Title: Quiz 14 — Disaggregated Prefill/Decode with NVIDIA Dynamo
Course: AIINFRA 200: Production Inference Serving & GPU Orchestration > Section: Week 14 — Disaggregated Prefill/Decode with NVIDIA Dynamo

Q: Prefill is compute-bound, while decode is memory-bandwidth-bound.
- True
- False
A: True

Q: Which tensor parallelism setting best matches decode's resource profile?
- No parallelism, since decode only produces one token at a time
- Low tensor parallelism, to minimize communication overhead
- High tensor parallelism, to keep per-token latency low
- Tensor parallelism has no effect on decode performance
A: High tensor parallelism, to keep per-token latency low

Q: Name the two inference engines (besides vLLM) that NVIDIA Dynamo supports with full feature parity for disaggregated serving.
Feedback: Sample/expected answer: TensorRT-LLM and SGLang

Q: What is NVIDIA Dynamo's primary role in a disaggregated deployment?
- It replaces vLLM, TensorRT-LLM, and SGLang as the inference engine
- It coordinates GPUs/nodes into a unified system — routing, KV-cache transfer, placement, autoscaling, and fault handling
- It only handles model quantization
- It is a single-GPU scheduler with no multi-node capability
A: It coordinates GPUs/nodes into a unified system — routing, KV-cache transfer, placement, autoscaling, and fault handling

Q: Disaggregating prefill and decode is always a strict performance win, regardless of workload.
- True
- False
A: False

Q: What is the name of the low-latency communication library Dynamo uses to accelerate KV-cache transfer between GPUs?
Feedback: Sample/expected answer: NIXL

Q: For a standard chatbot or moderate RAG workload, what does the lecture suggest is often the simpler and better default?
- Disaggregated serving with three or more GPU pools
- Aggregated serving, with prefill and decode co-located on the same GPU
- Running prefill on CPU only
- Disabling KV-cache entirely
A: Aggregated serving, with prefill and decode co-located on the same GPU

Q: What does Dynamo's KV-aware Router do?
Feedback: Sample/expected answer: It directs incoming requests to the worker already holding the most relevant cached KV state, to minimize redundant recomputation.