{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Long customer conversations exhaust memory or limit concurrent requests.",
    "change": "Change only the attention cache policy, after checking model and runtime support.",
    "baseline": "The same model with its ordinary attention cache.",
    "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 cache memory, long-context answer quality, first-token latency, and requests completed.",
    "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": "2609.19657",
  "title": "PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving",
  "summary": "The paper replaces ad-hoc assumptions about KV-cache prefix reuse with a reproducible benchmark measuring time-to-first-token, throughput, and cache-hit behavior on a single H100 across vLLM and TensorRT-LLM. Adoption costs only benchmarking time, since prefix caching is already available in vLLM, but acting on the results requires matching the benchmark's workload dimensions (shared-prefix length, concurrency, cache size) to one's own traffic. The findings can fail to transfer because they were measured on an H100 at serving scales beyond a single 24 GB GPU, where memory pressure and cache-granularity regimes differ, and residual benefits depend on the runtime's scheduling layer.",
  "rationale": "Prefix caching is already a configuration flag in the runtimes the reader can deploy, but whether it materially reduces first-token latency on a memory-constrained GPU depends on workload-specific cache pressure, so it should be enabled and measured rather than adopted blindly.",
  "technique": "Benchmarking prefix-cache reuse in LLM serving",
  "family": "cache_kv",
  "recommendation": "testar",
  "published": "2026-09-17",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.19657",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The paper replaces ad-hoc assumptions about KV-cache prefix reuse with a reproducible benchmark measuring time-to-first-token, throughput, and cache-hit behavior on a single H100 across vLLM and TensorRT-LLM. Adoption costs only benchmarking time, since prefix caching is already available in vLLM, but acting on the results requires matching the benchmark's workload dimensions (shared-prefix length, concurrency, cache size) to one's own traffic. The findings can fail to transfer because they were measured on an H100 at serving scales beyond a single 24 GB GPU, where memory pressure and cache-granularity regimes differ, and residual benefits depend on the runtime's scheduling layer.",
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      "result": "",
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      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.19657",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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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": ""
}