Transformers Drive Rising AI Inference And Serving Costs

This explainer outlines the main drivers of AI cost, focusing on transformer architecture, attention, training, inference, memory bandwidth, infrastructure, and operational expenses. It details how context length, model size, KV caches, alignment, evaluation, and availability requirements raise compute and deployment costs, implying practitioners must optimize architecture, data pipelines, and serving strategies to control expenses.