{
  "model": "Qwen3.8-Flash-CIRU-STRIX-Orca",
  "base_url": "http://127.0.0.1:8083",
  "stream": true,
  "cases": [
    "mixed-short",
    "mixed-medium",
    "mixed-long",
    "repetitive-short"
  ],
  "repeats": 3,
  "rows": [
    {
      "case": "mixed-short",
      "style": "mixed",
      "words": 256,
      "nonce": 2,
      "wall_s": 6.11,
      "ttft_s": 1.295,
      "finish_reason": "length",
      "response_text": "Based on the provided text, which emphasizes the multifaceted nature of local inference performance, here are 20 distinct, detailed observations regarding the design of a reliable local inference benchmark. These observations address the specific variables mentioned (prompt length, memory traffic, attention state, expert routing, speculative acceptance, and scheduling) while expanding into broader system-level considerations.\n\n1. **Dynamic Prompt Length Stratification**: A reliable benchmark must not rely on a single prompt length. It should include a stratified distribution of prompt lengths (e.g., 1k, 4k, 32k, 128k tokens) to",
      "usage": {
        "completion_tokens": 128,
        "prompt_tokens": 371,
        "total_tokens": 499,
        "prompt_tokens_details": {
          "cached_tokens": 0
        }
      },
      "timings": {
        "cache_n": 0,
        "prompt_n": 371,
        "prompt_ms": 1292.413,
        "prompt_per_token_ms": 3.4835929919137465,
        "prompt_per_second": 287.0599413654923,
        "predicted_n": 128,
        "predicted_ms": 4814.721,
        "predicted_per_token_ms": 37.91118897637795,
        "predicted_per_second": 26.377437031138463,
        "draft_n": 227,
        "draft_n_accepted": 89
      },
      "server_timing": null
    },
    {
      "case": "mixed-short",
      "style": "mixed",
      "words": 256,
      "nonce": 3,
      "wall_s": 6.98,
      "ttft_s": 1.319,
      "finish_reason": "length",
      "response_text": "Based on the provided text, which emphasizes the multifaceted nature of local inference performance, here are 20 distinct, detailed observations regarding the design of a reliable local inference benchmark. These observations address the specific variables mentioned (prompt length, memory traffic, attention state, expert routing, speculative acceptance, and scheduling) while expanding into broader system-level considerations.\n\n1. **Dynamic Prompt Length Stratification**: A reliable benchmark must not rely on a single prompt length. It should implement a stratified sampling approach across short (e.g., <100 tokens), medium (1k\u20134k tokens), and long-context (32",
      "usage": {
        "completion_tokens": 128,
        "prompt_tokens": 371,
        "total_tokens": 499,
        "prompt_tokens_details": {
          "cached_tokens": 0
        }
      },
      "timings": {
        "cache_n": 0,
        "prompt_n": 371,
        "prompt_ms": 1315.019,
        "prompt_per_token_ms": 3.5445256064690027,
        "prompt_per_second": 282.1252012328339,
        "predicted_n": 128,
        "predicted_ms": 5660.584,
        "predicted_per_token_ms": 44.57152755905512,
        "predicted_per_second": 22.435847608656633,
        "draft_n": 248,
        "draft_n_accepted": 84
      },
      "server_timing": null
    },
    {
      "case": "mixed-short",
      "style": "mixed",
      "words": 256,
      "nonce": 4,
      "wall_s": 6.816,
      "ttft_s": 1.331,
      "finish_reason": "length",
      "response_text": "Based on the provided text, which emphasizes the multifaceted nature of local inference performance, here are 20 distinct observations regarding the design of a reliable local inference benchmark. These observations address the specific variables mentioned (prompt length, memory traffic, attention state, expert routing, speculative acceptance, and scheduling) while expanding into broader architectural and operational considerations.\n\n1. **Dynamic Prompt Length Stratification**: A reliable benchmark must not rely on a single prompt length. It should include a stratified distribution of short, medium, and long prompts to capture how performance degrades or scales with context window utilization, as attention mechanisms often exhibit quadratic or linear",
      "usage": {
        "completion_tokens": 128,
        "prompt_tokens": 371,
        "total_tokens": 499,
        "prompt_tokens_details": {
          "cached_tokens": 0
        }
      },
      "timings": {
        "cache_n": 0,
        "prompt_n": 371,
        "prompt_ms": 1322.545,
        "prompt_per_token_ms": 3.564811320754717,
        "prompt_per_second": 280.5197554714584,
        "predicted_n": 128,
        "predicted_ms": 5484.639,
        "predicted_per_token_ms": 43.186133858267716,
        "predicted_per_second": 23.155580522254972,
        "draft_n": 254,
        "draft_n_accepted": 83
      },
      "server_timing": null
    },
    {
      "case": "mixed-medium",
      "style": "mixed",
      "words": 2048,
      "nonce": 6,
      "wall_s": 14.299,
      "ttft_s": 7.369,
      "finish_reason": "length",
      "response_text": "Based on the repetitive stress test provided in \"Trial 000006,\" which highlights the critical factors of prompt length, memory traffic, attention state, expert routing, speculative acceptance, and scheduling, here are 20 distinct observations regarding the design of a reliable local inference benchmark.\n\n1.  **Dynamic Prompt Length Stratification**: Benchmarks must not rely on a single fixed prompt length. Instead, they should employ a stratified sampling approach that tests short (context window < 10%), medium (50%), and long (near-maximum) contexts. This is crucial because, as noted in the trial,",
      "usage": {
        "completion_tokens": 128,
        "prompt_tokens": 2678,
        "total_tokens": 2806,
        "prompt_tokens_details": {
          "cached_tokens": 0
        }
      },
      "timings": {
        "cache_n": 0,
        "prompt_n": 2678,
        "prompt_ms": 7361.714,
        "prompt_per_token_ms": 2.7489596713965647,
        "prompt_per_second": 363.7739798095933,
        "predicted_n": 128,
        "predicted_ms": 6929.9,
        "predicted_per_token_ms": 54.56614173228346,
        "predicted_per_second": 18.326382776086238,
        "draft_n": 248,
        "draft_n_accepted": 84
      },
      "server_timing": null
    },
    {
      "case": "mixed-medium",
      "style": "mixed",
      "words": 2048,
      "nonce": 7,
      "wall_s": 12.081,
      "ttft_s": 7.326,
      "finish_reason": "length",
      "response_text": "Based on the factors identified in the prompt\u2014prompt length, memory traffic, attention state, expert routing, speculative acceptance, and scheduling\u2014here are 20 distinct, detailed observations for designing a reliable local inference benchmark.\n\n1.  **Dynamic Prompt Length Stratification**: Benchmarks must not rely on a single fixed prompt length. Instead, they should employ a stratified sampling approach across short (e.g., <1k tokens), medium (1k\u20138k tokens), and long-context (>32k tokens) inputs. This is critical because prefill latency scales quadratically with sequence length in standard attention mechanisms, while decode",
      "usage": {
        "completion_tokens": 128,
        "prompt_tokens": 2678,
        "total_tokens": 2806,
        "prompt_tokens_details": {
          "cached_tokens": 0
        }
      },
      "timings": {
        "cache_n": 0,
        "prompt_n": 2678,
        "prompt_ms": 7320.24,
        "prompt_per_token_ms": 2.7334727408513815,
        "prompt_per_second": 365.83499994535697,
        "predicted_n": 128,
        "predicted_ms": 4754.211,
        "predicted_per_token_ms": 37.43473228346457,
        "predicted_per_second": 26.713160185780563,
        "draft_n": 228,
        "draft_n_accepted": 88
      },
      "server_timing": null
    },
    {
      "case": "mixed-medium",
      "style": "mixed",
      "words": 2048,
      "nonce": 8,
      "wall_s": 13.478,
      "ttft_s": 7.414,
      "finish_reason": "length",
      "response_text": "Based on the repetitive stress test provided in \"Trial 000008,\" which highlights the critical variables affecting local inference performance, here are 20 distinct, detailed observations regarding the design of a reliable local inference benchmark.\n\n### 1. Standardization of Prompt Length and Complexity\nBenchmarking must strictly control for prompt length (input tokens) because prefill time scales quadratically with sequence length in standard attention mechanisms. A reliable benchmark should define specific tiers (e.g., 1k, 4k, 32k, 128k tokens) and use synthetic or standardized datasets (like LongB",
      "usage": {
        "completion_tokens": 128,
        "prompt_tokens": 2678,
        "total_tokens": 2806,
        "prompt_tokens_details": {
          "cached_tokens": 0
        }
      },
      "timings": {
        "cache_n": 0,
        "prompt_n": 2678,
        "prompt_ms": 7398.342,
        "prompt_per_token_ms": 2.7626370425690814,
        "prompt_per_second": 361.9729934085232,
        "predicted_n": 128,
        "predicted_ms": 6062.458,
        "predicted_per_token_ms": 47.735889763779525,
        "predicted_per_second": 20.948598736684033,
        "draft_n": 281,
        "draft_n_accepted": 78
      },
      "server_timing": null
    },
    {
      "case": "mixed-long",
      "style": "mixed",
      "words": 8192,
      "nonce": 10,
      "wall_s": 35.282,
      "ttft_s": 29.894,
      "finish_reason": "length",
      "response_text": "Based on the provided text, which appears to be a stress test or a demonstration of repetitive data injection (likely simulating a long context window or a specific prompt structure for \"Trial 000010\"), here are 20 distinct, detailed observations regarding the design of a reliable local inference benchmark. These observations address the specific factors mentioned in the text: prompt length, memory traffic, attention state, expert routing, speculative acceptance, and scheduling.\n\n1.  **Context Window Saturation Testing**: The extreme repetition in the prompt suggests a test of context window saturation. A reliable benchmark must include scenarios where the input length approaches",
      "usage": {
        "completion_tokens": 128,
        "prompt_tokens": 10583,
        "total_tokens": 10711,
        "prompt_tokens_details": {
          "cached_tokens": 0
        }
      },
      "timings": {
        "cache_n": 0,
        "prompt_n": 10583,
        "prompt_ms": 29874.11,
        "prompt_per_token_ms": 2.822839459510536,
        "prompt_per_second": 354.2532313096524,
        "predicted_n": 128,
        "predicted_ms": 5386.45,
        "predicted_per_token_ms": 42.41299212598425,
        "predicted_per_second": 23.577681032962342,
        "draft_n": 220,
        "draft_n_accepted": 89
      },
      "server_timing": null
    },
    {
      "case": "mixed-long",
      "style": "mixed",
      "words": 8192,
      "nonce": 11,
      "wall_s": 36.058,
      "ttft_s": 29.925,
      "finish_reason": "length",
      "response_text": "Based on the provided text, which appears to be a stress test or a demonstration of repetitive data injection (likely simulating high-volume logging or a \"needle in a haystack\" retrieval task), here are 20 distinct, detailed observations regarding the design of a reliable local inference benchmark. These observations address the specific variables mentioned in the prompt (prompt length, memory traffic, attention state, expert routing, speculative acceptance, and scheduling) while accounting for the noise introduced by the repetitive text.\n\n1.  **Prompt Length Scaling Linearity**: A reliable benchmark must test prompt lengths across multiple orders of magnitude (e.g., 1k,",
      "usage": {
        "completion_tokens": 128,
        "prompt_tokens": 10583,
        "total_tokens": 10711,
        "prompt_tokens_details": {
          "cached_tokens": 0
        }
      },
      "timings": {
        "cache_n": 0,
        "prompt_n": 10583,
        "prompt_ms": 29909.288,
        "prompt_per_token_ms": 2.8261634697155817,
        "prompt_per_second": 353.8365741103566,
        "predicted_n": 128,
        "predicted_ms": 6134.014,
        "predicted_per_token_ms": 48.29932283464567,
        "predicted_per_second": 20.704224020356,
        "draft_n": 231,
        "draft_n_accepted": 87
      },
      "server_timing": null
    },
    {
      "case": "mixed-long",
      "style": "mixed",
      "words": 8192,
      "nonce": 12,
      "wall_s": 35.172,
      "ttft_s": 29.956,
      "finish_reason": "length",
      "response_text": "Based on the provided text, which appears to be a stress test or a demonstration of repetitive data injection (likely simulating a long context window or a specific prompt structure for \"Trial 000012\"), here are 20 distinct, detailed observations regarding the design of a reliable local inference benchmark. These observations address the specific factors mentioned in the prompt (prompt length, memory traffic, attention state, expert routing, speculative acceptance, and scheduling) while expanding on broader engineering principles.\n\n1.  **Context Window Saturation Testing**: The repetition of the sentence \"Local inference performance depends on...\" suggests a test of context window",
      "usage": {
        "completion_tokens": 128,
        "prompt_tokens": 10583,
        "total_tokens": 10711,
        "prompt_tokens_details": {
          "cached_tokens": 0
        }
      },
      "timings": {
        "cache_n": 0,
        "prompt_n": 10583,
        "prompt_ms": 29888.081,
        "prompt_per_token_ms": 2.824159595577813,
        "prompt_per_second": 354.08763781120643,
        "predicted_n": 128,
        "predicted_ms": 5215.617,
        "predicted_per_token_ms": 41.06785039370079,
        "predicted_per_second": 24.349947475054243,
        "draft_n": 226,
        "draft_n_accepted": 87
      },
      "server_timing": null
    },
    {
      "case": "repetitive-short",
      "style": "repetitive",
      "words": 256,
      "nonce": 14,
      "wall_s": 3.629,
      "ttft_s": 1.354,
      "finish_reason": "stop",
      "response_text": "The quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.",
      "usage": {
        "completion_tokens": 110,
        "prompt_tokens": 369,
        "total_tokens": 479,
        "prompt_tokens_details": {
          "cached_tokens": 0
        }
      },
      "timings": {
        "cache_n": 0,
        "prompt_n": 369,
        "prompt_ms": 1348.763,
        "prompt_per_token_ms": 3.655184281842818,
        "prompt_per_second": 273.58401735516173,
        "predicted_n": 110,
        "predicted_ms": 2274.982,
        "predicted_per_token_ms": 20.871394495412844,
        "predicted_per_second": 47.912466999738896,
        "draft_n": 114,
        "draft_n_accepted": 91
      },
      "server_timing": null
    },
    {
      "case": "repetitive-short",
      "style": "repetitive",
      "words": 256,
      "nonce": 15,
      "wall_s": 3.616,
      "ttft_s": 1.379,
      "finish_reason": "stop",
      "response_text": "The quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.",
      "usage": {
        "completion_tokens": 110,
        "prompt_tokens": 369,
        "total_tokens": 479,
        "prompt_tokens_details": {
          "cached_tokens": 0
        }
      },
      "timings": {
        "cache_n": 0,
        "prompt_n": 369,
        "prompt_ms": 1362.94,
        "prompt_per_token_ms": 3.693604336043361,
        "prompt_per_second": 270.73825700324295,
        "predicted_n": 110,
        "predicted_ms": 2236.493,
        "predicted_per_token_ms": 20.518284403669725,
        "predicted_per_second": 48.73701817980204,
        "draft_n": 114,
        "draft_n_accepted": 91
      },
      "server_timing": null
    },
    {
      "case": "repetitive-short",
      "style": "repetitive",
      "words": 256,
      "nonce": 16,
      "wall_s": 3.61,
      "ttft_s": 1.378,
      "finish_reason": "stop",
      "response_text": "The quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.\nThe quick brown fox jumps over the lazy dog.",
      "usage": {
        "completion_tokens": 110,
        "prompt_tokens": 369,
        "total_tokens": 479,
        "prompt_tokens_details": {
          "cached_tokens": 0
        }
      },
      "timings": {
        "cache_n": 0,
        "prompt_n": 369,
        "prompt_ms": 1363.927,
        "prompt_per_token_ms": 3.696279132791328,
        "prompt_per_second": 270.542338409607,
        "predicted_n": 110,
        "predicted_ms": 2231.967,
        "predicted_per_token_ms": 20.476761467889908,
        "predicted_per_second": 48.83584748340813,
        "draft_n": 114,
        "draft_n_accepted": 91
      },
      "server_timing": null
    }
  ]
}