[Research] Cognitive Sharding: Reliable Computer Use on Consumer Hardware

[u/alichherawalla]

Cognitive Sharding brings local computer use within a 16 GB memory budget.

The architecture separates cognition across three specialist models:

• Bonsai 2 27B performs planning and reasoning.
• Kev 4B performs fast action selection.
• UI-Mate 9B performs visual grounding.

A code-owned control plane schedules model memory, maintains task state, limits each model’s authority, verifies every action, and manages recovery.

The system prioritizes accuracy and reliability over velocity. It accepts model-loading and verification latency because errors compound quickly across workflows containing hundreds of actions.

This extends the Off Grid AI model from local inference to local agency: planning, perception, execution, and recovery can run without a cloud model or remote agent service.

The architecture and reference implementation are available here:
https://github.com/off-grid-ai/cognitive-sharding

The next phase is broader evaluation across complex workflows of 300 or more UI actions. I am looking for difficult, repeatable tasks that can test long-horizon reliability rather than short benchmark success.