Multi-agent workflows have changed the shape of local inference. A lead agent decomposes a task and spawns subagents. What looked like one user request becomes dozens of independent model calls. Pointed at a single local engine, those calls compete for the same execution slots. The queue grows while a workstation, laptop, or DGX Spark on the same network sits idle.
NVIDIA Personal AI Router (PAIR) targets exactly that bottleneck. Announced this week, PAIR is a virtual inference router. It discovers compatible machines on a home network and schedules independent inference requests across them. It is not a new inference engine. Ollama or LM Studio still executes the model on whichever node PAIR selects.
Is it deployable? Yes. PAIR ships today as a public beta (v0.1.1) with signed installers for Windows, macOS, and Linux, and the full source is on GitHub under Apache 2.0. It runs entirely on the local network, with internet needed only to download models.
No new API
The design decision that matters most is that PAIR introduces no cluster API. It proxies the Ollama-compatible and LM Studio-compatible interfaces agents already speak, taking over the default port each engine uses. If a harness listens elsewhere, the proxy port is configurable in PAIR’s engine settings. The repository also exposes OpenAI-compatible proxy endpoints.
