Beyond Prefill-Decode Disaggregation: Dissecting LLM Inference for Heterogeneous Platforms via Dynamic Operator Scheduling

The problem is that prefill-decode disaggregation and roofline-based operator placement are insufficient for partitioning LLM inference across heterogeneous systems due to workload shape, device contention, and weight layout constraints. DOPS is a hardware-aware, closed-loop framework that uses a stage-aware DAG, a Bifocal scheduler for dynamic operator placement, and a Weight Layout Arbiter (WLA) for selecting efficient weight layouts. On heterogeneous platforms combining NPUs and PIM devices, Bifocal achieves geometric-mean speedups of 1.20× to 2.23× over the PD baseline, with WLA adding 1.28× to 1.33× further speedup. This matters because DOPS enables systematic analysis of workload sensitivity and hardware scalability, improving LLM serving efficiency on diverse heterogeneous hardware.