A cheap model that fails often becomes expensive due to retries, fallbacks, and human review. The true measure of economic efficiency is the cost to reliably complete a task, not the raw inference cost, which can be a misleading metric at scale.
Successfully deploying AI on a device like a phone goes beyond model size. Engineers must account for the entire workload, especially the growing KV cache from long contexts, to maintain application responsiveness and avoid memory overruns.
Instead of selecting one model for all tasks, a more powerful and efficient architecture uses a routing layer. This system delegates simple jobs to small, local models while escalating complex or sensitive requests to more capable ones, optimizing cost and performance.
Don't default to the most powerful AI. A better architectural principle is to give each task the minimum model capability required to solve it reliably. This means knowing when a simple program is better than an SLM, or an SLM is better than a large model.