EAServe：面向多模态大语言模型的编码感知分离式服务

Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2609.31551 (cs) [Submitted on 25 Sep 2026] Title:EAServe: Encode-Aware Disaggregated Serving for Multimodal Large Language Models Authors:Kunxiong Zhu, Zhihao Shu, Hangyu Zheng, Minghai Qin, Miao Yin, Gagan Agrawal, Wei Niu View a PDF of the paper titled EAServe: Encode-Aware Disaggregated Serving for Multimodal Large Language Models, by Kunxiong Zhu and 6 other authors View PDF HTML (experimental) Abstract:Disaggregating the two stages, Prefill and Decode, onto separate GPU pools is now a standard optimization for (text-only) LLM serving. However, multimodal LLMs (MLLMs), which add a third phase, Encode, pose new challenges for resource allocation. Encode turns images, video, or audio into embeddings that the language model can consume, yielding a three-stage Encode-Prefill-Decode (EPD) pipeline. Existing frameworks offer only partial answers: text-only PD systems lack Encode, while EPD frameworks expose it as a separate service without regulating downstream request flow. The pipeline also carries a structural resource imbalance: every request enters through Encode before downstream work can begin, yet per-request execution leaves the encode GPU severely underutilized even at high loads, starving the downstream Prefill and Decode workers. Addressing this, we reposition Encode as the control point of the EPD pipeline, exposing three tightly coupled dimensions: when work enters downstream, where prefill executes, and how the GPU is shared. We instantiate this in EAServe across two co-designed layers. Its runtime manages load-adaptive micro-batching, rate-controlled partial offload to a co-resident prefill worker, and dynamic SM partitioning for predictable co-location. The configuration layer, Hybrid Auto Selection (HAS), navigates the joint space of GPU allocation, encode batch size, and offload ratio by pruning unbalanced allocations with per-stage capacity profiling and refining the remainder through TPE-based Bayesian optimization. Evaluated on three MLLM architectures spanning image, video, and audio, EAServe delivers up to 4.3x and 1.7x higher goodput than NVIDIA Dynamo and vLLM, respectively, under identical SLO constraints, sustains more balanced and higher GPU utilization across the EPD pipeline, and reaches near-optimal configurations faster than baseline search methods. Comments: Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Performance (cs.PF) ACM classes: C.2.4; D.4.1; I.2.11 Cite as: arXiv:2609.31551 [cs.DC] (or arXiv:2609.31551v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2609.31551 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journal reference: International Conference on Parallel Architectures and Compilation Techniques (PACT '26), October 19-22, 2026, Chicago, IL, USA Related DOI: https://doi.org/10.1145/3838684.3846886 Focus to learn more DOI(s) linking to related resources Submission history From: Kunxiong Zhu [view email] [v1] Fri, 25 Sep 2026 17:20:22 UTC (1,391 KB) Full-text links: Access Paper: View a PDF of the paper titled EAServe: Encode-Aware Disaggregated Serving for Multimodal Large Language Models, by Kunxiong Zhu and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.DC new | recent | 2026-09 Change to browse by: cs cs.LG cs.PF References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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