AI Inference Architecture

Overview

AI Inference Architecture encompasses the hardware, software, and system-level design choices specific to deploying large language models for production inference, distinct from training infrastructure. As deployment shifts from capex-intensive training to opex-sensitive inference, purpose-built solutions emerge that optimize for latency, throughput, power efficiency, and cost per token.

Key architectural drivers include the "memory wall" (compute growth outpaces memory bandwidth, limiting token generation), agentic and reasoning models (orders of magnitude more tokens per query), and distributed inference serving. Technologies include in-memory compute (d-Matrix DIMC achieving 150 TB/s), processing-in-memory (SK hynix PIM/PNM), and rack-scale inference reference designs. Inference is projected to represent 80% of AI workloads and is a primary driver of Gigawatt-Scale-Data-Centers, with AI capex trajectories toward $1 trillion by 2030.