{
  "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": "2610.13041",
  "title": "RAMP: Recurrent Adaptive Mixed Precision for Efficient Prefill in Recurrent-Depth Language Models",
  "summary": "RAMP replaces uniform-precision recurrent prefill with a schedule that runs early recurrent steps in low-precision weight-and-activation arithmetic and restores full precision for terminal steps, exploiting the finding that early quantization errors are attenuated by later refinement. The cost is implementation complexity in the recurrent inference loop and the need for weight-and-activation quantization kernels, with quality risk if the error-attenuation property does not hold for a given model. It can fail on non-recurrent architectures, on workloads dominated by decode rather than prefill, and beyond the 4K context lengths actually validated.",
  "rationale": "The technique applies only to recurrent-depth language models, requires H100-class quantization kernels, and delivers a modest validated speedup, making it premature for adoption on a 24 GB GPU running conventional architectures.",
  "technique": "Depth-adaptive mixed precision for recurrent prefill",
  "family": "quantizacao",
  "recommendation": "observar",
  "published": "2026-10-09",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.13041",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 122,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "RAMP replaces uniform-precision recurrent prefill with a schedule that runs early recurrent steps in low-precision weight-and-activation arithmetic and restores full precision for terminal steps, exploiting the finding that early quantization errors are attenuated by later refinement. The cost is implementation complexity in the recurrent inference loop and the need for weight-and-activation quantization kernels, with quality risk if the error-attenuation property does not hold for a given model. It can fail on non-recurrent architectures, on workloads dominated by decode rather than prefill, and beyond the 4K context lengths actually validated.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.13041",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Up to 1.16x end-to-end first-prefill speedup (13.9% latency reduction) on an NVIDIA H100 at context lengths up to 4K, with near-full-precision quality.",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2610.13041",
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    }
  ],
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    },
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
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      "source_page": null,
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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": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}