Serving LLMs with APIs and Local Runtimes: A Practical Guide

A model checkpoint is not the same as an application. Think of it as an engine that lacks controls, safety features, or any practical way for people or software to use it directly.

LLM serving is what turns that engine into a usable service. It loads the model, handles requests, formats prompts, manages memory, schedules inference, generates tokens, and sends results back through a reliable interface.

When building most applications, the first big decision is whether to use a managed model API or run the model locally or on your own servers. There is no one-size-fits-all answer. The best option depends on things like model quality, traffic patterns, privacy needs, available hardware, costs, and how much control you want.

What LLM Serving Actually Involves

The runtime is the software that executes the model. It loads model weights into system or accelerator memory, tokenizes the input, applies the correct chat template, performs inference, and converts the generated tokens back into text.

The API is the interface that makes the runtime usable by other software. A request usually includes a model name, a prompt or message history, generation settings, and an instruction to stream the response or return it all at once.