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vLLM upgrades EPD encoder disaggregation for lower latency and higher throughput

2 articles2 sourcessince Nov 10Last article 5h ago ·

Overview

AISummary of 2 articles

vLLM announces on X on November 11, 2026, that its latest EPD encoder disaggregation improvements lower time to first token and raise throughput per GPU, counting encoder GPUs, and that the upgrade is integrated with vLLM Router, SMG, and llm-d.

EPD separates the multimodal encoder into its own instance, and metadata-only requests let downstream instances skip media preprocessing and receive embeddings through connectors.

In a vLLM blog post dated November 10, 2026, vLLM reports a DeepSeek V4.1 Flash test on GB200 in which EPD delivered 6.6 to 9.5% higher throughput per GPU than a PD baseline at concurrency 64 to 128, with P90 TTFT 44 to 47% lower, counting encoder GPUs. The blog says initial support covers images, Model Runner V2, encoder TP=1, and no dynamic LoRA, and that DeepSeek V4.1 Flash metadata-only support is on main through #62802. No independent benchmark has been reported.

Written by AI from the articles below · updated Nov 11, 5:21 AM ET

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Developments

2 developments

  1. Nov 11, 5:03 AM ET · 1 article
    vLLM upgrades encoder disaggregation performance and integrations
    vLLM: vLLM upgrades its EPD encoder disaggregation with lower latency and higher throughput
  2. Nov 10, 7:00 PM ET · 1 article
    vLLM explains encoder disaggregation for multimodal serving
    vLLM Blog: vLLM explains encoder disaggregation for scaling multimodal serving

Article timeline

The articles in this story. Times are ET.

Nov 11
  1. vLLMOfficial
    vLLM upgrades its EPD encoder disaggregation with lower latency and higher throughput

    AIvLLM says its latest EPD encoder disaggregation improvements deliver lower TTFT and higher throughput per GPU, counting all encoder GPUs. The upgrade is integrated with vLLM Router, SMG, and llm-d. The post links to a blog with details.

Nov 10
  1. vLLM BlogOfficialPick
    vLLM explains encoder disaggregation for scaling multimodal serving

    AIvLLM's blog explains how separating the multimodal encoder into its own instance, using EPD disaggregation, changes serving. Metadata-only requests let downstream instances skip media preprocessing and receive embeddings through connectors. In a DeepSeek V4.1 Flash test on GB200, EPD delivered 6.6 to 9.5% higher throughput per GPU than a PD baseline at concurrency 64 to 128, with P90 TTFT 44 to 47% lower, counting encoder GPUs.

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