# Article data sheet

Regenerated: 2026-08-17T06:35:14Z

## Quality

| Measure | Qwen3.8 | Qwen3.6 | Gemma 4 |
|---|---:|---:|---:|
| instruction | 43/50 (86.0%; CI 73.8–93.0) | 43/50 (86.0%; CI 73.8–93.0) | 44/50 (88.0%; CI 76.2–94.4) |
| reasoning runs | 117/120 (97.5%; CI 92.9–99.1) | 28/120 (23.3%; CI 16.7–31.7) | 104/120 (86.7%; CI 79.4–91.6) |
| reasoning pass at 3 | 39/40 (97.5%; CI 87.1–99.6) | 20/40 (50.0%; CI 35.2–64.8) | 38/40 (95.0%; CI 83.5–98.6) |
| tools single runs | 90/90 (100.0%; CI 95.9–100.0) | 79/90 (87.8%; CI 79.4–93.0) | 90/90 (100.0%; CI 95.9–100.0) |
| tools multi runs | 28/30 (93.3%; CI 78.7–98.2) | 27/30 (90.0%; CI 74.4–96.5) | 30/30 (100.0%; CI 88.6–100.0) |
| coding pass at 1 | 12/12 (100.0%; CI 75.8–100.0) | 8/12 (66.7%; CI 39.1–86.2) | 6/12 (50.0%; CI 25.4–74.6) |
| coding pass at 3 | 12/12 (100.0%; CI 75.8–100.0) | 8/12 (66.7%; CI 39.1–86.2) | 7/12 (58.3%; CI 32.0–80.7) |
| coding runs | 35/36 (97.2%; CI 85.8–99.5) | 16/36 (44.4%; CI 29.5–60.4) | 16/36 (44.4%; CI 29.5–60.4) |
| document qa | 23/24 (95.8%; CI 79.8–99.3) | 8/24 (33.3%; CI 18.0–53.3) | 22/24 (91.7%; CI 74.2–97.7) |
| writing mechanical | 8/12 (66.7%; CI 39.1–86.2) | 11/12 (91.7%; CI 64.6–98.5) | 9/12 (75.0%; CI 46.8–91.1) |
| multilingual mechanical | 13/18 (72.2%; CI 49.1–87.5) | 13/18 (72.2%; CI 49.1–87.5) | 14/18 (77.8%; CI 54.8–91.0) |
| vision | 18/20 (90.0%; CI 69.9–97.2) | 17/20 (85.0%; CI 64.0–94.8) | 19/20 (95.0%; CI 76.4–99.1) |

## Synthetic speed

| Condition | Qwen3.8 | Qwen3.6 | Gemma 4 |
|---|---:|---:|---:|
| prompt_512 | 3001.35 | 2980.06 | 2974.77 |
| prompt_8192 | 2843.75 | 2840.08 | 2682.45 |
| prompt_32768 | 2554.36 | 2554.77 | 2235.46 |
| decode_256 | 49.09 | 49.04 | 45.00 |

## Context and memory

### Qwen3.8
| Lane | Context | KV | Pass | Peak MiB | Headroom MiB | Input tokens |
|---|---:|---|---|---:|---:|---:|
| equal Q4 | 8192 | f16/f16 | pass | 16626 | 7938 | 6137 |
| equal Q4 | 16384 | f16/f16 | pass | 17146 | 7418 | 14327 |
| equal Q4 | 32768 | f16/f16 | pass | 18186 | 6378 | 30720 |
| equal Q4 | 65536 | f16/f16 | pass | 20266 | 4298 | 63480 |

### Qwen3.6
| Lane | Context | KV | Pass | Peak MiB | Headroom MiB | Input tokens |
|---|---:|---|---|---:|---:|---:|
| equal Q4 | 8192 | f16/f16 | pass | 16626 | 7938 | 6137 |
| equal Q4 | 16384 | f16/f16 | pass | 17146 | 7418 | 14327 |
| equal Q4 | 32768 | f16/f16 | pass | 18186 | 6378 | 30720 |
| equal Q4 | 65536 | f16/f16 | pass | 20266 | 4298 | 63480 |

### Gemma 4
| Lane | Context | KV | Pass | Peak MiB | Headroom MiB | Input tokens |
|---|---:|---|---|---:|---:|---:|
| equal Q4 | 8192 | f16/f16 | pass | 19962 | 4602 | 6134 |
| equal Q4 | 16384 | f16/f16 | pass | 20610 | 3954 | 14330 |
| equal Q4 | 32768 | f16/f16 | pass | 21906 | 2658 | 30710 |
| equal Q4 | 65536 | f16/f16 | FAIL | 17854 | 6710 | — |
| best fit | 65536 | q8_0/q8_0 | pass | 21956 | 2608 | 63482 |

## Streaming and power

| Model | Load/ready s | Warm TTFT s | 64-token E2E s | Bench peak W |
|---|---:|---:|---:|---:|
| Qwen3.8 | 24.052 | 0.1070 | 0.2351 | 450.8 |
| Qwen3.6 | 22.107 | 0.1078 | 0.4618 | 451.0 |
| Gemma 4 | 25.049 | 0.4214 | 1.0904 | 453.9 |

## Coding efficiency

| Model | Successful runs | Median successful model s | Median calls | Invalid calls |
|---|---:|---:|---:|---:|
| Qwen3.8 | 35/36 | 14.278 | 5.0 | 0 |
| Qwen3.6 | 16/36 | 24.175 | 5.0 | 1 |
| Gemma 4 | 16/36 | 44.393 | 10.0 | 10 |

## Reasoning efficiency

| Model | No visible final | Length finishes | Median completion tokens | Median elapsed s |
|---|---:|---:|---:|---:|
| Qwen3.8 | 2/120 | 2 | 190.0 | 4.235 |
| Qwen3.6 | 81/120 | 92 | 768.0 | 16.156 |
| Gemma 4 | 4/120 | 16 | 522.5 | 12.100 |

## Blind writing

| Model | Points | Percent |
|---|---:|---:|
| Qwen3.8 | 93/96 | 96.9% |
| Qwen3.6 | 93/96 | 96.9% |
| Gemma 4 | 94/96 | 97.9% |

## Qwen3.8 versus Qwen3.6 paired outcomes

| Category | Q3.8 only | Q3.6 only | Both pass | Both fail | p |
|---|---:|---:|---:|---:|---:|
| coding | 19 | 0 | 16 | 1 | 0.0000 |
| documents | 15 | 0 | 8 | 1 | 0.0001 |
| instruction | 0 | 0 | 43 | 7 | 1.0000 |
| multilingual | 0 | 0 | 13 | 5 | 1.0000 |
| reasoning | 89 | 0 | 28 | 3 | 0.0000 |
| tools_multi | 3 | 2 | 25 | 0 | 1.0000 |
| tools_single | 11 | 0 | 79 | 0 | 0.0010 |
| vision | 1 | 0 | 17 | 2 | 1.0000 |
| writing | 0 | 3 | 8 | 1 | 0.2500 |

## MTP retained summaries

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              "content": "<think>\nThe user is asking me to continue with a \"detailed synthetic audit narrative\" based on an \"audit ledger\" with values: cobalt=17, maple=29, quartz=41, delta=53. The prompt is repeated many times, which suggests this might be a test of whether I'll just keep generating repetitive content, or it could be a creative writing prompt asking me to build a fictional audit narrative around these four named entities with their assigned numbers.\n\nLet me craft a single, detailed, creative synthetic audit narrative that incorporates all four ledger entries (cobalt=17, maple=29, quartz=41, delta=53) in a coherent, interesting way. I'll treat this as a creative fiction piece - a fictional corporate/government audit report with these four \"accounts\" or \"divisions\" or \"sites\" being audited. I'll make it detailed and narrative in style.\n\nLet me write one complete, well-structured narrative rather than repeating it multiple times.\n</think>\n\n# Synthetic Audit Narrative \u2014 Cycle 7, Quarter 3\n\n**Classification:** Internal / Synthetic\n**Ledger Reference:** cobalt=17, maple=29, quartz=41, delta",
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              "content": "<think>\nThe user is asking me to continue with a \"detailed synthetic audit narrative\" based on an \"audit ledger\" with values: cobalt=17, maple=29, quartz=41, delta=53. The prompt is repeated many times, which suggests this might be a test of whether I'll just keep generating repetitive content, or it could be a creative writing prompt asking me to build a fictional audit narrative around these four named entities with their assigned numbers.\n\nLet me craft a single, detailed, creative synthetic audit narrative that incorporates all four ledger entries (cobalt=17, maple=29, quartz=41, delta=53) in a coherent, interesting way. I'll treat this as a creative fiction piece - a fictional corporate/government audit report with these four \"accounts\" or \"divisions\" or \"sites\" being audited. I'll make it detailed and narrative in style.\n\nLet me write one complete, well-structured narrative rather than repeating it multiple times.\n</think>\n\n# Synthetic Audit Narrative \u2014 Cycle 7, Quarter 3\n\n**Classification:** Internal / Synthetic\n**Ledger Reference:** cobalt=17, maple=29, quartz=41, delta",
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              "content": "<think>\nThe user is asking me to \"continue with a detailed synthetic audit narrative\" based on an \"audit ledger\" with values: cobalt=17, maple=29, quartz=41, delta=53. This prompt is repeated many times, which suggests it might be a test of whether I'll just keep generating repetitive content, or it could be a creative writing prompt asking me to write a fictional/synthetic audit narrative using these as parameters.\n\nLet me write a creative, detailed synthetic audit narrative that incorporates these four elements (cobalt, maple, quartz, delta) with their assigned values. I'll treat this as a creative fiction piece - a fictional audit report for a fictional organization. I'll make it detailed and interesting, treating the numbers as some kind of metric (perhaps transaction counts, risk scores, or compliance indices).\n\nLet me craft a single, well-structured narrative rather than repeating it multiple times.\n</think>\n\n# Synthetic Audit Narrative \u2014 Cycle 7Q\n\n**Classification:** Internal / Synthetic Exercise\n**Ledger Reference:** cobalt=17, maple=29, quartz=41, delta=53\n\n---\n\n## I. Scope & Opening\n\nThe audit team convened at 06",
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              "content": "<think>\nThe user is asking me to \"continue with a detailed synthetic audit narrative\" based on an \"audit ledger\" with values: cobalt=17, maple=29, quartz=41, delta=53. This prompt is repeated many times, which suggests it might be a test of whether I'll just keep generating repetitive content, or it could be a creative writing prompt asking me to write a fictional/synthetic audit narrative using these as parameters.\n\nLet me write a creative, detailed synthetic audit narrative that incorporates these four elements (cobalt, maple, quartz, delta) with their assigned values. I'll treat this as a creative fiction piece - a fictional audit report for a fictional organization. I'll make it detailed and interesting, treating the numbers as some kind of metric (perhaps transaction counts, risk scores, or compliance indices).\n\nLet me craft a single, well-structured narrative rather than repeating it multiple times.\n</think>\n\n# Synthetic Audit Narrative \u2014 Cycle 7Q\n\n**Classification:** Internal / Synthetic Exercise\n**Ledger Reference:** cobalt=17, maple=29, quartz=41, delta=53\n\n---\n\n## I. Scope & Opening\n\nThe audit team convened at 06",
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              "content": "<think>\nThe user is asking me to \"continue with a detailed synthetic audit narrative\" based on an \"audit ledger\" with values: cobalt=17, maple=29, quartz=41, delta=53. This prompt is repeated many times, which suggests it might be a test of whether I'll just keep generating repetitive content, or it could be a creative writing prompt asking me to write a fictional/synthetic audit narrative using these as parameters.\n\nLet me write a creative, detailed synthetic audit narrative that incorporates these four elements (cobalt, maple, quartz, delta) with their assigned values. I'll treat this as a creative fiction piece - a fictional audit report for a fictional organization. I'll make it detailed and interesting, treating the numbers as some kind of metric (perhaps transaction counts, risk scores, or compliance indices).\n\nLet me craft a single, well-structured narrative rather than repeating it multiple times.\n</think>\n\n# Synthetic Audit Narrative \u2014 Cycle 7Q\n\n**Classification:** Internal / Synthetic Exercise\n**Ledger Reference:** cobalt=17, maple=29, quartz=41, delta=53\n\n---\n\n## I. Scope & Opening\n\nThe audit team convened at 06",
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      "mtp_speedup_ratio": 2.0195031899050804,
      "mtp_extra_peak_vram_mib": 956
    },
    "practical": {
      "model_id": "qwen38",
      "draft_model": null,
      "baseline": {
        "label": "baseline",
        "supported": true,
        "runs": [
          {
            "prompt_id": "technical_memo",
            "repetition": 1,
            "elapsed_seconds": 8.1132956109941,
            "completion_tokens": 384,
            "output": "**To:** Engineering Team\n**From:** Infrastructure Lead\n**Date:** October 24, 2023\n**Subject:** Context Window Limitations in Q4 Model Deployment\n\nWe are observing failures when deploying our Q4 quantized model with a 64K context window, despite successful tests at 8K. This discrepancy stems from how memory is allocated during inference, not just the model\u2019s static size.\n\nFirst, the model weights themselves are fixed. In Q4 format, these weights occupy a constant amount of VRAM regardless of context length. However, the KV cache is dynamic. It stores key and value vectors for every token in the prompt. As context grows from 8K to 64K, the KV cache expands linearly. At 8K, this overhead is manageable. At 64K, it becomes the dominant memory consumer, often exceeding the size of the weights themselves.\n\nSecond, runtime allocations introduce variable overhead. The inference engine must allocate buffers for attention computations, intermediate activations, and temporary data structures. These allocations are not uniform; they can spike unpredictably depending on batch size and sequence complexity. If the system does not reserve sufficient contiguous memory blocks, these spikes cause out-of-memory errors.\n\nThird, we must account for the output reserve. The model needs space to generate new tokens. If the prompt consumes nearly all available VRAM, there is no room for the output buffer. This leads to immediate failure or severe degradation, as the system cannot allocate space for the next token.\n\nFinally, measured headroom is critical. We must monitor actual VRAM usage, not just theoretical estimates. A model that fits at 8K may have only 10% headroom. At 64K, that headroom vanishes. We recommend implementing dynamic context truncation or increasing VRAM capacity to ensure stable performance at higher context",
            "output_sha256": "1564c090a788c1a083c1e1fdb73577cfb1fba027557a13dd2c86244f3bd34087",
            "timings": {
              "cache_n": 0,
              "prompt_n": 74,
              "prompt_ms": 116.717,
              "prompt_per_token_ms": 1.5772567567567568,
              "prompt_per_second": 634.0121833151983,
              "predicted_n": 384,
              "predicted_ms": 7876.588,
              "predicted_per_token_ms": 20.565503916449085,
              "predicted_per_second": 48.625115341820596
            }
          },
          {
            "prompt_id": "technical_memo",
            "repetition": 2,
            "elapsed_seconds": 8.109806593507528,
            "completion_tokens": 384,
            "output": "**To:** Engineering Team\n**From:** Infrastructure Lead\n**Date:** October 24, 2023\n**Subject:** Context Window Limitations in Q4 Model Deployment\n\nWe are observing failures when deploying our Q4 quantized model with a 64K context window, despite successful tests at 8K. This discrepancy stems from how memory is allocated during inference, not just the model\u2019s static size.\n\nFirst, the model weights themselves are fixed. In Q4 format, these weights occupy a constant amount of VRAM regardless of context length. However, the KV cache is dynamic. It stores key and value vectors for every token in the prompt. As context grows from 8K to 64K, the KV cache expands linearly. At 8K, this overhead is manageable. At 64K, it becomes the dominant memory consumer, often exceeding the size of the weights themselves.\n\nSecond, runtime allocations introduce variable overhead. The inference engine must allocate buffers for attention computations, intermediate activations, and temporary data structures. These allocations are not uniform; they can spike unpredictably depending on batch size and sequence complexity. If the system does not reserve sufficient contiguous memory blocks, these spikes cause out-of-memory errors.\n\nThird, we must account for the output reserve. The model needs space to generate new tokens. If the prompt consumes nearly all available VRAM, there is no room for the output buffer. This leads to immediate failure or severe degradation, as the system cannot allocate space for the next token.\n\nFinally, measured headroom is critical. We must monitor actual VRAM usage, not just theoretical estimates. A model that fits at 8K may have only 10% headroom. At 64K, that headroom vanishes. We recommend implementing dynamic context truncation or increasing VRAM capacity to ensure stable performance at higher context",
            "output_sha256": "1564c090a788c1a083c1e1fdb73577cfb1fba027557a13dd2c86244f3bd34087",
            "timings": {
              "cache_n": 70,
              "prompt_n": 4,
              "prompt_ms": 98.564,
              "prompt_per_token_ms": 24.641,
              "prompt_per_second": 40.582768556470924,
              "predicted_n": 384,
              "predicted_ms": 7880.825,
              "predicted_per_token_ms": 20.576566579634463,
              "predicted_per_second": 48.59897282327675
            }
          },
          {
            "prompt_id": "technical_memo",
            "repetition": 3,
            "elapsed_seconds": 7.988789699971676,
            "completion_tokens": 384,
            "output": "**To:** Engineering Team\n**From:** Infrastructure Lead\n**Date:** October 24, 2023\n**Subject:** Context Window Limitations in Q4 Model Deployment\n\nWe are observing failures when deploying our Q4 quantized model with a 64K context window, despite successful tests at 8K. This discrepancy stems from how memory is allocated during inference, not just the model\u2019s static size.\n\nFirst, the model weights themselves are fixed. In Q4 format, these weights occupy a constant amount of VRAM regardless of context length. However, the KV cache is dynamic. It stores key and value vectors for every token in the prompt. As context grows from 8K to 64K, the KV cache expands linearly. At 8K, this overhead is manageable. At 64K, it becomes the dominant memory consumer, often exceeding the size of the weights themselves.\n\nSecond, runtime allocations introduce variable overhead. The inference engine must allocate buffers for attention computations, intermediate activations, and temporary data structures. These allocations are not uniform; they can spike unpredictably depending on batch size and sequence complexity. If the system does not reserve sufficient contiguous memory blocks, these spikes cause out-of-memory errors.\n\nThird, we must account for the output reserve. The model needs space to generate new tokens. If the prompt consumes nearly all available VRAM, there is no room for the output buffer. This leads to immediate failure or severe degradation, as the system cannot allocate space for the next token.\n\nFinally, measured headroom is critical. We must monitor actual VRAM usage, not just theoretical estimates. A model that fits at 8K may have only 10% headroom. At 64K, that headroom vanishes. We recommend implementing dynamic context truncation or increasing VRAM capacity to ensure stable performance at higher context",
            "output_sha256": "1564c090a788c1a083c1e1fdb73577cfb1fba027557a13dd2c86244f3bd34087",
            "timings": {
              "cache_n": 70,
              "prompt_n": 4,
              "prompt_ms": 106.03,
              "prompt_per_token_ms": 26.5075,
              "prompt_per_second": 37.7251721210978,
              "predicted_n": 384,
              "predicted_ms": 7880.709,
              "predicted_per_token_ms": 20.5762637075718,
              "predicted_per_second": 48.59968817526444
            }
          },
          {
            "prompt_id": "technical_memo",
            "repetition": 4,
            "elapsed_seconds": 7.988531365990639,
            "completion_tokens": 384,
            "output": "**To:** Engineering Team\n**From:** Infrastructure Lead\n**Date:** October 24, 2023\n**Subject:** Context Window Limitations in Q4 Model Deployment\n\nWe are observing failures when deploying our Q4 quantized model with a 64K context window, despite successful tests at 8K. This discrepancy stems from how memory is allocated during inference, not just the model\u2019s static size.\n\nFirst, the model weights themselves are fixed. In Q4 format, these weights occupy a constant amount of VRAM regardless of context length. However, the KV cache is dynamic. It stores key and value vectors for every token in the prompt. As context grows from 8K to 64K, the KV cache expands linearly. At 8K, this overhead is manageable. At 64K, it becomes the dominant memory consumer, often exceeding the size of the weights themselves.\n\nSecond, runtime allocations introduce variable overhead. The inference engine must allocate buffers for attention computations, intermediate activations, and temporary data structures. These allocations are not uniform; they can spike unpredictably depending on batch size and sequence complexity. If the system does not reserve sufficient contiguous memory blocks, these spikes cause out-of-memory errors.\n\nThird, we must account for the output reserve. The model needs space to generate new tokens. If the prompt consumes nearly all available VRAM, there is no room for the output buffer. This leads to immediate failure or severe degradation, as the system cannot allocate space for the next token.\n\nFinally, measured headroom is critical. We must monitor actual VRAM usage, not just theoretical estimates. A model that fits at 8K may have only 10% headroom. At 64K, that headroom vanishes. We recommend implementing dynamic context truncation or increasing VRAM capacity to ensure stable performance at higher context",
            "output_sha256": "1564c090a788c1a083c1e1fdb73577cfb1fba027557a13dd2c86244f3bd34087",
            "timings": {
              "cache_n": 70,
              "prompt_n": 4,
              "prompt_ms": 109.66,
              "prompt_per_token_ms": 27.415,
              "prompt_per_second": 36.47638154295094,
              "predicted_n": 384,
              "predicted_ms": 7876.634,
              "predicted_per_token_ms": 20.56562402088773,
              "predicted_per_second": 48.624831368323065
            }
          },
          {
            "prompt_id": "technical_memo",
            "repetition": 5,
            "elapsed_seconds": 7.989511042833328,
            "completion_tokens": 384,
            "output": "**To:** Engineering Team\n**From:** Infrastructure Lead\n**Date:** October 24, 2023\n**Subject:** Context Window Limitations in Q4 Model Deployment\n\nWe are observing failures when deploying our Q4 quantized model with a 64K context window, despite successful tests at 8K. This discrepancy stems from how memory is allocated during inference, not just the model\u2019s static size.\n\nFirst, the model weights themselves are fixed. In Q4 format, these weights occupy a constant amount of VRAM regardless of context length. However, the KV cache is dynamic. It stores key and value vectors for every token in the prompt. As context grows from 8K to 64K, the KV cache expands linearly. At 8K, this overhead is manageable. At 64K, it becomes the dominant memory consumer, often exceeding the size of the weights themselves.\n\nSecond, runtime allocations introduce variable overhead. The inference engine must allocate buffers for attention computations, intermediate activations, and temporary data structures. These allocations are not uniform; they can spike unpredictably depending on batch size and sequence complexity. If the system does not reserve sufficient contiguous memory blocks, these spikes cause out-of-memory errors.\n\nThird, we must account for the output reserve. The model needs space to generate new tokens. If the prompt consumes nearly all available VRAM, there is no room for the output buffer. This leads to immediate failure or severe degradation, as the system cannot allocate space for the next token.\n\nFinally, measured headroom is critical. We must monitor actual VRAM usage, not just theoretical estimates. A model that fits at 8K may have only 10% headroom. At 64K, that headroom vanishes. We recommend implementing dynamic context truncation or increasing VRAM capacity to ensure stable performance at higher context",
            "output_sha256": "1564c090a788c1a083c1e1fdb73577cfb1fba027557a13dd2c86244f3bd34087",
            "timings": {
              "cache_n": 70,
              "prompt_n": 4,
              "prompt_ms": 111.572,
              "prompt_per_token_ms": 27.893,
              "prompt_per_second": 35.8512888538343,
              "predicted_n": 384,
              "predicted_ms": 7875.795,
              "predicted_per_token_ms": 20.563433420365534,
              "predicted_per_second": 48.63001131949219
            }
          },
          {
            "prompt_id": "coding_review",
            "repetition": 1,
            "elapsed_seconds": 8.013996943831444,
            "completion_tokens": 384,
            "output": "The current implementation fails because `sorted(items)` only orders the intervals; it does not merge them. For example, `[[1,3], [2,6]]` remains `[[1,3], [2,6]]` instead of the expected `[[1,6]]`. To fix this, we must iterate through the sorted list, comparing each interval\u2019s start time with the previous interval\u2019s end time. If they overlap or touch, we extend the previous interval\u2019s end time to the maximum of both ends. Otherwise, we append the new interval to the result.\n\nHere is the corrected function:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort by start time; create new list to ensure immutability\n    sorted_items = sorted(items, key=lambda x: x[0])\n    merged = [list(sorted_items[0])]\n    \n    for current in sorted_items[1:]:\n        last = merged[-1]\n        if current[0] <= last[1]:\n            last[1] = max(last[1], current[1])\n        else:\n            merged.append(list(current))\n            \n    return merged\n```\n\nThis approach guarantees input immutability by sorting a copy and creating new lists for the merged results, preventing side effects on the caller\u2019s data. It also explicitly handles empty input by returning an empty list immediately, avoiding index errors.\n\nTo validate this logic, consider three specific tests. First, test `merge_intervals([])` to ensure it returns `[]` without raising exceptions. Second, test `merge_intervals([[1,4], [4,5]])` to verify that touching intervals (where the end of one equals the start of the next) are correctly merged into `[[1,5]]`. Third, test `merge_intervals([[1",
            "output_sha256": "a69b3b3ca824d15873e836a10ae80661da4fedbab56fc9f385d6cb8c3286333a",
            "timings": {
              "cache_n": 0,
              "prompt_n": 66,
              "prompt_ms": 134.508,
              "prompt_per_token_ms": 2.0380000000000003,
              "prompt_per_second": 490.6771344455348,
              "predicted_n": 384,
              "predicted_ms": 7877.481,
              "predicted_per_token_ms": 20.56783550913838,
              "predicted_per_second": 48.61960314471086
            }
          },
          {
            "prompt_id": "coding_review",
            "repetition": 2,
            "elapsed_seconds": 8.109016932547092,
            "completion_tokens": 384,
            "output": "The current implementation fails because `sorted(items)` only orders the intervals; it does not merge them. For example, `[[1,3], [2,6]]` remains `[[1,3], [2,6]]` instead of the expected `[[1,6]]`. To fix this, we must iterate through the sorted list, comparing each interval\u2019s start time with the previous interval\u2019s end time. If they overlap or touch, we extend the previous interval\u2019s end time to the maximum of both ends. Otherwise, we append the new interval to the result.\n\nHere is the corrected function:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort by start time; create new list to ensure immutability\n    sorted_items = sorted(items, key=lambda x: x[0])\n    merged = [list(sorted_items[0])]\n    \n    for current in sorted_items[1:]:\n        last = merged[-1]\n        if current[0] <= last[1]:\n            last[1] = max(last[1], current[1])\n        else:\n            merged.append(list(current))\n            \n    return merged\n```\n\nThis approach guarantees input immutability by sorting a copy and creating new lists for the merged results, preventing side effects on the caller\u2019s data. It also explicitly handles empty input by returning an empty list immediately, avoiding index errors.\n\nTo validate this logic, consider three specific tests. First, test `merge_intervals([])` to ensure it returns `[]` without raising exceptions. Second, test `merge_intervals([[1,4], [4,5]])` to verify that touching intervals (where the end of one equals the start of the next) are correctly merged into `[[1,5]]`. Third, test `merge_intervals([[1",
            "output_sha256": "a69b3b3ca824d15873e836a10ae80661da4fedbab56fc9f385d6cb8c3286333a",
            "timings": {
              "cache_n": 62,
              "prompt_n": 4,
              "prompt_ms": 98.695,
              "prompt_per_token_ms": 24.67375,
              "prompt_per_second": 40.52890217336238,
              "predicted_n": 384,
              "predicted_ms": 7880.264,
              "predicted_per_token_ms": 20.575101827676242,
              "predicted_per_second": 48.60243260885675
            }
          },
          {
            "prompt_id": "coding_review",
            "repetition": 3,
            "elapsed_seconds": 7.990844272077084,
            "completion_tokens": 384,
            "output": "The current implementation fails because `sorted(items)` only orders the intervals; it does not merge them. For example, `[[1,3], [2,6]]` remains `[[1,3], [2,6]]` instead of the expected `[[1,6]]`. To fix this, we must iterate through the sorted list, comparing each interval\u2019s start time with the previous interval\u2019s end time. If they overlap or touch, we extend the previous interval\u2019s end time to the maximum of both ends. Otherwise, we append the new interval to the result.\n\nHere is the corrected function:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort by start time; create new list to ensure immutability\n    sorted_items = sorted(items, key=lambda x: x[0])\n    merged = [list(sorted_items[0])]\n    \n    for current in sorted_items[1:]:\n        last = merged[-1]\n        if current[0] <= last[1]:\n            last[1] = max(last[1], current[1])\n        else:\n            merged.append(list(current))\n            \n    return merged\n```\n\nThis approach guarantees input immutability by sorting a copy and creating new lists for the merged results, preventing side effects on the caller\u2019s data. It also explicitly handles empty input by returning an empty list immediately, avoiding index errors.\n\nTo validate this logic, consider three specific tests. First, test `merge_intervals([])` to ensure it returns `[]` without raising exceptions. Second, test `merge_intervals([[1,4], [4,5]])` to verify that touching intervals (where the end of one equals the start of the next) are correctly merged into `[[1,5]]`. Third, test `merge_intervals([[1",
            "output_sha256": "a69b3b3ca824d15873e836a10ae80661da4fedbab56fc9f385d6cb8c3286333a",
            "timings": {
              "cache_n": 62,
              "prompt_n": 4,
              "prompt_ms": 108.185,
              "prompt_per_token_ms": 27.04625,
              "prompt_per_second": 36.9737024541295,
              "predicted_n": 384,
              "predicted_ms": 7880.268,
              "predicted_per_token_ms": 20.57511227154047,
              "predicted_per_second": 48.60240793841021
            }
          },
          {
            "prompt_id": "coding_review",
            "repetition": 4,
            "elapsed_seconds": 7.988594025373459,
            "completion_tokens": 384,
            "output": "The current implementation fails because `sorted(items)` only orders the intervals; it does not merge them. For example, `[[1,3], [2,6]]` remains `[[1,3], [2,6]]` instead of the expected `[[1,6]]`. To fix this, we must iterate through the sorted list, comparing each interval\u2019s start time with the previous interval\u2019s end time. If they overlap or touch, we extend the previous interval\u2019s end time to the maximum of both ends. Otherwise, we append the new interval to the result.\n\nHere is the corrected function:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort by start time; create new list to ensure immutability\n    sorted_items = sorted(items, key=lambda x: x[0])\n    merged = [list(sorted_items[0])]\n    \n    for current in sorted_items[1:]:\n        last = merged[-1]\n        if current[0] <= last[1]:\n            last[1] = max(last[1], current[1])\n        else:\n            merged.append(list(current))\n            \n    return merged\n```\n\nThis approach guarantees input immutability by sorting a copy and creating new lists for the merged results, preventing side effects on the caller\u2019s data. It also explicitly handles empty input by returning an empty list immediately, avoiding index errors.\n\nTo validate this logic, consider three specific tests. First, test `merge_intervals([])` to ensure it returns `[]` without raising exceptions. Second, test `merge_intervals([[1,4], [4,5]])` to verify that touching intervals (where the end of one equals the start of the next) are correctly merged into `[[1,5]]`. Third, test `merge_intervals([[1",
            "output_sha256": "a69b3b3ca824d15873e836a10ae80661da4fedbab56fc9f385d6cb8c3286333a",
            "timings": {
              "cache_n": 62,
              "prompt_n": 4,
              "prompt_ms": 110.293,
              "prompt_per_token_ms": 27.57325,
              "prompt_per_second": 36.267034172612945,
              "predicted_n": 384,
              "predicted_ms": 7876.101,
              "predicted_per_token_ms": 20.56423237597911,
              "predicted_per_second": 48.6281219603456
            }
          },
          {
            "prompt_id": "coding_review",
            "repetition": 5,
            "elapsed_seconds": 7.997595839202404,
            "completion_tokens": 384,
            "output": "The current implementation fails because `sorted(items)` only orders the intervals; it does not merge them. For example, `[[1,3], [2,6]]` remains `[[1,3], [2,6]]` instead of the expected `[[1,6]]`. To fix this, we must iterate through the sorted list, comparing each interval\u2019s start time with the previous interval\u2019s end time. If they overlap or touch, we extend the previous interval\u2019s end time to the maximum of both ends. Otherwise, we append the new interval to the result.\n\nHere is the corrected function:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort by start time; create new list to ensure immutability\n    sorted_items = sorted(items, key=lambda x: x[0])\n    merged = [list(sorted_items[0])]\n    \n    for current in sorted_items[1:]:\n        last = merged[-1]\n        if current[0] <= last[1]:\n            last[1] = max(last[1], current[1])\n        else:\n            merged.append(list(current))\n            \n    return merged\n```\n\nThis approach guarantees input immutability by sorting a copy and creating new lists for the merged results, preventing side effects on the caller\u2019s data. It also explicitly handles empty input by returning an empty list immediately, avoiding index errors.\n\nTo validate this logic, consider three specific tests. First, test `merge_intervals([])` to ensure it returns `[]` without raising exceptions. Second, test `merge_intervals([[1,4], [4,5]])` to verify that touching intervals (where the end of one equals the start of the next) are correctly merged into `[[1,5]]`. Third, test `merge_intervals([[1",
            "output_sha256": "a69b3b3ca824d15873e836a10ae80661da4fedbab56fc9f385d6cb8c3286333a",
            "timings": {
              "cache_n": 62,
              "prompt_n": 4,
              "prompt_ms": 112.617,
              "prompt_per_token_ms": 28.15425,
              "prompt_per_second": 35.518616194713054,
              "predicted_n": 384,
              "predicted_ms": 7882.946,
              "predicted_per_token_ms": 20.582104438642297,
              "predicted_per_second": 48.58589669395173
            }
          },
          {
            "prompt_id": "incident_runbook",
            "repetition": 1,
            "elapsed_seconds": 6.204885635524988,
            "completion_tokens": 296,
            "output": "1. Reproduce the failure by replaying the exact failing prompt against the local server to confirm the malformed tool call output.\n2. Freeze the specific input payload and expected output into a static fixture to ensure consistent testing conditions.\n3. Inspect the chat template configuration to verify that the system prompt and tool definitions are correctly formatted for the model.\n4. Audit the JSON parser logic to determine if it is incorrectly handling whitespace, escaping, or incomplete streams from the model.\n5. Check the runtime revision of the inference engine to identify any recent changes that might affect token generation or decoding.\n6. Verify the model hash against the original checkpoint to rule out file corruption or unintended weight modifications.\n7. Review the server logs for any warnings or errors related to token limits, context overflow, or decoding anomalies.\n8. Isolate the issue by testing the same model with a minimal, known-good prompt to distinguish between model and pipeline faults.\n9. Roll back the inference engine or chat template to the last known stable version if the fault persists after initial checks.\n10. Re-run the frozen fixture against the rolled-back environment to confirm that the previous version resolves the malformed output.\n11. Implement a strict validation layer in the parser to reject and log any non-compliant tool call structures before execution.\n12. Verify the fix by running a comprehensive suite of edge-case prompts to ensure robustness across various tool invocation scenarios.",
            "output_sha256": "b751904fd63a413fb93dc4fa030304deca925375fda1ad49d86ef4c61b5e4421",
            "timings": {
              "cache_n": 0,
              "prompt_n": 66,
              "prompt_ms": 134.419,
              "prompt_per_token_ms": 2.036651515151515,
              "prompt_per_second": 491.00201608403574,
              "predicted_n": 296,
              "predicted_ms": 6068.587,
              "predicted_per_token_ms": 20.571481355932203,
              "predicted_per_second": 48.61098637953118
            }
          },
          {
            "prompt_id": "incident_runbook",
            "repetition": 2,
            "elapsed_seconds": 6.301020756363869,
            "completion_tokens": 296,
            "output": "1. Reproduce the failure by replaying the exact failing prompt against the local server to confirm the malformed tool call output.\n2. Freeze the specific input payload and expected output into a static fixture to ensure consistent testing conditions.\n3. Inspect the chat template configuration to verify that the system prompt and tool definitions are correctly formatted for the model.\n4. Audit the JSON parser logic to determine if it is incorrectly handling whitespace, escaping, or incomplete streams from the model.\n5. Check the runtime revision of the inference engine to identify any recent changes that might affect token generation or decoding.\n6. Verify the model hash against the original checkpoint to rule out file corruption or unintended weight modifications.\n7. Review the server logs for any warnings or errors related to token limits, context overflow, or decoding anomalies.\n8. Isolate the issue by testing the same model with a minimal, known-good prompt to distinguish between model and pipeline faults.\n9. Roll back the inference engine or chat template to the last known stable version if the fault persists after initial checks.\n10. Re-run the frozen fixture against the rolled-back environment to confirm that the previous version resolves the malformed output.\n11. Implement a strict validation layer in the parser to reject and log any non-compliant tool call structures before execution.\n12. Verify the fix by running a comprehensive suite of edge-case prompts to ensure robustness across various tool invocation scenarios.",
            "output_sha256": "b751904fd63a413fb93dc4fa030304deca925375fda1ad49d86ef4c61b5e4421",
            "timings": {
              "cache_n": 62,
              "prompt_n": 4,
              "prompt_ms": 99.312,
              "prompt_per_token_ms": 24.828,
              "prompt_per_second": 40.27710649266957,
              "predicted_n": 296,
              "predicted_ms": 6070.088,
              "predicted_per_token_ms": 20.576569491525422,
              "predicted_per_second": 48.59896594579848
            }
          },
          {
            "prompt_id": "incident_runbook",
            "repetition": 3,
            "elapsed_seconds": 6.177913278341293,
            "completion_tokens": 296,
            "output": "1. Reproduce the failure by replaying the exact failing prompt against the local server to confirm the malformed tool call output.\n2. Freeze the specific input payload and expected output into a static fixture to ensure consistent testing conditions.\n3. Inspect the chat template configuration to verify that the system prompt and tool definitions are correctly formatted for the model.\n4. Audit the JSON parser logic to determine if it is incorrectly handling whitespace, escaping, or incomplete streams from the model.\n5. Check the runtime revision of the inference engine to identify any recent changes that might affect token generation or decoding.\n6. Verify the model hash against the original checkpoint to rule out file corruption or unintended weight modifications.\n7. Review the server logs for any warnings or errors related to token limits, context overflow, or decoding anomalies.\n8. Isolate the issue by testing the same model with a minimal, known-good prompt to distinguish between model and pipeline faults.\n9. Roll back the inference engine or chat template to the last known stable version if the fault persists after initial checks.\n10. Re-run the frozen fixture against the rolled-back environment to confirm that the previous version resolves the malformed output.\n11. Implement a strict validation layer in the parser to reject and log any non-compliant tool call structures before execution.\n12. Verify the fix by running a comprehensive suite of edge-case prompts to ensure robustness across various tool invocation scenarios.",
            "output_sha256": "b751904fd63a413fb93dc4fa030304deca925375fda1ad49d86ef4c61b5e4421",
            "timings": {
              "cache_n": 62,
              "prompt_n": 4,
              "prompt_ms": 107.971,
              "prompt_per_token_ms": 26.99275,
              "prompt_per_second": 37.04698483852145,
              "predicted_n": 296,
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          {
            "prompt_id": "incident_runbook",
            "repetition": 4,
            "elapsed_seconds": 6.180674310773611,
            "completion_tokens": 296,
            "output": "1. Reproduce the failure by replaying the exact failing prompt against the local server to confirm the malformed tool call output.\n2. Freeze the specific input payload and expected output into a static fixture to ensure consistent testing conditions.\n3. Inspect the chat template configuration to verify that the system prompt and tool definitions are correctly formatted for the model.\n4. Audit the JSON parser logic to determine if it is incorrectly handling whitespace, escaping, or incomplete streams from the model.\n5. Check the runtime revision of the inference engine to identify any recent changes that might affect token generation or decoding.\n6. Verify the model hash against the original checkpoint to rule out file corruption or unintended weight modifications.\n7. Review the server logs for any warnings or errors related to token limits, context overflow, or decoding anomalies.\n8. Isolate the issue by testing the same model with a minimal, known-good prompt to distinguish between model and pipeline faults.\n9. Roll back the inference engine or chat template to the last known stable version if the fault persists after initial checks.\n10. Re-run the frozen fixture against the rolled-back environment to confirm that the previous version resolves the malformed output.\n11. Implement a strict validation layer in the parser to reject and log any non-compliant tool call structures before execution.\n12. Verify the fix by running a comprehensive suite of edge-case prompts to ensure robustness across various tool invocation scenarios.",
            "output_sha256": "b751904fd63a413fb93dc4fa030304deca925375fda1ad49d86ef4c61b5e4421",
            "timings": {
              "cache_n": 62,
              "prompt_n": 4,
              "prompt_ms": 111.424,
              "prompt_per_token_ms": 27.856,
              "prompt_per_second": 35.89890867317634,
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              "predicted_ms": 6067.053,
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              "predicted_per_second": 48.62327723196089
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          {
            "prompt_id": "incident_runbook",
            "repetition": 5,
            "elapsed_seconds": 6.18062462285161,
            "completion_tokens": 296,
            "output": "1. Reproduce the failure by replaying the exact failing prompt against the local server to confirm the malformed tool call output.\n2. Freeze the specific input payload and expected output into a static fixture to ensure consistent testing conditions.\n3. Inspect the chat template configuration to verify that the system prompt and tool definitions are correctly formatted for the model.\n4. Audit the JSON parser logic to determine if it is incorrectly handling whitespace, escaping, or incomplete streams from the model.\n5. Check the runtime revision of the inference engine to identify any recent changes that might affect token generation or decoding.\n6. Verify the model hash against the original checkpoint to rule out file corruption or unintended weight modifications.\n7. Review the server logs for any warnings or errors related to token limits, context overflow, or decoding anomalies.\n8. Isolate the issue by testing the same model with a minimal, known-good prompt to distinguish between model and pipeline faults.\n9. Roll back the inference engine or chat template to the last known stable version if the fault persists after initial checks.\n10. Re-run the frozen fixture against the rolled-back environment to confirm that the previous version resolves the malformed output.\n11. Implement a strict validation layer in the parser to reject and log any non-compliant tool call structures before execution.\n12. Verify the fix by running a comprehensive suite of edge-case prompts to ensure robustness across various tool invocation scenarios.",
            "output_sha256": "b751904fd63a413fb93dc4fa030304deca925375fda1ad49d86ef4c61b5e4421",
            "timings": {
              "cache_n": 62,
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              "prompt_ms": 110.154,
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        "ready_seconds": 24.03945952653885,
        "summary": {
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          "by_prompt": {
            "technical_memo": {
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              "median_decode_tokens_per_second": 48.624831368323065,
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              "median_completion_tokens": 384
            },
            "incident_runbook": {
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              "median_decode_tokens_per_second": 48.61133082334082,
              "median_end_to_end_seconds": 6.180674310773611,
              "median_completion_tokens": 296
            }
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          "draft_tokens": 0,
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          "acceptance_rate": null
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        "peak_gpu_mib": 16626
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      "mtp": {
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        "runs": [
          {
            "prompt_id": "technical_memo",
            "repetition": 1,
            "elapsed_seconds": 5.050113685429096,
            "completion_tokens": 384,
            "output": "**To:** Engineering Team\n**From:** Infrastructure Lead\n**Subject:** Q4 Model Context Limitations: 8K vs. 64K\n\nWe are observing failures when running our Q4 quantized model at 64K context, despite successful loads at 8K. This discrepancy stems from the distinction between static model weights and dynamic runtime memory requirements.\n\nFirst, the model weights themselves are static. A Q4 quantization reduces the size of these parameters significantly, allowing the base model to fit within our GPU\u2019s VRAM. However, loading the weights is only the first step. The primary variable in long-context inference is the Key-Value (KV) cache. As the context window expands from 8K to 64K, the KV cache grows linearly. This cache stores attention states for every token processed. At 64K, this allocation can consume a substantial portion of available VRAM, far exceeding the footprint of the weights alone.\n\nSecond, we must account for runtime allocations. The inference engine requires temporary buffers for matrix multiplications and intermediate activations. These buffers scale with batch size and sequence length. At 8K, these temporary spikes are manageable. At 64K, the peak memory usage during forward passes can spike dramatically, potentially exceeding the remaining free VRAM even if the average usage looks acceptable.\n\nFinally, we must reserve space for output generation. The system needs headroom to allocate memory for the next token\u2019s logits and sampling operations. If the KV cache and runtime buffers consume nearly all available VRAM, there is no room for this output reserve. Consequently, the runtime may crash or fail to allocate memory for the next step, even if the initial load succeeded.\n\nIn summary, 8K success does not guarantee 64K viability. We must measure actual peak memory usage, including KV cache growth and runtime spikes, to",
            "output_sha256": "13719ec30447399062c2bebff03f42c4cf8550c8e78c51b57c59aff7ed3fc67d",
            "timings": {
              "cache_n": 0,
              "prompt_n": 74,
              "prompt_ms": 136.359,
              "prompt_per_token_ms": 1.8426891891891892,
              "prompt_per_second": 542.6851179606773,
              "predicted_n": 384,
              "predicted_ms": 4796.642,
              "predicted_per_token_ms": 12.523869451697127,
              "predicted_per_second": 79.84752666552977,
              "draft_n": 469,
              "draft_n_accepted": 226
            }
          },
          {
            "prompt_id": "technical_memo",
            "repetition": 2,
            "elapsed_seconds": 4.980126958340406,
            "completion_tokens": 384,
            "output": "**To:** Engineering Team\n**From:** Infrastructure Lead\n**Subject:** Q4 Model Context Limitations: 8K vs. 64K\n\nWe are observing failures when running our Q4 quantized model at 64K context, despite successful loads at 8K. This discrepancy stems from the distinction between static model weights and dynamic runtime memory requirements.\n\nFirst, the model weights themselves are static. A Q4 quantization reduces the size of these parameters significantly, allowing the base model to fit within our GPU\u2019s VRAM. However, loading the weights is only the first step. The primary variable in long-context inference is the Key-Value (KV) cache. As the context window expands from 8K to 64K, the KV cache grows linearly. This cache stores attention states for every token processed. At 64K, this allocation can consume a substantial portion of available VRAM, far exceeding the footprint of the weights alone.\n\nSecond, we must account for runtime allocations. The inference engine requires temporary buffers for matrix multiplications and intermediate activations. These buffers scale with batch size and sequence length. At 8K, these temporary spikes are manageable. At 64K, the peak memory usage during forward passes can spike dramatically, potentially exceeding the remaining free VRAM even if the average usage looks acceptable.\n\nFinally, we must reserve space for output generation. The system needs headroom to allocate memory for the next token\u2019s logits and sampling operations. If the KV cache and runtime buffers consume nearly all available VRAM, there is no room for this output reserve. Consequently, the runtime may crash or fail to allocate memory for the next step, even if the initial load succeeded.\n\nIn summary, 8K success does not guarantee 64K viability. We must measure actual peak memory usage, including KV cache growth and runtime spikes, to",
            "output_sha256": "13719ec30447399062c2bebff03f42c4cf8550c8e78c51b57c59aff7ed3fc67d",
            "timings": {
              "cache_n": 70,
              "prompt_n": 4,
              "prompt_ms": 100.574,
              "prompt_per_token_ms": 25.1435,
              "prompt_per_second": 39.771710382405,
              "predicted_n": 384,
              "predicted_ms": 4751.793,
              "predicted_per_token_ms": 12.406770234986944,
              "predicted_per_second": 80.60115413276631,
              "draft_n": 466,
              "draft_n_accepted": 227
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          },
          {
            "prompt_id": "technical_memo",
            "repetition": 3,
            "elapsed_seconds": 4.865616552531719,
            "completion_tokens": 384,
            "output": "**To:** Engineering Team\n**From:** Infrastructure Lead\n**Subject:** Q4 Model Context Limitations: 8K vs. 64K\n\nWe are observing failures when running our Q4 quantized model at 64K context, despite successful loads at 8K. This discrepancy stems from the distinction between static model weights and dynamic runtime memory requirements.\n\nFirst, the model weights themselves are static. A Q4 quantization reduces the size of these parameters significantly, allowing the base model to fit within our GPU\u2019s VRAM. However, loading the weights is only the first step. The primary variable in long-context inference is the Key-Value (KV) cache. As the context window expands from 8K to 64K, the KV cache grows linearly. This cache stores attention states for every token processed. At 64K, this allocation can consume a substantial portion of available VRAM, far exceeding the footprint of the weights alone.\n\nSecond, we must account for runtime allocations. The inference engine requires temporary buffers for matrix multiplications and intermediate activations. These buffers scale with batch size and sequence length. At 8K, these temporary spikes are manageable. At 64K, the peak memory usage during forward passes can spike dramatically, potentially exceeding the remaining free VRAM even if the average usage looks acceptable.\n\nFinally, we must reserve space for output generation. The system needs headroom to allocate memory for the next token\u2019s logits and sampling operations. If the KV cache and runtime buffers consume nearly all available VRAM, there is no room for this output reserve. Consequently, the runtime may crash or fail to allocate memory for the next step, even if the initial load succeeded.\n\nIn summary, 8K success does not guarantee 64K viability. We must measure actual peak memory usage, including KV cache growth and runtime spikes, to",
            "output_sha256": "13719ec30447399062c2bebff03f42c4cf8550c8e78c51b57c59aff7ed3fc67d",
            "timings": {
              "cache_n": 70,
              "prompt_n": 4,
              "prompt_ms": 106.763,
              "prompt_per_token_ms": 26.69075,
              "prompt_per_second": 37.466163371205376,
              "predicted_n": 384,
              "predicted_ms": 4756.954,
              "predicted_per_token_ms": 12.420245430809398,
              "predicted_per_second": 80.51370688049538,
              "draft_n": 466,
              "draft_n_accepted": 227
            }
          },
          {
            "prompt_id": "technical_memo",
            "repetition": 4,
            "elapsed_seconds": 4.867223717272282,
            "completion_tokens": 384,
            "output": "**To:** Engineering Team\n**From:** Infrastructure Lead\n**Subject:** Q4 Model Context Limitations: 8K vs. 64K\n\nWe are observing failures when running our Q4 quantized model at 64K context, despite successful loads at 8K. This discrepancy stems from the distinction between static model weights and dynamic runtime memory requirements.\n\nFirst, the model weights themselves are static. A Q4 quantization reduces the size of these parameters significantly, allowing the base model to fit within our GPU\u2019s VRAM. However, loading the weights is only the first step. The primary variable in long-context inference is the Key-Value (KV) cache. As the context window expands from 8K to 64K, the KV cache grows linearly. This cache stores attention states for every token processed. At 64K, this allocation can consume a substantial portion of available VRAM, far exceeding the footprint of the weights alone.\n\nSecond, we must account for runtime allocations. The inference engine requires temporary buffers for matrix multiplications and intermediate activations. These buffers scale with batch size and sequence length. At 8K, these temporary spikes are manageable. At 64K, the peak memory usage during forward passes can spike dramatically, potentially exceeding the remaining free VRAM even if the average usage looks acceptable.\n\nFinally, we must reserve space for output generation. The system needs headroom to allocate memory for the next token\u2019s logits and sampling operations. If the KV cache and runtime buffers consume nearly all available VRAM, there is no room for this output reserve. Consequently, the runtime may crash or fail to allocate memory for the next step, even if the initial load succeeded.\n\nIn summary, 8K success does not guarantee 64K viability. We must measure actual peak memory usage, including KV cache growth and runtime spikes, to",
            "output_sha256": "13719ec30447399062c2bebff03f42c4cf8550c8e78c51b57c59aff7ed3fc67d",
            "timings": {
              "cache_n": 70,
              "prompt_n": 4,
              "prompt_ms": 108.899,
              "prompt_per_token_ms": 27.22475,
              "prompt_per_second": 36.731283115547434,
              "predicted_n": 384,
              "predicted_ms": 4756.403,
              "predicted_per_token_ms": 12.41880678851175,
              "predicted_per_second": 80.52303389767435,
              "draft_n": 466,
              "draft_n_accepted": 227
            }
          },
          {
            "prompt_id": "technical_memo",
            "repetition": 5,
            "elapsed_seconds": 4.869043473154306,
            "completion_tokens": 384,
            "output": "**To:** Engineering Team\n**From:** Infrastructure Lead\n**Subject:** Q4 Model Context Limitations: 8K vs. 64K\n\nWe are observing failures when running our Q4 quantized model at 64K context, despite successful loads at 8K. This discrepancy stems from the distinction between static model weights and dynamic runtime memory requirements.\n\nFirst, the model weights themselves are static. A Q4 quantization reduces the size of these parameters significantly, allowing the base model to fit within our GPU\u2019s VRAM. However, loading the weights is only the first step. The primary variable in long-context inference is the Key-Value (KV) cache. As the context window expands from 8K to 64K, the KV cache grows linearly. This cache stores attention states for every token processed. At 64K, this allocation can consume a substantial portion of available VRAM, far exceeding the footprint of the weights alone.\n\nSecond, we must account for runtime allocations. The inference engine requires temporary buffers for matrix multiplications and intermediate activations. These buffers scale with batch size and sequence length. At 8K, these temporary spikes are manageable. At 64K, the peak memory usage during forward passes can spike dramatically, potentially exceeding the remaining free VRAM even if the average usage looks acceptable.\n\nFinally, we must reserve space for output generation. The system needs headroom to allocate memory for the next token\u2019s logits and sampling operations. If the KV cache and runtime buffers consume nearly all available VRAM, there is no room for this output reserve. Consequently, the runtime may crash or fail to allocate memory for the next step, even if the initial load succeeded.\n\nIn summary, 8K success does not guarantee 64K viability. We must measure actual peak memory usage, including KV cache growth and runtime spikes, to",
            "output_sha256": "13719ec30447399062c2bebff03f42c4cf8550c8e78c51b57c59aff7ed3fc67d",
            "timings": {
              "cache_n": 70,
              "prompt_n": 4,
              "prompt_ms": 109.923,
              "prompt_per_token_ms": 27.48075,
              "prompt_per_second": 36.38910873975419,
              "predicted_n": 384,
              "predicted_ms": 4757.187,
              "predicted_per_token_ms": 12.420853785900784,
              "predicted_per_second": 80.50976343793087,
              "draft_n": 466,
              "draft_n_accepted": 227
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          },
          {
            "prompt_id": "coding_review",
            "repetition": 1,
            "elapsed_seconds": 3.9140447601675987,
            "completion_tokens": 384,
            "output": "The current implementation fails to merge overlapping or touching intervals, returning only a sorted list of the original items. This violates the core requirement, as distinct intervals like `[1, 3]` and `[4, 5]` should merge into `[1, 5]`, while `[1, 2]` and `[3, 4]` should remain separate if they do not touch. The defect stems from missing the merging logic after sorting.\n\nHere is the corrected function:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort by start time\n    sorted_items = sorted(items, key=lambda x: x[0])\n    merged = [sorted_items[0]]\n    \n    for current in sorted_items[1:]:\n        last = merged[-1]\n        # Check for overlap or touching\n        if current[0] <= last[1]:\n            # Merge by extending the end if necessary\n            merged[-1] = [last[0], max(last[1], current[1])]\n        else:\n            merged.append(current)\n            \n    return merged\n```\n\nThis version ensures input immutability by creating a new list for `sorted_items` and `merged`, leaving the original `items` list unchanged. It also explicitly handles empty input by returning an empty list immediately, preventing index errors.\n\nTo validate this fix, consider these three tests:\n1. `test_empty_input`: Verify that `merge_intervals([])` returns `[]`.\n2. `test_overlapping_intervals`: Input `[[1, 3], [2, 6], [8, 10]]` should return `[[1, 6], [8, 10]]`.\n3. `test_touching_intervals`: Input `[[1, 4], [",
            "output_sha256": "8cb277b10263a5db33430f4c17f715405ff8d9888fcfc005e5b2663d6c1f7eb3",
            "timings": {
              "cache_n": 0,
              "prompt_n": 66,
              "prompt_ms": 147.247,
              "prompt_per_token_ms": 2.2310151515151517,
              "prompt_per_second": 448.2264494353025,
              "predicted_n": 384,
              "predicted_ms": 3764.773,
              "predicted_per_token_ms": 9.82969451697128,
              "predicted_per_second": 101.73256129918059,
              "draft_n": 369,
              "draft_n_accepted": 260
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          {
            "prompt_id": "coding_review",
            "repetition": 2,
            "elapsed_seconds": 3.99339222535491,
            "completion_tokens": 384,
            "output": "The current implementation fails to merge overlapping or touching intervals, returning only a sorted list of the original items. This violates the core requirement, as distinct intervals like `[1, 3]` and `[4, 5]` should merge into `[1, 5]`, while `[1, 2]` and `[3, 4]` should remain separate if they do not touch. The defect stems from missing the merging logic after sorting.\n\nHere is the corrected function:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort by start time\n    sorted_items = sorted(items, key=lambda x: x[0])\n    merged = [sorted_items[0]]\n    \n    for current in sorted_items[1:]:\n        last = merged[-1]\n        # Check for overlap or touching\n        if current[0] <= last[1]:\n            # Merge by extending the end if necessary\n            merged[-1] = [last[0], max(last[1], current[1])]\n        else:\n            merged.append(current)\n            \n    return merged\n```\n\nThis version ensures input immutability by creating a new list for `sorted_items` and `merged`, leaving the original `items` list unchanged. It also explicitly handles empty input by returning an empty list immediately, preventing index errors.\n\nTo validate this fix, consider these three tests:\n1. `test_empty_input`: Verify that `merge_intervals([])` returns `[]`.\n2. `test_overlapping_intervals`: Input `[[1, 3], [2, 6], [8, 10]]` should return `[[1, 6], [8, 10]]`.\n3. `test_touching_intervals`: Input `[[1, 4], [",
            "output_sha256": "8cb277b10263a5db33430f4c17f715405ff8d9888fcfc005e5b2663d6c1f7eb3",
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            "prompt_id": "coding_review",
            "repetition": 3,
            "elapsed_seconds": 3.8756188564002514,
            "completion_tokens": 384,
            "output": "The current implementation fails to merge overlapping or touching intervals, returning only a sorted list of the original items. This violates the core requirement, as distinct intervals like `[1, 3]` and `[4, 5]` should merge into `[1, 5]`, while `[1, 2]` and `[3, 4]` should remain separate if they do not touch. The defect stems from missing the merging logic after sorting.\n\nHere is the corrected function:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort by start time\n    sorted_items = sorted(items, key=lambda x: x[0])\n    merged = [sorted_items[0]]\n    \n    for current in sorted_items[1:]:\n        last = merged[-1]\n        # Check for overlap or touching\n        if current[0] <= last[1]:\n            # Merge by extending the end if necessary\n            merged[-1] = [last[0], max(last[1], current[1])]\n        else:\n            merged.append(current)\n            \n    return merged\n```\n\nThis version ensures input immutability by creating a new list for `sorted_items` and `merged`, leaving the original `items` list unchanged. It also explicitly handles empty input by returning an empty list immediately, preventing index errors.\n\nTo validate this fix, consider these three tests:\n1. `test_empty_input`: Verify that `merge_intervals([])` returns `[]`.\n2. `test_overlapping_intervals`: Input `[[1, 3], [2, 6], [8, 10]]` should return `[[1, 6], [8, 10]]`.\n3. `test_touching_intervals`: Input `[[1, 4], [",
            "output_sha256": "8cb277b10263a5db33430f4c17f715405ff8d9888fcfc005e5b2663d6c1f7eb3",
            "timings": {
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              "prompt_ms": 105.926,
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              "predicted_per_token_ms": 9.837605744125327,
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          {
            "prompt_id": "coding_review",
            "repetition": 4,
            "elapsed_seconds": 3.8799964413046837,
            "completion_tokens": 384,
            "output": "The current implementation fails to merge overlapping or touching intervals, returning only a sorted list of the original items. This violates the core requirement, as distinct intervals like `[1, 3]` and `[4, 5]` should merge into `[1, 5]`, while `[1, 2]` and `[3, 4]` should remain separate if they do not touch. The defect stems from missing the merging logic after sorting.\n\nHere is the corrected function:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort by start time\n    sorted_items = sorted(items, key=lambda x: x[0])\n    merged = [sorted_items[0]]\n    \n    for current in sorted_items[1:]:\n        last = merged[-1]\n        # Check for overlap or touching\n        if current[0] <= last[1]:\n            # Merge by extending the end if necessary\n            merged[-1] = [last[0], max(last[1], current[1])]\n        else:\n            merged.append(current)\n            \n    return merged\n```\n\nThis version ensures input immutability by creating a new list for `sorted_items` and `merged`, leaving the original `items` list unchanged. It also explicitly handles empty input by returning an empty list immediately, preventing index errors.\n\nTo validate this fix, consider these three tests:\n1. `test_empty_input`: Verify that `merge_intervals([])` returns `[]`.\n2. `test_overlapping_intervals`: Input `[[1, 3], [2, 6], [8, 10]]` should return `[[1, 6], [8, 10]]`.\n3. `test_touching_intervals`: Input `[[1, 4], [",
            "output_sha256": "8cb277b10263a5db33430f4c17f715405ff8d9888fcfc005e5b2663d6c1f7eb3",
            "timings": {
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              "predicted_per_token_ms": 9.849885117493471,
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            "output": "The current implementation fails to merge overlapping or touching intervals, returning only a sorted list of the original items. This violates the core requirement, as distinct intervals like `[1, 3]` and `[4, 5]` should merge into `[1, 5]`, while `[1, 2]` and `[3, 4]` should remain separate if they do not touch. The defect stems from missing the merging logic after sorting.\n\nHere is the corrected function:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort by start time\n    sorted_items = sorted(items, key=lambda x: x[0])\n    merged = [sorted_items[0]]\n    \n    for current in sorted_items[1:]:\n        last = merged[-1]\n        # Check for overlap or touching\n        if current[0] <= last[1]:\n            # Merge by extending the end if necessary\n            merged[-1] = [last[0], max(last[1], current[1])]\n        else:\n            merged.append(current)\n            \n    return merged\n```\n\nThis version ensures input immutability by creating a new list for `sorted_items` and `merged`, leaving the original `items` list unchanged. It also explicitly handles empty input by returning an empty list immediately, preventing index errors.\n\nTo validate this fix, consider these three tests:\n1. `test_empty_input`: Verify that `merge_intervals([])` returns `[]`.\n2. `test_overlapping_intervals`: Input `[[1, 3], [2, 6], [8, 10]]` should return `[[1, 6], [8, 10]]`.\n3. `test_touching_intervals`: Input `[[1, 4], [",
            "output_sha256": "8cb277b10263a5db33430f4c17f715405ff8d9888fcfc005e5b2663d6c1f7eb3",
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          {
            "prompt_id": "incident_runbook",
            "repetition": 1,
            "elapsed_seconds": 3.8414646834135056,
            "completion_tokens": 299,
            "output": "1. Reproduce the failure by replaying the exact failing prompt against the local server to confirm the malformed tool call output.\n2. Freeze the specific input payload and expected output into a version-controlled fixture to establish a stable baseline for testing.\n3. Inspect the chat template configuration to ensure the system prompt and user message formatting strictly adhere to the model's expected structure.\n4. Validate the JSON parser logic to confirm it correctly handles edge cases such as trailing commas or unescaped characters in the tool call arguments.\n5. Check the runtime revision of the inference engine to verify that the deployed version matches the tested and supported release.\n6. Verify the model hash against the original download source to rule out file corruption or silent updates to the weights.\n7. Analyze the server logs for any hidden warnings, token limit breaches, or decoding errors that precede the malformed output.\n8. Roll back the server to the last known good configuration and model version to immediately restore service stability.\n9. Re-run the frozen fixture against the rolled-back environment to confirm that the original issue is resolved.\n10. Execute a broader regression test suite to ensure that the rollback has not introduced new failures in other tool call scenarios.\n11. Document the root cause and the specific configuration or code change that triggered the malformed output for future reference.\n12. Implement automated monitoring alerts that detect malformed JSON in tool calls to provide immediate notification of similar future incidents.",
            "output_sha256": "89b55104e7656774e06c95d02740b00976b4748ba7c34816d20d0d8449f0f471",
            "timings": {
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              "draft_n_accepted": 178
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          {
            "prompt_id": "incident_runbook",
            "repetition": 2,
            "elapsed_seconds": 3.933672208338976,
            "completion_tokens": 299,
            "output": "1. Reproduce the failure by replaying the exact failing prompt against the local server to confirm the malformed tool call output.\n2. Freeze the specific input payload and expected output into a version-controlled fixture to establish a stable baseline for testing.\n3. Inspect the chat template configuration to ensure the system prompt and user message formatting strictly adhere to the model's expected structure.\n4. Validate the JSON parser logic to confirm it correctly handles edge cases such as trailing commas or unescaped characters in the tool call arguments.\n5. Check the runtime revision of the inference engine to verify that the deployed version matches the tested and supported release.\n6. Verify the model hash against the original download source to rule out file corruption or silent updates to the weights.\n7. Analyze the server logs for any hidden warnings, token limit breaches, or decoding errors that precede the malformed output.\n8. Roll back the server to the last known good configuration and model version to immediately restore service stability.\n9. Re-run the frozen fixture against the rolled-back environment to confirm that the original issue is resolved.\n10. Execute a broader regression test suite to ensure that the rollback has not introduced new failures in other tool call scenarios.\n11. Document the root cause and the specific configuration or code change that triggered the malformed output for future reference.\n12. Implement automated monitoring alerts that detect malformed JSON in tool calls to provide immediate notification of similar future incidents.",
            "output_sha256": "89b55104e7656774e06c95d02740b00976b4748ba7c34816d20d0d8449f0f471",
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          {
            "prompt_id": "incident_runbook",
            "repetition": 3,
            "elapsed_seconds": 3.8183337971568108,
            "completion_tokens": 299,
            "output": "1. Reproduce the failure by replaying the exact failing prompt against the local server to confirm the malformed tool call output.\n2. Freeze the specific input payload and expected output into a version-controlled fixture to establish a stable baseline for testing.\n3. Inspect the chat template configuration to ensure the system prompt and user message formatting strictly adhere to the model's expected structure.\n4. Validate the JSON parser logic to confirm it correctly handles edge cases such as trailing commas or unescaped characters in the tool call arguments.\n5. Check the runtime revision of the inference engine to verify that the deployed version matches the tested and supported release.\n6. Verify the model hash against the original download source to rule out file corruption or silent updates to the weights.\n7. Analyze the server logs for any hidden warnings, token limit breaches, or decoding errors that precede the malformed output.\n8. Roll back the server to the last known good configuration and model version to immediately restore service stability.\n9. Re-run the frozen fixture against the rolled-back environment to confirm that the original issue is resolved.\n10. Execute a broader regression test suite to ensure that the rollback has not introduced new failures in other tool call scenarios.\n11. Document the root cause and the specific configuration or code change that triggered the malformed output for future reference.\n12. Implement automated monitoring alerts that detect malformed JSON in tool calls to provide immediate notification of similar future incidents.",
            "output_sha256": "89b55104e7656774e06c95d02740b00976b4748ba7c34816d20d0d8449f0f471",
            "timings": {
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            "prompt_id": "incident_runbook",
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            "output": "1. Reproduce the failure by replaying the exact failing prompt against the local server to confirm the malformed tool call output.\n2. Freeze the specific input payload and expected output into a version-controlled fixture to establish a stable baseline for testing.\n3. Inspect the chat template configuration to ensure the system prompt and user message formatting strictly adhere to the model's expected structure.\n4. Validate the JSON parser logic to confirm it correctly handles edge cases such as trailing commas or unescaped characters in the tool call arguments.\n5. Check the runtime revision of the inference engine to verify that the deployed version matches the tested and supported release.\n6. Verify the model hash against the original download source to rule out file corruption or silent updates to the weights.\n7. Analyze the server logs for any hidden warnings, token limit breaches, or decoding errors that precede the malformed output.\n8. Roll back the server to the last known good configuration and model version to immediately restore service stability.\n9. Re-run the frozen fixture against the rolled-back environment to confirm that the original issue is resolved.\n10. Execute a broader regression test suite to ensure that the rollback has not introduced new failures in other tool call scenarios.\n11. Document the root cause and the specific configuration or code change that triggered the malformed output for future reference.\n12. Implement automated monitoring alerts that detect malformed JSON in tool calls to provide immediate notification of similar future incidents.",
            "output_sha256": "89b55104e7656774e06c95d02740b00976b4748ba7c34816d20d0d8449f0f471",
            "timings": {
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            "prompt_id": "incident_runbook",
            "repetition": 5,
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            "output": "1. Reproduce the failure by replaying the exact failing prompt against the local server to confirm the malformed tool call output.\n2. Freeze the specific input payload and expected output into a version-controlled fixture to establish a stable baseline for testing.\n3. Inspect the chat template configuration to ensure the system prompt and user message formatting strictly adhere to the model's expected structure.\n4. Validate the JSON parser logic to confirm it correctly handles edge cases such as trailing commas or unescaped characters in the tool call arguments.\n5. Check the runtime revision of the inference engine to verify that the deployed version matches the tested and supported release.\n6. Verify the model hash against the original download source to rule out file corruption or silent updates to the weights.\n7. Analyze the server logs for any hidden warnings, token limit breaches, or decoding errors that precede the malformed output.\n8. Roll back the server to the last known good configuration and model version to immediately restore service stability.\n9. Re-run the frozen fixture against the rolled-back environment to confirm that the original issue is resolved.\n10. Execute a broader regression test suite to ensure that the rollback has not introduced new failures in other tool call scenarios.\n11. Document the root cause and the specific configuration or code change that triggered the malformed output for future reference.\n12. Implement automated monitoring alerts that detect malformed JSON in tool calls to provide immediate notification of similar future incidents.",
            "output_sha256": "89b55104e7656774e06c95d02740b00976b4748ba7c34816d20d0d8449f0f471",
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  "qwen36": {
    "microbenchmark": {
      "model_id": "qwen36",
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      "baseline": {
        "label": "baseline",
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        "runs": [
          {
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            "elapsed_seconds": 5.563714239746332,
            "timings": {
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              "prompt_ms": 307.049,
              "prompt_per_token_ms": 0.5321473136915078,
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            "response": {
              "index": 0,
              "content": " Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative.\n\n<think>\nHere's a thinking process:\n\n1.  **Analyze User Input:**\n   - The user repeatedly provides the same prompt: \"Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative.\"\n   - The repetition is extreme (about 20 times).\n   - The core request is: Given an audit ledger with four items/values (cobalt=17, maple=29, quartz=41, delta=53), write a detailed synthetic audit narrative.\n   - \"Synthetic\" implies fictional, constructed, or simulated data/context.\n   - \"Audit narrative\" implies a professional, structured report describing findings, methodology, observations, and conclusions related to the ledger entries.\n\n2.  **Identify Key Constraints & Requirements:**\n   - Must incorporate the exact ledger values: cobalt=17, maple=29, quartz=41, delta=53.\n   - Must be a \"detailed synthetic audit narrative.\"\n",
              "tokens": [],
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                "min_p": 0.05000000074505806,
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                "typical_p": 1.0,
                "repeat_last_n": 64,
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                "dry_base": 1.75,
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                "dry_penalty_last_n": 64,
                "dry_sequence_breakers": [
                  "\n",
                  ":",
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                    "token": 248044
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                    "token": 248046
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                    "token": 248063
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                  },
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                    "token": 248065
                  }
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                "grammar": "",
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                "preserved_tokens": [],
                "chat_format": "Content-only",
                "reasoning_format": "deepseek",
                "reasoning_in_content": false,
                "generation_prompt": "",
                "samplers": [
                  "penalties",
                  "dry",
                  "top_n_sigma",
                  "top_k",
                  "typ_p",
                  "top_p",
                  "min_p",
                  "xtc",
                  "temperature"
                ],
                "speculative.types": "none",
                "timings_per_token": false,
                "post_sampling_probs": false,
                "backend_sampling": false,
                "lora": []
              },
              "prompt": "Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative. ",
              "has_new_line": true,
              "truncated": false,
              "stop_type": "limit",
              "stopping_word": "",
              "tokens_cached": 832,
              "timings": {
                "cache_n": 0,
                "prompt_n": 577,
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              "predicted_per_token_ms": 20.604831372549018,
              "predicted_per_second": 48.5323069099347
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              "content": " Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative.\n\n<think>\nHere's a thinking process:\n\n1.  **Analyze User Input:**\n   - The user repeatedly provides the same prompt: \"Audit ledger: cobalt=17, maple=29, quartz=41, delta=53. Continue with a detailed synthetic audit narrative.\"\n   - The repetition is extreme (about 20 times).\n   - The core request is: Given an audit ledger with four items/values (cobalt=17, maple=29, quartz=41, delta=53), write a detailed synthetic audit narrative.\n   - \"Synthetic\" implies fictional, constructed, or simulated data/context.\n   - \"Audit narrative\" implies a professional, structured report describing findings, methodology, observations, and conclusions related to the ledger entries.\n\n2.  **Identify Key Constraints & Requirements:**\n   - Must incorporate the exact ledger values: cobalt=17, maple=29, quartz=41, delta=53.\n   - Must be a \"detailed synthetic audit narrative.\"\n",
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              "predicted_per_token_ms": 22.550231316725977,
              "predicted_per_second": 44.34544311118791
            }
          },
          {
            "prompt_id": "technical_memo",
            "repetition": 5,
            "elapsed_seconds": 6.429755549877882,
            "completion_tokens": 282,
            "output": "**MEMORANDUM**\n\n**TO:** Engineering Team  \n**FROM:** Systems Architecture  \n**SUBJECT:** Memory Constraints in Q4 Model Scaling (8K to 64K Context)\n\nThis memo clarifies why a Q4 quantized model that loads successfully at an 8K context window may trigger Out-of-Memory (OOM) errors when scaled to 64K.\n\nModel weights are static; once the Q4 weights are loaded into VRAM, they occupy a fixed footprint regardless of sequence length. However, the KV (Key-Value) cache is dynamic. The KV cache stores the attention states for every token in the prompt and generated output. Because the cache grows linearly with sequence length, a 64K context requires eight times more memory for these tensors than an 8K context.\n\nAt runtime, the system must allocate contiguous blocks of memory for this expanded cache. Additionally, the engine requires an output reserve\u2014a buffer of VRAM to hold the activations for the tokens being generated. \n\nIf the measured headroom (the remaining VRAM after loading weights) is sufficient for 8K but insufficient for the 64K KV cache and output reserve, the system will crash. In short, loading the model is only the baseline; the operational memory overhead of long-context attention is the primary driver of failure at 64K.",
            "output_sha256": "e13c0d609cf6e9e8cc44b71dee0af953381afc992a1ab5ad4da937756e13b900",
            "timings": {
              "cache_n": 69,
              "prompt_n": 5,
              "prompt_ms": 91.117,
              "prompt_per_token_ms": 18.2234,
              "prompt_per_second": 54.87450201389422,
              "predicted_n": 282,
              "predicted_ms": 6336.639,
              "predicted_per_token_ms": 22.550316725978647,
              "predicted_per_second": 44.34527515296359
            }
          },
          {
            "prompt_id": "coding_review",
            "repetition": 1,
            "elapsed_seconds": 8.19119881466031,
            "completion_tokens": 359,
            "output": "The current implementation is defective because `sorted(items)` only orders the intervals; it does not merge them. To correctly merge overlapping or touching intervals (e.g., `[1, 5]` and `[5, 10]`), you must iterate through the sorted list and combine intervals where the current start is less than or equal to the previous end.\n\nTo ensure input immutability, we avoid modifying the original list by sorting a copy. We also handle empty inputs by returning an empty list immediately.\n\nHere is the corrected implementation:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort a copy to maintain input immutability\n    sorted_items = sorted(items)\n    merged = [sorted_items[0]]\n    \n    for current in sorted_items[1:]:\n        prev_start, prev_end = merged[-1]\n        curr_start, curr_end = current\n        \n        if curr_start <= prev_end:\n            merged[-1] = (prev_start, max(prev_end, curr_end))\n        else:\n            merged.append(current)\n            \n    return merged\n```\n\nTo verify this fix, please implement the following test cases:\n1. **Empty Input**: Verify `[]` returns `[]`.\n2. **Touching Intervals**: Verify `[(1, 5), (5, 10)]` merges into `[(1, 10)]`.\n3. **Nested Intervals**: Verify `[(1, 10), (2, 5)]` merges into `[(1, 10)]`.",
            "output_sha256": "4d9123c4bb7584cd21d5cb4b99599b220f009ba37cae4bbb802af674c3ce0833",
            "timings": {
              "cache_n": 1,
              "prompt_n": 68,
              "prompt_ms": 79.192,
              "prompt_per_token_ms": 1.1645882352941175,
              "prompt_per_second": 858.6725931912315,
              "predicted_n": 359,
              "predicted_ms": 8110.013,
              "predicted_per_token_ms": 22.653667597765363,
              "predicted_per_second": 44.14296253285907
            }
          },
          {
            "prompt_id": "coding_review",
            "repetition": 2,
            "elapsed_seconds": 8.389983713626862,
            "completion_tokens": 359,
            "output": "The current implementation is defective because `sorted(items)` only orders the intervals; it does not merge them. To correctly merge overlapping or touching intervals (e.g., `[1, 5]` and `[5, 10]`), you must iterate through the sorted list and combine intervals where the current start is less than or equal to the previous end.\n\nTo ensure input immutability, we avoid modifying the original list by sorting a copy. We also handle empty inputs by returning an empty list immediately.\n\nHere is the corrected implementation:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort a copy to maintain input immutability\n    sorted_items = sorted(items)\n    merged = [sorted_items[0]]\n    \n    for current in sorted_items[1:]:\n        prev_start, prev_end = merged[-1]\n        curr_start, curr_end = current\n        \n        if curr_start <= prev_end:\n            merged[-1] = (prev_start, max(prev_end, curr_end))\n        else:\n            merged.append(current)\n            \n    return merged\n```\n\nTo verify this fix, please implement the following test cases:\n1. **Empty Input**: Verify `[]` returns `[]`.\n2. **Touching Intervals**: Verify `[(1, 5), (5, 10)]` merges into `[(1, 10)]`.\n3. **Nested Intervals**: Verify `[(1, 10), (2, 5)]` merges into `[(1, 10)]`.",
            "output_sha256": "4d9123c4bb7584cd21d5cb4b99599b220f009ba37cae4bbb802af674c3ce0833",
            "timings": {
              "cache_n": 64,
              "prompt_n": 5,
              "prompt_ms": 89.976,
              "prompt_per_token_ms": 17.9952,
              "prompt_per_second": 55.57037432204144,
              "predicted_n": 359,
              "predicted_ms": 8130.919,
              "predicted_per_token_ms": 22.712064245810055,
              "predicted_per_second": 44.029463336186225
            }
          },
          {
            "prompt_id": "coding_review",
            "repetition": 3,
            "elapsed_seconds": 8.232561767101288,
            "completion_tokens": 359,
            "output": "The current implementation is defective because `sorted(items)` only orders the intervals; it does not merge them. To correctly merge overlapping or touching intervals (e.g., `[1, 5]` and `[5, 10]`), you must iterate through the sorted list and combine intervals where the current start is less than or equal to the previous end.\n\nTo ensure input immutability, we avoid modifying the original list by sorting a copy. We also handle empty inputs by returning an empty list immediately.\n\nHere is the corrected implementation:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort a copy to maintain input immutability\n    sorted_items = sorted(items)\n    merged = [sorted_items[0]]\n    \n    for current in sorted_items[1:]:\n        prev_start, prev_end = merged[-1]\n        curr_start, curr_end = current\n        \n        if curr_start <= prev_end:\n            merged[-1] = (prev_start, max(prev_end, curr_end))\n        else:\n            merged.append(current)\n            \n    return merged\n```\n\nTo verify this fix, please implement the following test cases:\n1. **Empty Input**: Verify `[]` returns `[]`.\n2. **Touching Intervals**: Verify `[(1, 5), (5, 10)]` merges into `[(1, 10)]`.\n3. **Nested Intervals**: Verify `[(1, 10), (2, 5)]` merges into `[(1, 10)]`.",
            "output_sha256": "4d9123c4bb7584cd21d5cb4b99599b220f009ba37cae4bbb802af674c3ce0833",
            "timings": {
              "cache_n": 64,
              "prompt_n": 5,
              "prompt_ms": 92.689,
              "prompt_per_token_ms": 18.537799999999997,
              "prompt_per_second": 53.943833680372,
              "predicted_n": 359,
              "predicted_ms": 8137.846,
              "predicted_per_token_ms": 22.73141340782123,
              "predicted_per_second": 43.9919851026918
            }
          },
          {
            "prompt_id": "coding_review",
            "repetition": 4,
            "elapsed_seconds": 8.281295474618673,
            "completion_tokens": 359,
            "output": "The current implementation is defective because `sorted(items)` only orders the intervals; it does not merge them. To correctly merge overlapping or touching intervals (e.g., `[1, 5]` and `[5, 10]`), you must iterate through the sorted list and combine intervals where the current start is less than or equal to the previous end.\n\nTo ensure input immutability, we avoid modifying the original list by sorting a copy. We also handle empty inputs by returning an empty list immediately.\n\nHere is the corrected implementation:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort a copy to maintain input immutability\n    sorted_items = sorted(items)\n    merged = [sorted_items[0]]\n    \n    for current in sorted_items[1:]:\n        prev_start, prev_end = merged[-1]\n        curr_start, curr_end = current\n        \n        if curr_start <= prev_end:\n            merged[-1] = (prev_start, max(prev_end, curr_end))\n        else:\n            merged.append(current)\n            \n    return merged\n```\n\nTo verify this fix, please implement the following test cases:\n1. **Empty Input**: Verify `[]` returns `[]`.\n2. **Touching Intervals**: Verify `[(1, 5), (5, 10)]` merges into `[(1, 10)]`.\n3. **Nested Intervals**: Verify `[(1, 10), (2, 5)]` merges into `[(1, 10)]`.",
            "output_sha256": "4d9123c4bb7584cd21d5cb4b99599b220f009ba37cae4bbb802af674c3ce0833",
            "timings": {
              "cache_n": 64,
              "prompt_n": 5,
              "prompt_ms": 94.859,
              "prompt_per_token_ms": 18.971799999999998,
              "prompt_per_second": 52.7098114042948,
              "predicted_n": 359,
              "predicted_ms": 8183.823,
              "predicted_per_token_ms": 22.859840782122905,
              "predicted_per_second": 43.74483661242429
            }
          },
          {
            "prompt_id": "coding_review",
            "repetition": 5,
            "elapsed_seconds": 8.27882008254528,
            "completion_tokens": 359,
            "output": "The current implementation is defective because `sorted(items)` only orders the intervals; it does not merge them. To correctly merge overlapping or touching intervals (e.g., `[1, 5]` and `[5, 10]`), you must iterate through the sorted list and combine intervals where the current start is less than or equal to the previous end.\n\nTo ensure input immutability, we avoid modifying the original list by sorting a copy. We also handle empty inputs by returning an empty list immediately.\n\nHere is the corrected implementation:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort a copy to maintain input immutability\n    sorted_items = sorted(items)\n    merged = [sorted_items[0]]\n    \n    for current in sorted_items[1:]:\n        prev_start, prev_end = merged[-1]\n        curr_start, curr_end = current\n        \n        if curr_start <= prev_end:\n            merged[-1] = (prev_start, max(prev_end, curr_end))\n        else:\n            merged.append(current)\n            \n    return merged\n```\n\nTo verify this fix, please implement the following test cases:\n1. **Empty Input**: Verify `[]` returns `[]`.\n2. **Touching Intervals**: Verify `[(1, 5), (5, 10)]` merges into `[(1, 10)]`.\n3. **Nested Intervals**: Verify `[(1, 10), (2, 5)]` merges into `[(1, 10)]`.",
            "output_sha256": "4d9123c4bb7584cd21d5cb4b99599b220f009ba37cae4bbb802af674c3ce0833",
            "timings": {
              "cache_n": 64,
              "prompt_n": 5,
              "prompt_ms": 95.842,
              "prompt_per_token_ms": 19.1684,
              "prompt_per_second": 52.16919513365748,
              "predicted_n": 359,
              "predicted_ms": 8180.423,
              "predicted_per_token_ms": 22.850343575418993,
              "predicted_per_second": 43.76301812265698
            }
          },
          {
            "prompt_id": "incident_runbook",
            "repetition": 1,
            "elapsed_seconds": 4.409821692854166,
            "completion_tokens": 191,
            "output": "1. Capture the exact prompt and parameters that triggered the malformed tool call.\n2. Create a frozen fixture of the input to ensure consistent reproduction.\n3. Verify that the chat template has not been modified or corrupted.\n4. Test the output against the tool parser to identify the specific syntax error.\n5. Check for recent runtime revisions or environment variable changes.\n6. Validate the model hash to ensure the weights have not been corrupted.\n7. Analyze server logs for OOM errors or CUDA kernel timeouts.\n8. Inspect the sampling settings for anomalies like excessive temperature or top-p.\n9. Roll back the model or runtime version to the last known stable state.\n10. Compare the malformed output with the output from the rolled-back version.\n11. Run a comprehensive verification suite of tool-use benchmarks.\n12. Document the root cause and update the regression test suite.",
            "output_sha256": "615be06731a7014c09988670798db5ec664441e7413c5346d04f7f78ac4fbed1",
            "timings": {
              "cache_n": 1,
              "prompt_n": 68,
              "prompt_ms": 79.219,
              "prompt_per_token_ms": 1.164985294117647,
              "prompt_per_second": 858.379934106717,
              "predicted_n": 191,
              "predicted_ms": 4328.532,
              "predicted_per_token_ms": 22.781747368421055,
              "predicted_per_second": 43.89478927266796
            }
          },
          {
            "prompt_id": "incident_runbook",
            "repetition": 2,
            "elapsed_seconds": 4.2484855726361275,
            "completion_tokens": 178,
            "output": "1. Capture the exact prompt and parameters that triggered the malformed tool call.\n2. Attempt to reproduce the error using a frozen fixture to isolate the input.\n3. Verify that the chat template has not been modified or corrupted.\n4. Inspect the output parser for regressions in regex or JSON handling.\n5. Check for recent runtime revisions or environment variable changes.\n6. Validate the model hash to ensure the weights were not corrupted.\n7. Analyze server logs for CUDA errors or memory pressure warnings.\n8. Review recent updates to the inference engine or backend library.\n9. Roll back the model or server version to the last known stable state.\n10. Test the rollback using the original reproduction fixture.\n11. Perform a verification sweep across all defined tool schemas.\n12. Document the root cause and implement a regression test.",
            "output_sha256": "af601e76ce7338051410f85b9ba3aa3d4480e88433053ee915903688de0ad5bb",
            "timings": {
              "cache_n": 64,
              "prompt_n": 5,
              "prompt_ms": 98.605,
              "prompt_per_token_ms": 19.721,
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              "predicted_n": 178,
              "predicted_ms": 4021.46,
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              "predicted_per_second": 44.013865610997
            }
          },
          {
            "prompt_id": "incident_runbook",
            "repetition": 3,
            "elapsed_seconds": 4.264458168298006,
            "completion_tokens": 178,
            "output": "1. Capture the exact prompt and parameters that triggered the malformed tool call.\n2. Attempt to reproduce the error using a frozen fixture to isolate the input.\n3. Verify that the chat template has not been modified or corrupted.\n4. Inspect the output parser for regressions in regex or JSON handling.\n5. Check for recent runtime revisions or environment variable changes.\n6. Validate the model hash to ensure the weights were not corrupted.\n7. Analyze server logs for CUDA errors or memory pressure warnings.\n8. Review recent updates to the inference engine or backend library.\n9. Roll back the model or server version to the last known stable state.\n10. Test the rollback using the original reproduction fixture.\n11. Perform a verification sweep across all defined tool schemas.\n12. Document the root cause and implement a regression test.",
            "output_sha256": "af601e76ce7338051410f85b9ba3aa3d4480e88433053ee915903688de0ad5bb",
            "timings": {
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              "prompt_n": 5,
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          },
          {
            "prompt_id": "incident_runbook",
            "repetition": 4,
            "elapsed_seconds": 4.117669228464365,
            "completion_tokens": 178,
            "output": "1. Capture the exact prompt and parameters that triggered the malformed tool call.\n2. Attempt to reproduce the error using a frozen fixture to isolate the input.\n3. Verify that the chat template has not been modified or corrupted.\n4. Inspect the output parser for regressions in regex or JSON handling.\n5. Check for recent runtime revisions or environment variable changes.\n6. Validate the model hash to ensure the weights were not corrupted.\n7. Analyze server logs for CUDA errors or memory pressure warnings.\n8. Review recent updates to the inference engine or backend library.\n9. Roll back the model or server version to the last known stable state.\n10. Test the rollback using the original reproduction fixture.\n11. Perform a verification sweep across all defined tool schemas.\n12. Document the root cause and implement a regression test.",
            "output_sha256": "af601e76ce7338051410f85b9ba3aa3d4480e88433053ee915903688de0ad5bb",
            "timings": {
              "cache_n": 64,
              "prompt_n": 5,
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          {
            "prompt_id": "incident_runbook",
            "repetition": 5,
            "elapsed_seconds": 4.143128082156181,
            "completion_tokens": 178,
            "output": "1. Capture the exact prompt and parameters that triggered the malformed tool call.\n2. Attempt to reproduce the error using a frozen fixture to isolate the input.\n3. Verify that the chat template has not been modified or corrupted.\n4. Inspect the output parser for regressions in regex or JSON handling.\n5. Check for recent runtime revisions or environment variable changes.\n6. Validate the model hash to ensure the weights were not corrupted.\n7. Analyze server logs for CUDA errors or memory pressure warnings.\n8. Review recent updates to the inference engine or backend library.\n9. Roll back the model or server version to the last known stable state.\n10. Test the rollback using the original reproduction fixture.\n11. Perform a verification sweep across all defined tool schemas.\n12. Document the root cause and implement a regression test.",
            "output_sha256": "af601e76ce7338051410f85b9ba3aa3d4480e88433053ee915903688de0ad5bb",
            "timings": {
              "cache_n": 64,
              "prompt_n": 5,
              "prompt_ms": 100.727,
              "prompt_per_token_ms": 20.145400000000002,
              "prompt_per_second": 49.63912357163422,
              "predicted_n": 178,
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          "by_prompt": {
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              "median_end_to_end_seconds": 4.2484855726361275,
              "median_completion_tokens": 178
            }
          },
          "draft_tokens": 0,
          "draft_tokens_accepted": 0,
          "acceptance_rate": null
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        "peak_gpu_mib": 19960
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      "mtp": {
        "label": "mtp",
        "supported": true,
        "runs": [
          {
            "prompt_id": "technical_memo",
            "repetition": 1,
            "elapsed_seconds": 3.73878463357687,
            "completion_tokens": 263,
            "output": "**MEMORANDUM**\n\n**TO:** Engineering Team  \n**FROM:** Systems Architecture  \n**SUBJECT:** Memory Constraints in Long-Context Inference\n\nWhile a Q4 quantized model may successfully load into VRAM at an 8K context window, it may still trigger Out-of-Memory (OOM) errors when scaled to 64K. This occurs because model weights are static, but runtime memory requirements are dynamic.\n\nThe primary driver is the KV (Key-Value) cache. The cache stores previous token representations to avoid redundant computations; its size scales linearly with sequence length. Moving from 8K to 64K context increases the KV cache footprint by eightfold, often exceeding the remaining VRAM not occupied by the weights.\n\nFurthermore, the system must account for runtime allocations and the output reserve. The model requires a dedicated memory buffer to generate new tokens; if the KV cache consumes all available headroom, there is no space left for the activation tensors required to produce the next token.\n\nMeasured headroom at 8K is often deceptive. A model that fits comfortably at low context may leave only a small margin of free memory. When the KV cache expands to 64K, it quickly exhausts this margin, leading to a crash despite the weights remaining constant.",
            "output_sha256": "742dbff5fcb3f6b4fac7f38fa9b4220baa6e79561cbc5f10908c2136d007ac99",
            "timings": {
              "cache_n": 1,
              "prompt_n": 73,
              "prompt_ms": 146.128,
              "prompt_per_token_ms": 2.001753424657534,
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              "predicted_ms": 3527.821,
              "predicted_per_token_ms": 13.464965648854962,
              "predicted_per_second": 74.26680662085747,
              "draft_n": 318,
              "draft_n_accepted": 158
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          },
          {
            "prompt_id": "technical_memo",
            "repetition": 2,
            "elapsed_seconds": 3.962625492364168,
            "completion_tokens": 277,
            "output": "**MEMORANDUM**\n\n**TO:** Engineering Team  \n**FROM:** Systems Architecture  \n**SUBJECT:** Memory Constraints for Q4 Model Scaling (8K to 64K Context)\n\nWhile a Q4 quantized model may successfully load into VRAM at an 8K context window, scaling to 64K often triggers Out-of-Memory (OOM) errors. This is because model weights are static, but runtime memory requirements are dynamic.\n\nThe primary driver is the KV (Key-Value) cache. The KV cache stores previous token representations to avoid redundant computations; its size scales linearly with sequence length. Moving from 8K to 64K increases the KV cache footprint by eightfold, often exceeding the remaining VRAM after weights are loaded.\n\nFurthermore, the system must account for runtime allocations and the output reserve. The model requires a buffer of contiguous memory to generate new tokens; if the KV cache consumes all available headroom, there is no space left for the activation tensors required for the next token's inference.\n\nMeasured headroom at 8K is often deceptive. While the GPU may show available memory upon loading, that space is rapidly consumed as the context window fills. To support 64K, we must either implement KV cache quantization or increase available VRAM to accommodate the linear growth of the context buffer.",
            "output_sha256": "86136ad9975d47f884afc55b6b6029a7e7998d2eb8b8b3438c8eaf55ccfa5db8",
            "timings": {
              "cache_n": 69,
              "prompt_n": 5,
              "prompt_ms": 133.266,
              "prompt_per_token_ms": 26.6532,
              "prompt_per_second": 37.518947068269476,
              "predicted_n": 277,
              "predicted_ms": 3627.259,
              "predicted_per_token_ms": 13.142242753623188,
              "predicted_per_second": 76.09051352550232,
              "draft_n": 330,
              "draft_n_accepted": 168
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          },
          {
            "prompt_id": "technical_memo",
            "repetition": 3,
            "elapsed_seconds": 5.837522257119417,
            "completion_tokens": 277,
            "output": "**MEMORANDUM**\n\n**TO:** Engineering Team  \n**FROM:** Systems Architecture  \n**SUBJECT:** Memory Constraints for Q4 Model Scaling (8K to 64K Context)\n\nWhile a Q4 quantized model may successfully load into VRAM at an 8K context window, scaling to 64K often triggers Out-of-Memory (OOM) errors. This is because model weights are static, but runtime memory requirements are dynamic.\n\nThe primary driver is the KV (Key-Value) cache. The KV cache stores previous token representations to avoid redundant computations; its size scales linearly with sequence length. Moving from 8K to 64K increases the KV cache footprint by eightfold, often exceeding the remaining VRAM after weights are loaded.\n\nFurthermore, the system must account for runtime allocations and the output reserve. The model requires a buffer of contiguous memory to generate new tokens; if the KV cache consumes all available headroom, there is no space left for the activation tensors required for the next token's inference.\n\nMeasured headroom at 8K is often deceptive. While the GPU may show available memory upon loading, that space is rapidly consumed as the context window fills. To support 64K, we must either implement KV cache quantization or increase available VRAM to accommodate the linear growth of the context buffer.",
            "output_sha256": "86136ad9975d47f884afc55b6b6029a7e7998d2eb8b8b3438c8eaf55ccfa5db8",
            "timings": {
              "cache_n": 69,
              "prompt_n": 5,
              "prompt_ms": 216.666,
              "prompt_per_token_ms": 43.3332,
              "prompt_per_second": 23.076994083058718,
              "predicted_n": 277,
              "predicted_ms": 5158.294,
              "predicted_per_token_ms": 18.689471014492753,
              "predicted_per_second": 53.506062275628345,
              "draft_n": 330,
              "draft_n_accepted": 168
            }
          },
          {
            "prompt_id": "technical_memo",
            "repetition": 4,
            "elapsed_seconds": 5.499874759465456,
            "completion_tokens": 277,
            "output": "**MEMORANDUM**\n\n**TO:** Engineering Team  \n**FROM:** Systems Architecture  \n**SUBJECT:** Memory Constraints for Q4 Model Scaling (8K to 64K Context)\n\nWhile a Q4 quantized model may successfully load into VRAM at an 8K context window, scaling to 64K often triggers Out-of-Memory (OOM) errors. This is because model weights are static, but runtime memory requirements are dynamic.\n\nThe primary driver is the KV (Key-Value) cache. The KV cache stores previous token representations to avoid redundant computations; its size scales linearly with sequence length. Moving from 8K to 64K increases the KV cache footprint by eightfold, often exceeding the remaining VRAM after weights are loaded.\n\nFurthermore, the system must account for runtime allocations and the output reserve. The model requires a buffer of contiguous memory to generate new tokens; if the KV cache consumes all available headroom, there is no space left for the activation tensors required for the next token's inference.\n\nMeasured headroom at 8K is often deceptive. While the GPU may show available memory upon loading, that space is rapidly consumed as the context window fills. To support 64K, we must either implement KV cache quantization or increase available VRAM to accommodate the linear growth of the context buffer.",
            "output_sha256": "86136ad9975d47f884afc55b6b6029a7e7998d2eb8b8b3438c8eaf55ccfa5db8",
            "timings": {
              "cache_n": 69,
              "prompt_n": 5,
              "prompt_ms": 224.609,
              "prompt_per_token_ms": 44.921800000000005,
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              "predicted_n": 277,
              "predicted_ms": 5272.268,
              "predicted_per_token_ms": 19.102420289855072,
              "predicted_per_second": 52.34938739836442,
              "draft_n": 330,
              "draft_n_accepted": 168
            }
          },
          {
            "prompt_id": "technical_memo",
            "repetition": 5,
            "elapsed_seconds": 4.400271397083998,
            "completion_tokens": 277,
            "output": "**MEMORANDUM**\n\n**TO:** Engineering Team  \n**FROM:** Systems Architecture  \n**SUBJECT:** Memory Constraints for Q4 Model Scaling (8K to 64K Context)\n\nWhile a Q4 quantized model may successfully load into VRAM at an 8K context window, scaling to 64K often triggers Out-of-Memory (OOM) errors. This is because model weights are static, but runtime memory requirements are dynamic.\n\nThe primary driver is the KV (Key-Value) cache. The KV cache stores previous token representations to avoid redundant computations; its size scales linearly with sequence length. Moving from 8K to 64K increases the KV cache footprint by eightfold, often exceeding the remaining VRAM after weights are loaded.\n\nFurthermore, the system must account for runtime allocations and the output reserve. The model requires a buffer of contiguous memory to generate new tokens; if the KV cache consumes all available headroom, there is no space left for the activation tensors required for the next token's inference.\n\nMeasured headroom at 8K is often deceptive. While the GPU may show available memory upon loading, that space is rapidly consumed as the context window fills. To support 64K, we must either implement KV cache quantization or increase available VRAM to accommodate the linear growth of the context buffer.",
            "output_sha256": "86136ad9975d47f884afc55b6b6029a7e7998d2eb8b8b3438c8eaf55ccfa5db8",
            "timings": {
              "cache_n": 69,
              "prompt_n": 5,
              "prompt_ms": 196.739,
              "prompt_per_token_ms": 39.3478,
              "prompt_per_second": 25.41438149019767,
              "predicted_n": 277,
              "predicted_ms": 4200.972,
              "predicted_per_token_ms": 15.22091304347826,
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              "draft_n": 330,
              "draft_n_accepted": 168
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          },
          {
            "prompt_id": "coding_review",
            "repetition": 1,
            "elapsed_seconds": 5.0005965903401375,
            "completion_tokens": 346,
            "output": "The current implementation is incorrect because `sorted(items)` only orders the intervals without merging them. To merge overlapping or touching intervals (e.g., `[1, 3]` and `[3, 5]`), you must iterate through the sorted list and combine intervals where the current start is less than or equal to the previous end.\n\nTo ensure input immutability, we avoid modifying the original list by creating a new sorted copy. We also handle empty inputs by returning an empty list immediately.\n\nHere is the corrected implementation:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort a copy to maintain input immutability\n    sorted_items = sorted(items)\n    merged = [sorted_items[0]]\n    \n    for current in sorted_items[1:]:\n        prev = merged[-1]\n        # Merge if intervals overlap or touch\n        if current[0] <= prev[1]:\n            merged[-1] = (prev[0], max(prev[1], current[1]))\n        else:\n            merged.append(current)\n            \n    return merged\n```\n\nTo verify this fix, please implement the following test cases:\n1. **Empty Input**: Verify that `[]` returns `[]`.\n2. **Touching Intervals**: Ensure `[(1, 3), (3, 5)]` merges into `[(1, 5)]`.\n3. **Nested Intervals**: Ensure `[(1, 10), (2, 5)]` merges into `[(1, 10)]`.",
            "output_sha256": "f97fbc1672b35a7c73b826e20f14eecac7e33016d3dcf5ecf3fb6f308e444c8c",
            "timings": {
              "cache_n": 1,
              "prompt_n": 68,
              "prompt_ms": 147.542,
              "prompt_per_token_ms": 2.169735294117647,
              "prompt_per_second": 460.88571389841536,
              "predicted_n": 346,
              "predicted_ms": 4849.851,
              "predicted_per_token_ms": 14.057539130434781,
              "predicted_per_second": 71.13620604014433,
              "draft_n": 324,
              "draft_n_accepted": 238
            }
          },
          {
            "prompt_id": "coding_review",
            "repetition": 2,
            "elapsed_seconds": 5.929749671369791,
            "completion_tokens": 350,
            "output": "The current implementation is defective because `sorted(items)` only orders the intervals; it does not merge them. To correctly merge overlapping or touching intervals (e.g., `[1, 5]` and `[5, 10]`), you must iterate through the sorted list and combine intervals where the current start is less than or equal to the previous end.\n\nTo ensure input immutability, we avoid modifying the original list by sorting a copy. We also handle empty inputs by returning an empty list immediately.\n\nHere is the corrected implementation:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort a copy to maintain input immutability\n    sorted_items = sorted(items)\n    merged = [sorted_items[0]]\n    \n    for current in sorted_items[1:]:\n        prev = merged[-1]\n        # Merge if intervals overlap or touch\n        if current[0] <= prev[1]:\n            merged[-1] = (prev[0], max(prev[1], current[1]))\n        else:\n            merged.append(current)\n            \n    return merged\n```\n\nTo verify this fix, please implement the following test cases:\n1. **Empty Input**: Verify `[]` returns `[]`.\n2. **Touching Intervals**: Verify `[(1, 5), (5, 10)]` merges into `[(1, 10)]`.\n3. **Nested Intervals**: Verify `[(1, 10), (2, 5)]` merges into `[(1, 10)]`.",
            "output_sha256": "a0303971c623d729937371e22354a1c7b6c0af6e408fe54623266f5126928695",
            "timings": {
              "cache_n": 64,
              "prompt_n": 5,
              "prompt_ms": 198.806,
              "prompt_per_token_ms": 39.7612,
              "prompt_per_second": 25.150146373851893,
              "predicted_n": 350,
              "predicted_ms": 5231.227,
              "predicted_per_token_ms": 14.98918911174785,
              "predicted_per_second": 66.71474971359491,
              "draft_n": 321,
              "draft_n_accepted": 242
            }
          },
          {
            "prompt_id": "coding_review",
            "repetition": 3,
            "elapsed_seconds": 5.701077997684479,
            "completion_tokens": 350,
            "output": "The current implementation is defective because `sorted(items)` only orders the intervals; it does not merge them. To correctly merge overlapping or touching intervals (e.g., `[1, 5]` and `[5, 10]`), you must iterate through the sorted list and combine intervals where the current start is less than or equal to the previous end.\n\nTo ensure input immutability, we avoid modifying the original list by sorting a copy. We also handle empty inputs by returning an empty list immediately.\n\nHere is the corrected implementation:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort a copy to maintain input immutability\n    sorted_items = sorted(items)\n    merged = [sorted_items[0]]\n    \n    for current in sorted_items[1:]:\n        prev = merged[-1]\n        # Merge if intervals overlap or touch\n        if current[0] <= prev[1]:\n            merged[-1] = (prev[0], max(prev[1], current[1]))\n        else:\n            merged.append(current)\n            \n    return merged\n```\n\nTo verify this fix, please implement the following test cases:\n1. **Empty Input**: Verify `[]` returns `[]`.\n2. **Touching Intervals**: Verify `[(1, 5), (5, 10)]` merges into `[(1, 10)]`.\n3. **Nested Intervals**: Verify `[(1, 10), (2, 5)]` merges into `[(1, 10)]`.",
            "output_sha256": "a0303971c623d729937371e22354a1c7b6c0af6e408fe54623266f5126928695",
            "timings": {
              "cache_n": 64,
              "prompt_n": 5,
              "prompt_ms": 190.813,
              "prompt_per_token_ms": 38.1626,
              "prompt_per_second": 26.203665368711775,
              "predicted_n": 350,
              "predicted_ms": 5118.638,
              "predicted_per_token_ms": 14.66658452722063,
              "predicted_per_second": 68.18220003055501,
              "draft_n": 321,
              "draft_n_accepted": 242
            }
          },
          {
            "prompt_id": "coding_review",
            "repetition": 4,
            "elapsed_seconds": 4.0986007787287235,
            "completion_tokens": 350,
            "output": "The current implementation is defective because `sorted(items)` only orders the intervals; it does not merge them. To correctly merge overlapping or touching intervals (e.g., `[1, 5]` and `[5, 10]`), you must iterate through the sorted list and combine intervals where the current start is less than or equal to the previous end.\n\nTo ensure input immutability, we avoid modifying the original list by sorting a copy. We also handle empty inputs by returning an empty list immediately.\n\nHere is the corrected implementation:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort a copy to maintain input immutability\n    sorted_items = sorted(items)\n    merged = [sorted_items[0]]\n    \n    for current in sorted_items[1:]:\n        prev = merged[-1]\n        # Merge if intervals overlap or touch\n        if current[0] <= prev[1]:\n            merged[-1] = (prev[0], max(prev[1], current[1]))\n        else:\n            merged.append(current)\n            \n    return merged\n```\n\nTo verify this fix, please implement the following test cases:\n1. **Empty Input**: Verify `[]` returns `[]`.\n2. **Touching Intervals**: Verify `[(1, 5), (5, 10)]` merges into `[(1, 10)]`.\n3. **Nested Intervals**: Verify `[(1, 10), (2, 5)]` merges into `[(1, 10)]`.",
            "output_sha256": "a0303971c623d729937371e22354a1c7b6c0af6e408fe54623266f5126928695",
            "timings": {
              "cache_n": 64,
              "prompt_n": 5,
              "prompt_ms": 212.593,
              "prompt_per_token_ms": 42.5186,
              "prompt_per_second": 23.51911869158439,
              "predicted_n": 350,
              "predicted_ms": 3882.867,
              "predicted_per_token_ms": 11.125693409742121,
              "predicted_per_second": 89.88203819497294,
              "draft_n": 321,
              "draft_n_accepted": 242
            }
          },
          {
            "prompt_id": "coding_review",
            "repetition": 5,
            "elapsed_seconds": 3.8001036942005157,
            "completion_tokens": 350,
            "output": "The current implementation is defective because `sorted(items)` only orders the intervals; it does not merge them. To correctly merge overlapping or touching intervals (e.g., `[1, 5]` and `[5, 10]`), you must iterate through the sorted list and combine intervals where the current start is less than or equal to the previous end.\n\nTo ensure input immutability, we avoid modifying the original list by sorting a copy. We also handle empty inputs by returning an empty list immediately.\n\nHere is the corrected implementation:\n\n```python\ndef merge_intervals(items):\n    if not items:\n        return []\n    \n    # Sort a copy to maintain input immutability\n    sorted_items = sorted(items)\n    merged = [sorted_items[0]]\n    \n    for current in sorted_items[1:]:\n        prev = merged[-1]\n        # Merge if intervals overlap or touch\n        if current[0] <= prev[1]:\n            merged[-1] = (prev[0], max(prev[1], current[1]))\n        else:\n            merged.append(current)\n            \n    return merged\n```\n\nTo verify this fix, please implement the following test cases:\n1. **Empty Input**: Verify `[]` returns `[]`.\n2. **Touching Intervals**: Verify `[(1, 5), (5, 10)]` merges into `[(1, 10)]`.\n3. **Nested Intervals**: Verify `[(1, 10), (2, 5)]` merges into `[(1, 10)]`.",
            "output_sha256": "a0303971c623d729937371e22354a1c7b6c0af6e408fe54623266f5126928695",
            "timings": {
              "cache_n": 64,
              "prompt_n": 5,
              "prompt_ms": 166.331,
              "prompt_per_token_ms": 33.2662,
              "prompt_per_second": 30.06054193144994,
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              "predicted_per_token_ms": 10.404873925501432,
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              "draft_n": 321,
              "draft_n_accepted": 242
            }
          },
          {
            "prompt_id": "incident_runbook",
            "repetition": 1,
            "elapsed_seconds": 2.501501400023699,
            "completion_tokens": 191,
            "output": "1. Capture the exact prompt and parameters that triggered the malformed tool call.\n2. Create a frozen fixture of the input to ensure consistent reproduction.\n3. Verify that the chat template has not been modified or corrupted.\n4. Test the output against the tool parser to identify the specific syntax error.\n5. Check for recent runtime revisions or environment variable changes.\n6. Validate the model hash to ensure the weights have not been corrupted.\n7. Analyze server logs for OOM errors or CUDA kernel timeouts.\n8. Inspect the sampling settings for anomalies like excessive temperature or top-p.\n9. Roll back the model or runtime version to the last known stable state.\n10. Compare the malformed output with the output from the rolled-back version.\n11. Run a comprehensive verification suite of tool-use benchmarks.\n12. Document the root cause and update the regression test suite.",
            "output_sha256": "615be06731a7014c09988670798db5ec664441e7413c5346d04f7f78ac4fbed1",
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          {
            "prompt_id": "incident_runbook",
            "repetition": 2,
            "elapsed_seconds": 2.796393360942602,
            "completion_tokens": 190,
            "output": "1. Capture the exact prompt and parameters that triggered the malformed tool call.\n2. Create a frozen fixture of the input to ensure consistent reproduction.\n3. Verify that the chat template has not been modified or corrupted.\n4. Test the output against the tool parser to identify the specific syntax error.\n5. Check for recent runtime revisions or environment variable changes.\n6. Validate the model hash to ensure the weights have not been corrupted.\n7. Analyze server logs for CUDA errors or memory pressure warnings.\n8. Inspect the sampling settings for anomalies like excessive temperature or top-p.\n9. Roll back the model or server version to the last known stable state.\n10. Compare the malformed output with the output from the rolled-back version.\n11. Run a comprehensive verification suite of tool-use benchmarks.\n12. Document the root cause and update the regression test suite.",
            "output_sha256": "191ef164a8c2fafab6e8b9c14942a4c3ab15c1d089b1739b0e8841ce4d55f704",
            "timings": {
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            "prompt_id": "incident_runbook",
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            "elapsed_seconds": 2.899658687412739,
            "completion_tokens": 190,
            "output": "1. Capture the exact prompt and parameters that triggered the malformed tool call.\n2. Create a frozen fixture of the input to ensure consistent reproduction.\n3. Verify that the chat template has not been modified or corrupted.\n4. Test the output against the tool parser to identify the specific syntax error.\n5. Check for recent runtime revisions or environment variable changes.\n6. Validate the model hash to ensure the weights have not been corrupted.\n7. Analyze server logs for CUDA errors or memory pressure warnings.\n8. Inspect the sampling settings for anomalies like excessive temperature or top-p.\n9. Roll back the model or server version to the last known stable state.\n10. Compare the malformed output with the output from the rolled-back version.\n11. Run a comprehensive verification suite of tool-use benchmarks.\n12. Document the root cause and update the regression test suite.",
            "output_sha256": "191ef164a8c2fafab6e8b9c14942a4c3ab15c1d089b1739b0e8841ce4d55f704",
            "timings": {
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            "prompt_id": "incident_runbook",
            "repetition": 4,
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            "output": "1. Capture the exact prompt and parameters that triggered the malformed tool call.\n2. Create a frozen fixture of the input to ensure consistent reproduction.\n3. Verify that the chat template has not been modified or corrupted.\n4. Test the output against the tool parser to identify the specific syntax error.\n5. Check for recent runtime revisions or environment variable changes.\n6. Validate the model hash to ensure the weights have not been corrupted.\n7. Analyze server logs for CUDA errors or memory pressure warnings.\n8. Inspect the sampling settings for anomalies like excessive temperature or top-p.\n9. Roll back the model or server version to the last known stable state.\n10. Compare the malformed output with the output from the rolled-back version.\n11. Run a comprehensive verification suite of tool-use benchmarks.\n12. Document the root cause and update the regression test suite.",
            "output_sha256": "191ef164a8c2fafab6e8b9c14942a4c3ab15c1d089b1739b0e8841ce4d55f704",
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            "output": "1. Capture the exact prompt and parameters that triggered the malformed tool call.\n2. Create a frozen fixture of the input to ensure consistent reproduction.\n3. Verify that the chat template has not been modified or corrupted.\n4. Test the output against the tool parser to identify the specific syntax error.\n5. Check for recent runtime revisions or environment variable changes.\n6. Validate the model hash to ensure the weights have not been corrupted.\n7. Analyze server logs for CUDA errors or memory pressure warnings.\n8. Inspect the sampling settings for anomalies like excessive temperature or top-p.\n9. Roll back the model or server version to the last known stable state.\n10. Compare the malformed output with the output from the rolled-back version.\n11. Run a comprehensive verification suite of tool-use benchmarks.\n12. Document the root cause and update the regression test suite.",
            "output_sha256": "191ef164a8c2fafab6e8b9c14942a4c3ab15c1d089b1739b0e8841ce4d55f704",
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          "end_to_end_speedup_ratio": 2.1785774149215733,
          "decode_speedup_ratio": 2.1961192167273467
        },
        {
          "prompt_id": "incident_runbook",
          "repetition": 1,
          "outputs_identical": true,
          "end_to_end_speedup_ratio": 1.762869967937011,
          "decode_speedup_ratio": 1.8231654230820262
        },
        {
          "prompt_id": "incident_runbook",
          "repetition": 2,
          "outputs_identical": false,
          "end_to_end_speedup_ratio": 1.5192732295731304,
          "decode_speedup_ratio": 1.7086863424638337
        },
        {
          "prompt_id": "incident_runbook",
          "repetition": 3,
          "outputs_identical": false,
          "end_to_end_speedup_ratio": 1.4706759063781498,
          "decode_speedup_ratio": 1.7221554718041985
        },
        {
          "prompt_id": "incident_runbook",
          "repetition": 4,
          "outputs_identical": false,
          "end_to_end_speedup_ratio": 1.577309575067828,
          "decode_speedup_ratio": 1.7595929175059009
        },
        {
          "prompt_id": "incident_runbook",
          "repetition": 5,
          "outputs_identical": false,
          "end_to_end_speedup_ratio": 1.295725714509191,
          "decode_speedup_ratio": 1.4338250953723248
        }
      ],
      "identical_outputs": 1,
      "paired_runs": 15,
      "median_end_to_end_speedup_ratio": 1.5192732295731304,
      "median_decode_speedup_ratio": 1.6726685681478397,
      "mtp_extra_peak_vram_mib": 574
    }
  }
}
```
