AI Inference Infrastructure

Overview

Inference is the act of running live inputs through an already-trained model to produce outputs, an answer, a classification, a generated image. Where training happens once, inference happens endlessly, for every request a deployed application receives. It must absorb traffic that rises and falls through the day while holding response times steady enough for users to rely on.

How it works

An inference service loads model weights into GPU memory and routes each incoming request to an available GPU. When a model is too large for one chip, its weights are split across several. Techniques such as quantization shrink the memory footprint and lift throughput, while batching groups requests together so the hardware stays busy and cost per query falls.

Why it matters

Training and serving pull in different directions, and infrastructure tuned for one rarely suits the other. Inference lives or dies on low latency, high availability, and the ability to soak up sudden bursts without degrading. Getting it wrong shows up immediately as slow, unreliable products; getting it right keeps AI features responsive at any load.