High-Performance Architecture

Inference succeeds or fails on tail latency. You can have plenty of GPU power and still miss service-level objectives when storage spikes, replicas scale at the same time or shared systems get noisy. This article lays out the storage behavior observed in real inference services, a reference storage stack that maintains stable P95 and P99 latencies under bursty traffic, and recommendations when pushing inference to the edge.

This document provides a comprehensive guide to designing efficient AI inference architectures, focusing on optimizing hardware and system design for real-time model deployment rather than training. * Inference focus over training: Inference drives AI product value and requires less inter-connectivity than training, allowing cost-efficient architectures by removing unnecessary training overhead. * Guidance for cost-effective inference: Selecting suitable GPUs, optimizing models, and designing tailored data center solutions are key to achieving low cost per token and reliable inference performance.