DuetServe: Harmonizing Prefill and Decode for LLM Serving via Adaptive GPU Multiplexing

• URL: http://arxiv.org/abs/2511.04791v1
• Date: Thu, 06 Nov 2025 20:18:34 GMT
• Title: DuetServe: Harmonizing Prefill and Decode for LLM Serving via Adaptive GPU Multiplexing
• Authors: Lei Gao, Chaoyi Jiang, Hossein Entezari Zarch, Daniel Wong, Murali Annavaram,
• Abstract summary: DuetServe is a unified LLM serving framework that achieves disaggregation-level isolation within a single GPU.<n>DuetServe improves total throughput by up to 1.3x while maintaining low generation latency compared to state-of-the-art frameworks.
• Score: 15.376910065679994
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Modern LLM serving systems must sustain high throughput while meeting strict latency SLOs across two distinct inference phases: compute-intensive prefill and memory-bound decode phases. Existing approaches either (1) aggregate both phases on shared GPUs, leading to interference between prefill and decode phases, which degrades time-between-tokens (TBT); or (2) disaggregate the two phases across GPUs, improving latency but wasting resources through duplicated models and KV cache transfers. We present DuetServe, a unified LLM serving framework that achieves disaggregation-level isolation within a single GPU. DuetServe operates in aggregated mode by default and dynamically activates SM-level GPU spatial multiplexing when TBT degradation is predicted. Its key idea is to decouple prefill and decode execution only when needed through fine-grained, adaptive SM partitioning that provides phase isolation only when contention threatens latency service level objectives (SLOs). DuetServe integrates (1) an attention-aware roofline model to forecast iteration latency, (2) a partitioning optimizer that selects the optimal SM split to maximize throughput under TBT constraints, and (3) an interruption-free execution engine that eliminates CPU-GPU synchronization overhead. Evaluations show that DuetServe improves total throughput by up to 1.3x while maintaining low generation latency compared to state-of-the-art frameworks.

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