Multi‑agent workloads turn a single user request into dozens of independent model calls. When all of those calls hit the same local inference engine they compete for a limited number of execution slots, the queue grows, and other machines on the same network sit idle. This wastes compute, raises latency, and forces developers to rewrite their agent harnesses to manage distribution manually.
NVIDIA Personal AI Router (PAIR) solves exactly that bottleneck. PAIR is a virtual inference router that runs on the local network, discovers machines that run Ollama or LM Studio, and schedules each independent request to the node that can run it best. It does not replace the inference engine; it simply proxies the existing Ollama‑compatible or LM Studio‑compatible API that agents already use, so no code changes are required. Installation is a public beta with signed installers for Windows, macOS and Linux; the source is available under Apache 2.0. Pairing uses mDNS for automatic discovery, a six‑digit PIN for trust, and mTLS‑encrypted traffic once paired.
A node is eligible only when its engine is enabled and the exact requested model is present. PAIR’s scheduler checks five signals—online status, engine availability, model presence, current job load, and GPU utilization—and assigns each request to the single most suitable node. The request stays on that node for its whole lifetime; PAIR does not pool VRAM, merge GPUs, or split a request across machines.
Result: higher throughput, lower latency, and better use of existing laptops, workstations, or DGX Sparks without any new cluster API or complex configuration. Agents keep talking to localhost:11434 (or whatever port they already use); PAIR transparently forwards the call to the best‑available machine.
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