Most inference cost is architectural waste. We redesign the architecture so the waste disappears.
One compact shared engine on a single GPU. Serves every domain. Never changes.
Legal, finance, support, compliance - each is a fraction of the base model. Trained in minutes from your data on a single GPU. Your data stays yours.
All adapters in memory simultaneously. A fraction of conventional memory requirements. Switch between them in milliseconds.
100 domain models in 1.3GB. Currently that's 1,400GB - a rack of GPUs. We put it on one card. Validated on 7B parameter models across language, vision, and genomics benchmarks.