Research / In research
Intelligence, with a smaller footprint.
Exploring model architectures that bring the right pathways into memory, when they're needed.
Larger models ask more of the hardware that runs them. We're investigating a different starting point: architectures designed around selective activation and efficient movement from storage to memory.
The research explores routing, weight residency, and teacher-guided training. The question is whether a model can use substantially less resident memory while preserving useful quality and speed.
This is active experimental work. Quality, throughput, and the cost of moving data all need to be measured together. It is not yet a drop-in replacement for an existing model.
Follow the work here as it develops.