How distributed inference keeps large AI models moving

When an AI model answers a question or completes a task, it performs inference, i.e., using a trained model to generate an output in response to some input. Doing that at scale, however, is more challenging than it sounds. Running a large language model on a GPU is one thing. Serving that model to thousands or even millions of people and AI agents at the same time is quite another.

Requests arrive with different priorities and lengths, which have unique computing needs. Some users expect an immediate response to simple queries, while others submit large jobs that can run in the background. AI agent traffic adds a new pattern: Agents may send bursts of related requests, pause to call a tool, and then return with nearly the same context. Meanwhile, the system must make efficient use of expensive accelerators without creating long queues or repeatedly performing the same computations.

This challenge resembles air traffic control. A busy airport cannot simply send each arriving aircraft…