Abstract

The serving economics, platform architecture, and on-device thesis that follow from a native Rust train-and-inference engine whose decode kernel runs at the memory-bandwidth wall. Derives a cost law (tokens/$/s ∝ B / (q·N_active)) showing quantization — not bigger accelerators — is the primary cost lever, and proves paged-attention tenant concurrency and a structural egress bound (raw data never crosses the wire). Honestly marks train/personalize/federate as stub/roadmap.