EAServe: Encode-Aware Disaggregated Serving for Multimodal Large Language Models
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 ca...
| Authors | Kunxiong Zhu, Zhihao Shu, Hangyu Zheng et al. |
|---|---|
| Primary category | cs.DC |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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