{
  "lesson": "17-disaggregated-prefill-decode",
  "title": "Disaggregated Prefill/Decode — NVIDIA Dynamo and llm-d",
  "questions": [
    {
      "stage": "pre",
      "question": "Why do prefill and decode want different optimal GPU configurations?",
      "options": [
        "Prefill must run on AMD and decode on NVIDIA",
        "They use different model weights",
        "Prefill is compute-bound on matmul throughput; decode is memory-bound on HBM bandwidth, so colocating them wastes one resource",
        "Decode requires more network bandwidth"
      ],
      "correct": 2,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "What transport does NVIDIA Dynamo use to move KV cache between the prefill and decode pools?",
      "options": [
        "gRPC bidi only",
        "NIXL (RDMA/InfiniBand when available, TCP fallback)",
        "Plain HTTP",
        "Shared filesystem on NFS"
      ],
      "correct": 1,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "When does disaggregation NOT pay off according to the lesson?",
      "options": [
        "RAG with 8K+ prefixes",
        "MoE workloads on Blackwell",
        "Multi-tenant serving with shared system prompts",
        "Prompts under 512 tokens and outputs under 200 tokens, where the KV transfer tax dominates the gain"
      ],
      "correct": 3,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "What is the core architectural difference between Dynamo and llm-d?",
      "options": [
        "Dynamo is open source; llm-d is closed",
        "Dynamo runs on CPU; llm-d on GPU",
        "Dynamo is a stack-above orchestrator over vLLM/SGLang/TRT-LLM; llm-d is Kubernetes-native with prefill/decode/router as independent Services",
        "Dynamo requires AMD; llm-d requires NVIDIA"
      ],
      "correct": 2,
      "explanation": ""
    },
    {
      "stage": "post",
      "question": "Which Dynamo components automatically tune the prefill:decode ratio for an SLO?",
      "options": [
        "Marlin kernels",
        "Planner Profiler and SLA Planner",
        "Cluster Autoscaler",
        "Sidecar proxy and Envoy filter"
      ],
      "correct": 1,
      "explanation": ""
    },
    {
      "stage": "post",
      "question": "How does disaggregation interact with cache-aware routing from Phase 17 · 11?",
      "options": [
        "Disaggregation disables KV cache reuse entirely",
        "The cache-aware router can land a request on the decode pool already holding its prefix; on miss it flows prefill -> decode, so the two compound",
        "They are mutually exclusive",
        "Cache-aware routing is only for colocated serving"
      ],
      "correct": 1,
      "explanation": ""
    }
  ]
}