{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A self-hosted assistant is too expensive because its model uses most of the GPU memory.",
    "change": "Try the paper's supported lower-precision weights on a copy of your serving setup.",
    "baseline": "The same model at its original precision, on the same GPU.",
    "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": "Peak memory, task pass rate, latency, and total serving cost at the same context and load.",
    "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": "2609.26333",
  "title": "Disaggregated Quantization: Specializing LLM Prefill and Decode",
  "summary": "The method replaces single-format weight-only quantization with phase-specialized formats: a low-precision compute-native checkpoint for prefill and a compact weight-only checkpoint for decode, with the prefill weights optionally streamed from SSD to fit one device. It costs an additional trained checkpoint (storage and either training compute or reliance on released prefiller weights), plus SSD streaming bandwidth that is only amortized over long prompts. It can fail when prefill checkpoints are unavailable for the reader's model, when prompts are short enough that SSD weight loading dominates, or when accuracy gains measured on Qwen3/Gemma3 do not transfer to other architectures.",
  "rationale": "The single-GPU, llama.cpp-based ODP setup matches the reader's constraints and offers a real TTFT gain, but it depends on the availability of trained prefill checkpoints for their specific model and on long-prompt workloads, so workload-specific validation is required before adoption.",
  "technique": "Disaggregated prefill/decode quantization (DQ/ODP)",
  "family": "quantizacao",
  "recommendation": "testar",
  "published": "2026-09-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.26333",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 26,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2609.26333",
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    {
      "claim_id": "claim-02",
      "statement": "Offloaded disaggregated prefill delivers a 1.78x time-to-first-token speedup over the weight-only baseline at 8K prompt length on a 27B model in llama.cpp; training an NVFP4 prefiller also raises 1-bit decode accuracy by 32.5 points on MMLU-Pro without modifying the decode checkpoint.",
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    }
  ],
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "finding": "",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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      "source_page": null,
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    }
  ],
  "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": "",
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  "equations_source_sha256": "",
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}