Compute as a fabric.

The future of AI inference isn't a data center or a device—it's both. We just proved it with an MVP that orchestrates audio-effects inference across a heterogeneous mix of edge and cloud compute. The same job can execute on a low-power RISC-V single-board computer at the edge, or seamlessly fan out to a local or cloud GPU server when the workload demands serious horsepower. It's a step toward location-agnostic, elastic AI pipelines that treat compute as a fabric instead of a silo.

Edge or cloud, same job

Rudy routes audio-effects inference to the best available compute resource. A low-power RISC-V board at the edge can handle light loads; when the workload grows, the same job fans out to a local or cloud GPU without changing the pipeline.

Elastic AI pipelines

By treating compute as a fabric rather than a fixed location, Rudy opens the door to location-agnostic, elastic AI pipelines that scale with the work instead of the hardware budget.

Project Rudy screenshot

Here's an example display dashboard of how Project Rudy automatically coordinates inference jobs.

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