{
  "id": "qwen35-m4pro-20260306",
  "title": "Qwen3.5 on M4 Pro: dated precision matrix",
  "kind": "benchmark",
  "source_type": "community_operator_report",
  "summary": "A fixed March M4 Pro report with repeated generation, first-token and memory measurements across available precisions.",
  "runtime": "MLX",
  "runtime_topics": [
    "MLX"
  ],
  "hardware_topics": [
    "apple"
  ],
  "platform_topics": [
    "mac"
  ],
  "model_label": "Qwen3.5 0.8B / 2B / 4B / 9B / 27B / 35B-A3B",
  "model_family": "Qwen",
  "model_id": null,
  "model_release_date": null,
  "model_artifact": null,
  "evidence_state": "source_reported",
  "site_reproduced": false,
  "record_type": "curated_reference",
  "performance_ranking_allowed": false,
  "metrics": null,
  "metrics_status": "site_metrics_unmeasured; attributed_source_figures_in_editorial.reported_results",
  "conditions": {
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    "weight_hash": null,
    "runtime_execution_revision": null,
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    "concurrency": null,
    "cache_state": null,
    "quantization": null,
    "speculative_decoding": null,
    "temperature": null,
    "top_p": null
  },
  "reported_setup": {
    "system": "Apple M4 Pro 10P+4E / 20 GPU cores / 64 GB unified memory",
    "node_count": 1,
    "parallelism": null,
    "kv_dtype": null,
    "context_limit_tokens": null,
    "experiment_date": "2026-03-06"
  },
  "test_profiles": [
    {
      "label": "Generation speed",
      "evidence_url": "https://github.com/weklund/mlx-coding-bench/blob/5ae9b491f7a0539d5abef949eded6bd57785a981/measurements/llm_benchmarks/Apple_M4_Pro_10P%2B4E%2B20GPU_64GB/2026-03-06__19%3A47%3A24/REPORT.md",
      "conditions": {
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        "input_tokens": 256,
        "output_tokens": 100,
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        "cache_state": null,
        "quantization": "int4 / int8 / BF16 as listed per model",
        "speculative_decoding": null,
        "temperature": null,
        "top_p": null,
        "runtime_version": "MLX 0.31.0 / mlx-lm 0.30.7",
        "repetitions": 20,
        "warmup_iterations": 1
      }
    },
    {
      "label": "First-token latency and peak memory",
      "evidence_url": "https://github.com/weklund/mlx-coding-bench/blob/5ae9b491f7a0539d5abef949eded6bd57785a981/measurements/llm_benchmarks/Apple_M4_Pro_10P%2B4E%2B20GPU_64GB/2026-03-06__19%3A47%3A24/REPORT.md",
      "conditions": {
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        "runtime_version": "MLX 0.31.0 / mlx-lm 0.30.7",
        "repetitions": 20,
        "warmup_iterations": 1
      }
    }
  ],
  "deployment_options": [],
  "recipe_parameters": null,
  "measurement_scope": "inference_speed",
  "notes": [
    "Generation and first-token/memory tests use different prompt lengths.",
    "The report states 20 iterations, one warmup and mean/standard deviation."
  ],
  "limitations": [
    "Only listed model/precision cells were tested.",
    "The historical report is retained as dated evidence, not current Qwen3.8 performance."
  ],
  "comparison_status": "not_comparable",
  "editorial_status": "source_derived_detailed_article",
  "public_benchmark_snapshot": false,
  "source": {
    "publisher": "weklund",
    "url": "https://github.com/weklund/mlx-coding-bench",
    "repository": "weklund/mlx-coding-bench",
    "platform": "github",
    "revision": "5ae9b491f7a0539d5abef949eded6bd57785a981",
    "document_path": "measurements/llm_benchmarks/Apple_M4_Pro_10P+4E+20GPU_64GB/2026-03-06__19:47:24/REPORT.md",
    "pinned_url": "https://github.com/weklund/mlx-coding-bench/blob/5ae9b491f7a0539d5abef949eded6bd57785a981/measurements/llm_benchmarks/Apple_M4_Pro_10P%2B4E%2B20GPU_64GB/2026-03-06__19%3A47%3A24/REPORT.md",
    "report_url": "https://github.com/weklund/mlx-coding-bench/blob/5ae9b491f7a0539d5abef949eded6bd57785a981/measurements/llm_benchmarks/Apple_M4_Pro_10P%2B4E%2B20GPU_64GB/2026-03-06__19%3A47%3A24/REPORT.md",
    "document_sha256": "ff21a1f8a2597867e191a376cea28af293d47dfff82bb423193009b05c7ef5a3",
    "last_commit_at": "2026-07-01T19:16:36Z",
    "checked_on": "2026-10-17",
    "metadata_status": "checked"
  },
  "license": {
    "declared_spdx": "MIT",
    "scope": "Repository metadata only; per-file, model, data and conversion terms remain separate."
  },
  "editorial": {
    "version": 1,
    "published_on": "2026-10-17",
    "modified_on": "2026-10-17",
    "byline": "Run AI Here editorial",
    "creation_method": "AI-assisted source reading and original editorial synthesis; no independent model execution.",
    "answer": "The operator report is dated 6 March 2026 and names an M4 Pro with ten performance and four efficiency CPU cores, twenty GPU cores and 64 GB unified memory. It uses MLX 0.31.0 and mlx-lm 0.30.7, one warmup followed by twenty measured iterations. These are historical Qwen3.5 results, not measurements of current Qwen3.8 or another Apple chip.",
    "sections": [
      {
        "heading": "Which exact machine and stack were tested?",
        "paragraphs": [
          "The operator report is dated 6 March 2026 and names an M4 Pro with ten performance and four efficiency CPU cores, twenty GPU cores and 64 GB unified memory. It uses MLX 0.31.0 and mlx-lm 0.30.7, one warmup followed by twenty measured iterations. These are historical Qwen3.5 results, not measurements of current Qwen3.8 or another Apple chip."
        ],
        "citations": [
          "https://github.com/weklund/mlx-coding-bench/blob/5ae9b491f7a0539d5abef949eded6bd57785a981/measurements/llm_benchmarks/Apple_M4_Pro_10P+4E+20GPU_64GB/2026-03-06__19:47:24/REPORT.md#L3"
        ]
      },
      {
        "heading": "Why are the generation and latency tables separate?",
        "paragraphs": [
          "Generation uses a 256-token prompt and 100-token output. First-token latency and peak-memory tables use a 4,096-token prompt. The 4B int4 example consequently reports 68.0 ± 0.4 generated tokens/s in one profile and 6.14 ± 0.25 seconds to first token in the other. Those values cannot be assembled into one measured end-to-end 256-token request."
        ],
        "citations": [
          "https://github.com/weklund/mlx-coding-bench/blob/5ae9b491f7a0539d5abef949eded6bd57785a981/measurements/llm_benchmarks/Apple_M4_Pro_10P+4E+20GPU_64GB/2026-03-06__19:47:24/REPORT.md#L21"
        ]
      },
      {
        "heading": "What does the dense versus MoE example show?",
        "paragraphs": [
          "The selected int4 rows put 27B at 11.8 ± 0.2 tokens/s and 35B-A3B at 25.6 ± 0.5 under the generation profile. At the longer prompt their reported peaks are 22.1 and 23.7 GiB. That is an observed speed/memory tradeoff on this stack; it does not prove equal coding quality or a broad rule that one architecture is better."
        ],
        "citations": [
          "https://github.com/weklund/mlx-coding-bench/blob/5ae9b491f7a0539d5abef949eded6bd57785a981/measurements/llm_benchmarks/Apple_M4_Pro_10P+4E+20GPU_64GB/2026-03-06__19:47:24/REPORT.md#L45"
        ]
      },
      {
        "heading": "Which claims are not reproduced here?",
        "paragraphs": [
          "The source adds qualitative recommendations and secondary-source quality claims after its measured tables. We keep those separate from the directly reported MLX timing and memory cells. Missing BF16 cells stay missing; they are not zero-speed or failed measurements. Weight hashes, cache state and concurrent-user counts are not specified by the selected report."
        ],
        "citations": [
          "https://github.com/weklund/mlx-coding-bench/blob/5ae9b491f7a0539d5abef949eded6bd57785a981/measurements/llm_benchmarks/Apple_M4_Pro_10P+4E+20GPU_64GB/2026-03-06__19:47:24/REPORT.md#L58"
        ]
      }
    ],
    "facts": [],
    "reported_results": [
      {
        "label": "Qwen3.5-4B int4 generation",
        "metric": "generation",
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        "unit": "tokens/s",
        "statistic": "mean of 20 measurements",
        "uncertainty": {
          "standard_deviation": 0.4
        },
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          "model": "Qwen3.5-4B int4",
          "quantization": "int4",
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        },
        "evidence_state": "source_reported",
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