# Generated from run-config.json. Contains no credentials or cluster endpoints.
schema_version: 1
source: "run-config.json"
tested_configuration:
  schema: "stable-upstream-multi-replica-benchmark-v1"
  date: "2026-08-10"
  topology:
    endpoint_picker_replicas: 1
    model_replicas_tested:
      - 1
      - 2
      - 4
    gpus_per_model_replica: 1
  hardware:
    gpu_per_model_replica: "NVIDIA H100"
  model_service:
    model: "GPT-OSS 20B"
    runtime: "Red Hat AI Inference Server vLLM"
    tensor_parallel_size: 1
    max_model_len: 32768
    max_num_batched_tokens: 8192
    max_num_seqs: 128
    gpu_memory_utilization: 0.9
    prefix_cache: "disabled"
  endpoint_picker:
    version: "llm-d Endpoint Picker v0.9.0"
    flow_control_gate: "enabled"
    detector: "token-concurrency"
    max_token_concurrency: 20000
    estimated_output_tokens_included: false
    headroom: 0.25
    picker: "random"
    priority_bands:
      - 100
      - 50
      - 0
      - -10
    fairness: "round-robin within each priority band"
  traffic:
    scenario_file: "scenario.json"
    description: "Cache-off long-context interference with offered load scaled per model replica."
    scenario_names:
      - "one_replica_scaled_load"
      - "two_replica_scaled_load"
      - "four_replica_scaled_load"
  execution:
    arrival: "open-loop Poisson replay with noisy sinusoidal phases"
    seed: 18
    production_repeats_per_topology: 3
    matched_offered_load_per_gpu: true
    load_generator: "GuideLLM"
    worker_processes_per_tenant_per_gpu: 8
    worker_poll_interval_seconds: 0.01
  decision_limits:
    offered_load_difference_per_gpu_percent: 1
    maximum_served_rps_per_gpu_regression_percent: 15
    maximum_premium_burst_p95_ttft_regression_percent: 20
    required_non_200_responses: 0
    required_endpoint_picker_restarts: 0
  runner:
    scenario_source: "pipeline/benchmark.py"
    sha256: "3811ec26c46bf3a26fa643698ec54bf569bb4bc99c3ea22ca18f805cb077b8e0"
    traffic_driver: "GuideLLM"
    guidellm_version: "0.7.0"
    trace_compiler: "pipeline/guidellm_trace.py"
    launcher: "pipeline/run_guidellm_scenario.py"
    scenario_file: "scenario.json"