Long prompts stall active LLM streams. Would you separate prefill and decoding?

Instruction: Compare disaggregated serving with chunked prefill, include KV-transfer failures, and define the measurements that would decide.

Context: Choose an LLM serving architecture for mixed prompt lengths using observed latency, capacity, and operating cost.

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I’d first check that large prefills are causing the stalls rather than queueing, memory pressure, or a slow client. Separating prefill and decoding could let me tune first-token latency and ongoing token delivery independently, but it adds KV-cache transfer, routing, and another failure boundary...

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Your preparation path

Work through these questions in order. Read the answer aloud, then explain it in your own words.

1. Start with the foundations

Build the vocabulary and explain the core decisions.

  1. What drives latency in an LLM application besides the model itself? Free sample
  2. How would you explain batching to a product engineer? Free sample
  3. How would you explain throughput versus latency in model serving? Free sample

2. Apply it to a real workflow

Practice diagnosis, validation, and everyday tradeoffs.

  1. What signals matter most when optimizing cost without hiding quality loss? Free sample
  2. Traffic spikes create queueing even though the model service itself is healthy. How would you investigate? Member answer
  3. The product is fast on short prompts and unstable on long tool-using requests. What would you inspect? Member answer

3. Prepare for senior discussions

Explain failure boundaries, recovery, and production choices.

  1. How would you reason about tail latency in a multi-step LLM workflow? Member answer
  2. Long prompts stall active LLM streams. Would you separate prefill and decoding? Member answer
  3. How is KV-cache quantization different from weight quantization, and how would you validate it? Member answer

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