SQD splits decode around subquadratic attention stages

SQD disaggregates decode by quadratic and subquadratic attention stages instead of treating decode as one pool or splitting only attention from feed-forward work. On an adjusted eight-B200 proxy, it improves tokens per joule by 31–56% over the strongest GPU-only baselines across GLM-5.2, Nemotron 3 Ultra and Gemma 4 31B. A validated analytical model of Rubin plus LPX projects 1.2–1.5 times tighter achievable latency and up to 3.6 times the throughput of attention-FFN disaggregation. The proxy modifies decode-device characteristics and most future-system results are modeled, not production measurements.

For inference architects planning mixed GPU and decode-accelerator fleets, hybrid and sparse attention change the correct disaggregation boundary. Moving fixed-state attention with the FFN can reduce energy and interconnect pressure. The paper is architecture guidance rather than deployable software, so capacity plans should wait for a real Rubin/LPX implementation and end-to-end validation.