NVIDIA Personal AI Router (PAIR), now available in beta, lets you combine the inference capacity of multiple computers on your local network and automatically distribute AI requests among them. It is primarily designed for local multi-agent AI workloads, where multiple independent model calls can otherwise overwhelm one GPU.
NVIDIA says a breadth-first approach to distributing agentic tasks is becoming increasingly common, with a lead agent dispatching subtasks to sub-agents or multiple agents working together to complete more complex tasks. However, this approach can create a bottleneck on the local GPU when it receives too many requests.
To address this challenge, NVIDIA PAIR maximizes the AI compute available locally by distributing individual inference requests across available systems. It integrates seamlessly with popular local inference services such as Ollama and LM Studio without requiring changes to the underlying architecture or agent harness.
Agents can send a request through the familiar local interface it expects. PAIR receives the request through its proxy, identifies its engine and model requirements, and selects one eligible node. That node executes the request from start to finish and sends the response back through PAIR. The agent continues to see one connection while PAIR handles placement behind it.
To demonstrate PAIR’s capabilities, NVIDIA released a demo combining Hermes Desktop, Ollama, and PAIR showing roughly a 2x reduction in completion time when combining an RTX Spark, a DGX Spark, and an RTX 5090 via PAIR compared with running the workload on a single RTX Spark laptop. In the demo, Hermes breaks the task into five independent specialist analyses, delegates them, reconciles their findings, and synthesizes the final plan—covering what needs to happen tonight, this week, later, or not at all. PAIR handles the distribution of these inference requests across available nodes, while Ollama runs the model on whichever node PAIR selects. NVIDIA notes, however, that the demo should not be taken as a performance guarantee, as results depend on multiple factors including workload parallelism, model, engine settings, hardware, network, and node availability.
NVIDIA PAIR can be used on Windows 11, Linux, and macOS, with support for both x64 and arm64 systems. It can also pair nodes running different operating systems, dispatching a task to a given node only if the required model or engine is known to be compatible with that node. NVIDIA explicitly notes that PAIR does not "merge GPUs or pool VRAM into one larger accelerator". Instead, it distributes individual inference requests across available systems.
Despite these disclaimers, NVIDIA's announcement sparked some confusion on social media, with users interpreting PAIR as a solution for sharing their available compute with third-parties or for running complex models by combining less capable compute.
Reddit user Vegetable-Warthog81 described their positive experience with PAIR using it to distribute inference across three RTX 5090s running Qwen 3.8 27B using Ollama:
PAIR makes distributing jobs across the three machines pretty painless. For long, repetitive “grunt work” where I care more about stability and just keeping all the GPUs busy than squeezing out maximum tokens/sec, it’s been surprisingly nice.
PAIR can be downloaded from GitHub. For a step-by-step introduction to how to use it, see the Getting Started documentation.
If you are looking for a platform that enables GPU compute to be shared across a network of parties, check Petals or Mesh LLM. Mesh LLM also supports splitting models that are too large to fit on a single machine using Skippy.