Problems / LLM serving, medium
Disaggregated serving with a small decode pool: answers got longer and decode slots fill up.
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Success ≥ 99%, p99 ≤ 5 s.
Like a coding problem, a solution has to pass every case, not just the one it was built for. Submit in the lab runs all 3; 1 of them are hidden until then.
120 requests a second for 30 seconds: 100% request (reads).
What each component does in this design, and what calls it. The simulator mimics each one from these settings.
| Component | Its job here | Settings |
|---|---|---|
| Load balancerLoad balancer | Traffic enters here.Calls AI app.Layer 4 balancers route by IP and port; layer 7 balancers read HTTP and can route by path or header. | – |
| AI appApp service | Load balancer forwards to it.Calls Prefill GPU pool, Decode GPU pool.Little's law: throughput ≈ concurrency ÷ latency. 200 slots at 50 ms serve about 4,000 req/s. | 3 replicas × 500 slots · timeout 30 s |
| Prefill GPU poolPrefill GPU pool | AI app calls it.Splitting prefill (reading the prompt) from decode (writing the answer) onto separate GPUs stops long prompts from stalling everyone else’s output. | 128 slots |
| Decode GPU poolDecode GPU pool | AI app calls it.Decoding is memory-bound: each request holds its KV cache in GPU memory for as long as it generates. | 128 slots |