{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your inference service slows down when several customers send requests together.",
    "change": "Replay a recorded request trace with only the proposed serving policy changed.",
    "baseline": "Your current scheduler on the same model and hardware.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Requests meeting your latency target, throughput, failures, and cost per successful request.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2609.31551",
  "title": "EAServe: Encode-Aware Disaggregated Serving for Multimodal Large Language Models",
  "summary": "EAServe replaces naive disaggregated Encode-Prefill-Decode serving (or monolithic MLLM serving in vLLM/Dynamo) by treating the Encode stage as the control point for request admission, prefill placement, and GPU sharing, with micro-batching, partial prefill offload onto the encode GPU, and dynamic SM partitioning plus a TPE-based configuration search. The cost is substantial engineering complexity: it requires a multi-GPU disaggregated deployment, per-stage capacity profiling, SM-level partitioning support, and a Bayesian auto-selection layer, none of which maps onto a single 24 GB GPU or API-only setup. It can fail when workload modality mix or load shifts invalidate the profiled configuration, when SM co-location introduces interference that violates SLOs, or when the operator lacks the multi-GPU infrastructure the method presupposes.",
  "rationale": "The technique is a multi-GPU serving-systems optimization whose entire premise (disaggregated EPD pools and SM co-location) is inapplicable to a single-GPU or API-based production setup, so the reader should track it only for future scaling.",
  "technique": "Encode-aware disaggregated MLLM serving with SM co-location",
  "family": "serving_e_batching",
  "recommendation": "observar",
  "published": "2026-09-25",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.31551",
  "independent_implementations": 2,
  "total_implementations": 2,
  "stars": 1,
  "citations": 0,
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      "dimension": "operations",
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      "dimension": "compatibility",
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  "report_available": false,
  "validation_tier": "unknown",
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