Openflows

Local Inference as Baseline

Type: circuit

This loop began with a practical audit: what intelligence can run here, on the machines already present?

Processors were examined. RAM measured. Idle systems reconsidered. Tools like LM Studio were installed and exercised. Models were downloaded, loaded, and run. Performance was observed under real conditions: writing, analysis, experimentation.

The result is simple.

Local inference functions as part of the working environment.

Language models can be hosted directly on available hardware. They operate within known constraints. Response times, memory limits, and model sizes are tangible and measurable. The relationship between computation and outcome is visible.

What changed is spatial.

Intelligence now resides in the same physical context as the rest of the system - alongside storage, power, network, and fabrication tools. Models are handled as files. Execution is bounded by local processors. Capacity is something you can inspect.

This configuration supports everyday work: drafting, synthesis, exploration, iteration. It does so within the existing stack, without architectural upheaval.

Local inference is therefore treated as infrastructural baseline.

It is present. It runs. It participates.

The loop is complete.