{
  "run_id": "6f89eb6649bb4b839b3f8e2ff373608b",
  "timestamp": "2026-07-28T05:39:29+00:00",
  "target_id": "qwen3.6-35b-a3b-nvfp4@your-node",
  "target": {
    "id": "qwen3.6-35b-a3b-nvfp4@your-node",
    "display_name": "Qwen3.6-35B-A3B (NVFP4)",
    "serving": {
      "engine": "vllm",
      "model_name": "brain",
      "base_url": "http://your-node:8001/v1",
      "service": "spark",
      "max_model_len": 65536,
      "flags": {
        "enforce_eager": true,
        "gpu_memory_utilization": 0.55,
        "kv_cache_dtype": "fp8",
        "max_num_batched_tokens": 4096,
        "moe_backend": "flashinfer_b12x",
        "reasoning_parser": "qwen3",
        "tool_call_parser": "qwen3_coder",
        "enable_auto_tool_choice": true
      }
    },
    "host": "your-node",
    "family": "qwen3.6",
    "params_b": 35,
    "active_params_b": 3,
    "quant": "nvfp4",
    "checkpoint": null,
    "tier": "production",
    "aliases": [
      "brain",
      "spark"
    ],
    "notes": "Live behind the `brain` and `local-moe` aliases. This is the model the bench exists to interrogate: it was deployed without measurement. FLAGS OF CONCERN: --enforce-eager disables CUDA graphs (costs throughput) and --gpu-memory-utilization 0.55 leaves ~45% of unified memory unused, capping KV cache and therefore concurrency. Both are worth an A/B once the baseline is recorded.\n",
    "slug": "qwen3.6-35b-a3b-nvfp4__your-node",
    "inspect_model": "openai-api/spark/brain"
  },
  "host": {
    "id": "your-node",
    "ssh": "your-node",
    "hardware": "NVIDIA GB10 Grace Blackwell (DGX Spark)",
    "memory_gb": 121,
    "memory_bandwidth_gbs": 273,
    "notes": "Unified LPDDR5X. Memory bandwidth is the binding constraint on decode throughput, not compute -- large dense models are bandwidth-starved here while MoE models with small active-param counts do comparatively well.\n"
  },
  "quality": [],
  "perf": {
    "engine": "vllm",
    "points": [
      {
        "concurrency": 1,
        "input_tokens": 1024,
        "output_tokens": 256,
        "n_requests": 1,
        "completed": 1,
        "failed": 0,
        "duration_s": 8.309,
        "output_tps_total": 30.81,
        "output_tps_per_stream": 30.81,
        "ttft_p50_ms": 314.74,
        "ttft_p95_ms": 314.74,
        "tpot_p50_ms": 31.35,
        "prefill_tps": 4530.7,
        "error": null
      },
      {
        "concurrency": 8,
        "input_tokens": 1024,
        "output_tokens": 256,
        "n_requests": 8,
        "completed": 8,
        "failed": 0,
        "duration_s": 11.002,
        "output_tps_total": 186.16,
        "output_tps_per_stream": 23.32,
        "ttft_p50_ms": 1918.86,
        "ttft_p95_ms": 2048.13,
        "tpot_p50_ms": 35.58,
        "prefill_tps": 838.2,
        "error": null
      },
      {
        "concurrency": 32,
        "input_tokens": 1024,
        "output_tokens": 256,
        "n_requests": 32,
        "completed": 32,
        "failed": 0,
        "duration_s": 32.638,
        "output_tps_total": 251.0,
        "output_tps_per_stream": 8.07,
        "ttft_p50_ms": 5044.49,
        "ttft_p95_ms": 8067.19,
        "tpot_p50_ms": 104.93,
        "prefill_tps": 289.0,
        "error": null
      }
    ],
    "peak_memory_gb": null,
    "idle_memory_gb": null,
    "notes": null
  },
  "verdict": {
    "signal_score": null,
    "reference_score": null,
    "single_stream_tps": 30.8,
    "interactive_viable": true,
    "peak_throughput_tps": 251.0,
    "peak_throughput_concurrency": 32
  },
  "runner": {
    "host": "workstation",
    "python": "3.12.3",
    "platform": "Linux-7.0.0-28-generic-x86_64-with-glibc2.39"
  },
  "schema_version": 1
}