Clairvoyance Turns Idle Company GPUs Into a Shared AI Pool

Idle GPUs are becoming a company resource. Stardock’s Clairvoyance is adding Hivemind, a feature that lets teams share unused computing capacity across their machines for local AI inference. The November 5 release positions Clairvoyance 0.90 as a desktop environment where local and cloud models can work together.

The idea targets routine, high-volume business work in procurement, supply chains, legal operations, and finance. Some of those tasks can run on hardware an organization already owns, reducing the need to send every request to a metered cloud service — a useful proposition for companies tired of watching inference bills behave like office plants with unlimited water.

Hivemind lets team members run AI work using idle GPUs across the company’s computers. That matters outside normal working hours, when machines capable of running local models may otherwise sit unused. As Brad Wardell, CEO of Stardock Software, put it: “Most enterprises have capable machines sitting idle every night, and Hivemind lets them pool those machines and put them to work.”

From One Local Machine to a Team

Clairvoyance includes Locama, a local-model manager that handles the installation, execution, and orchestration of local models on Windows, macOS, and Linux. Hivemind extends that model from one computer to a team’s available machines, turning separate local systems into a shared pool for inference.

Stardock’s technical terminology describes Hivemind as shared local inference among peers. Locama serves as the inference host, while Concierge acts as the routing component responsible for choosing a model and machine for a turn. That division gives Clairvoyance a way to coordinate available hardware without treating every computer as an isolated installation.

The announcement does not specify tensor-parallel execution, combined GPU memory, or a method for dividing one oversized model between peers. In plain English, Hivemind is described as sharing inference capacity — not as merging several GPUs into one giant virtual graphics card. The distinction matters when teams expect a model that cannot fit on any single machine to run across the group.

The release also frames Clairvoyance around named Staff. These AI assistants work with a user’s files and produce documents and other artifacts, while documentation describes assigning different providers or local models to individual Staff members. A workspace can therefore mix computing options instead of forcing every task through one model or one provider.

Local Control Still Requires Task Matching

A local model executes on hardware controlled by the user or organization. Connecting a cloud provider’s tool gives access through that provider’s account and authentication; installing Clairvoyance does not automatically include unlimited access to every commercial model it supports.

That distinction keeps the local-versus-cloud choice tied to actual infrastructure and credentials. A company can use local hardware for work that fits its available models, then connect a cloud provider when a Staff member needs another capability. The product’s pitch is not that local models replace every hosted model, but that both can occupy the same workspace.

Model selection still demands care. The documentation notes that tool-use capabilities vary among local models, and a model that can answer questions is not necessarily able to execute the agentic workflow a team needs. Choosing a smaller local model is therefore a task-specific decision, not a simple cost-cutting exercise.

That benchmark included with Locama is intended to show how well particular hardware runs AI. For teams considering Hivemind, the result should help identify which machines can handle local inference and which ones are better left to spreadsheets, meetings, or the ancient corporate tradition of doing nothing after 6 p.m.

Clairvoyance 0.90 is consequently less about announcing another model than about organizing the models and machines a company already has. Hivemind supplies the shared capacity, Locama manages local execution, Concierge handles routing, and Staff connect those choices to files and generated artifacts.

The practical question is whether a company’s idle hardware can support the tasks it wants to run. Hivemind offers a way to use that capacity across peers, but the release leaves the limits of multi-machine execution clear — and leaves model capability, hardware fit, and provider access as decisions teams still have to make.

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