{
  "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": "2610.26131",
  "title": "A Hybrid GPU and Processing-Near-Memory Architecture with Shared DRAM for Efficient Prefill-Decode Disaggregation",
  "summary": "DuoPD replaces GPU-side prefill-decode disaggregation and SM partitioning by executing full decoding on Processing-Near-Memory units in the HBM logic die while prefill runs on the GPU, sharing the HBM interface via TSV-level bandwidth allocation. The cost is entirely hardware-side: it requires custom PNM silicon, a new microarchitecture, and a hierarchical data-mapping scheme, with no software-only path to deployment. It can fail commercially if no memory vendor ships such parts, and technically if bandwidth sharing between GPU and PNM does not hold under real serving skew between prefill and decode load.",
  "rationale": "The technique is a hardware architecture requiring non-existent commodity PNM-equipped HBM, so a reader with one 24 GB GPU or API access cannot adopt or test it and should only track whether vendors productize it.",
  "technique": "PNM-based prefill-decode disaggregation over shared HBM",
  "family": "serving_e_batching",
  "recommendation": "observar",
  "published": "2026-10-19",
  "as_of": "2026-10-20",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.26131",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "DuoPD replaces GPU-side prefill-decode disaggregation and SM partitioning by executing full decoding on Processing-Near-Memory units in the HBM logic die while prefill runs on the GPU, sharing the HBM interface via TSV-level bandwidth allocation. The cost is entirely hardware-side: it requires custom PNM silicon, a new microarchitecture, and a hierarchical data-mapping scheme, with no software-only path to deployment. It can fail commercially if no memory vendor ships such parts, and technically if bandwidth sharing between GPU and PNM does not hold under real serving skew between prefill and decode load.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.26131",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Up to 2.23x higher end-to-end throughput than conventional prefill-decode disaggregation on server-scale LLMs.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.26131",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "finding": "",
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      "next_check": "",
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      "dimension": "compute",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "finding": "",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}