NVIDIA's free PAIR beta sends local inference to whichever machine on your network is free

Much of the appeal of running models locally disappears when four agent subtasks all queue behind the same graphics card. NVIDIA's answer, published at IFA 2026 on September 3, 2026, is a free, open source tool called Personal AI Router, or PAIR. It discovers compatible machines on the local network and routes each independent inference request to a system with capacity, adapting as devices join and leave.

What it does

PAIR gives your apps and agents a single local endpoint and proxies requests behind it. At launch it supports two backends you probably already run, Ollama and LM Studio, and it ships with both a graphical and a terminal interface on Windows, macOS, and Linux. Setup, as NVIDIA describes it, is installing the tool on each machine, pairing them on the same network, and pointing your app at the endpoint.

The privacy argument follows from the design rather than from a policy. NVIDIA says prompts, files, and agent context stay on your local network instead of going to a cloud inference service, and the tool needs no internet connection to operate. You only need one to download models.

• GeForce RTX 20 Series GPUs and newer.
• RTX PRO workstation GPUs, Turing architecture and newer.
• NVIDIA DGX Spark.
• Apple M4 silicon or newer.

What it does not do

This is the part worth reading twice before you plan around it. The machines stay separate systems handling parallel tasks, and PAIR does not combine them into one virtual GPU. A model that does not fit in the memory of a single machine on your network still does not fit; adding a second PC buys you throughput on independent requests, not headroom for a bigger model.

The rest of the fine print is ordinary beta fine print. The tool is in beta, its interface is English only, it asks for 8 GB of system memory or more, and NVIDIA recommends 20 GB of disk space.

What shipped alongside it

The same post carries inference numbers, and they are the vendor's own measurements rather than an independent benchmark. They arrive through the backends most people use locally, so you get them by updating rather than by changing anything.

• llama.cpp: up to 1.9x higher throughput on a GeForce RTX 5090, from kernel optimizations, better speculative decoding, and faster prefill, according to NVIDIA.
• vLLM: 1.2x on an RTX PRO 6000 Blackwell Workstation Edition and up to 1.4x on two clustered DGX Spark systems, according to NVIDIA.
• NVIDIA RTX Spark Windows PCs arrive in October 2026.