patches, embeds each as a token, and runs a transformer encoder — trading CNN inductive bias for scale.