Problems / LLM serving, medium
Speculative decoding speeds up answers, but the single draft-model GPU is now the bottleneck at 150 requests a second.
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Success ≥ 99%, p99 ≤ 5 s.
150 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 Draft model (speculative).Little's law: throughput ≈ concurrency ÷ latency. 200 slots at 50 ms serve about 4,000 req/s. | 3 replicas × 500 slots · timeout 30 s |
| Draft model (speculative)Speculative decoding | AI app calls it.A small draft model guesses several tokens; the large model checks them in one pass and keeps the ones it agrees with. | 32 slots |
Try it first. Open one hint at a time if you get stuck.
The draft model runs on every request.
It has far fewer slots than the main pool.
Give the draft model more capacity.