{
  "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": "2609.16085",
  "title": "Is INT8 Portable? A Cross-Platform Measurement Study of Quantized Inference on Embedded and Automotive Accelerators",
  "summary": "This is a measurement study rather than a deployable method; it replaces the assumption that a single INT8 QDQ artifact can be carried unchanged across targets with per-target validation against native vendor toolchains. Adoption costs per-hardware re-quantization, per-target regression testing of predictions, and reliance on vendor compilers rather than portable ONNX artifacts. What can fail: INT8 predictions diverge (958-965/1000 agreement) whenever targets use different integer kernels despite preserved top-1 accuracy, external quantization scales can be silently ignored by an NPU (accuracy 0.75 to 0.005 with no error), and latency on edge NPUs can be dominated by device-to-host transfer size rather than compute.",
  "rationale": "The findings matter only if the reader deploys quantized models to embedded or automotive accelerators; on a single 24GB GPU or API-based stack the hardware classes studied are unavailable, so the actionable content is awareness rather than adoption.",
  "technique": "Cross-platform INT8 portability measurement study",
  "family": "quantizacao",
  "recommendation": "observar",
  "published": "2026-09-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.16085",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This is a measurement study rather than a deployable method; it replaces the assumption that a single INT8 QDQ artifact can be carried unchanged across targets with per-target validation against native vendor toolchains. Adoption costs per-hardware re-quantization, per-target regression testing of predictions, and reliance on vendor compilers rather than portable ONNX artifacts. What can fail: INT8 predictions diverge (958-965/1000 agreement) whenever targets use different integer kernels despite preserved top-1 accuracy, external quantization scales can be silently ignored by an NPU (accuracy 0.75 to 0.005 with no error), and latency on edge NPUs can be dominated by device-to-host transfer size rather than compute.",
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    {
      "claim_id": "claim-02",
      "statement": "INT8 speeds inference up to 2.1x on cores with integer dot-product ISAs (ARM dotprod, x86 VNNI) but slows it by 1.7x on cores lacking them, for the identical model and runtime.",
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  ],
  "exposure_map": [
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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",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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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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      "source_page": null,
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    }
  ],
  "risks": [],
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  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "no_equations",
  "core_kind": "",
  "equations_fetched_at": "2026-10-10",
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  "equations_source_sha256": "",
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}