Actual subnet describes distributed compute clustering model

Team outlined use cases for device clustering on Actual, including combining consumer GPUs for shared inference, enabling mining when idle, and building internal compute pools for startups. Messages emphasize flexibility in aggregating hardware resources across users and devices.

• •Cluster multiple GPUs (3070, M5 Pro, etc.) for faster internal inference
• •Mine during idle periods; rent cluster capacity to other users
• •Startups can pool employee hardware for larger model inference
• •Potential financing mechanism for prosumer/consumer hardware