Problems / LLM serving, hard
Live requests were routed to a batch-inference queue meant for offline jobs: it holds requests for 30+ seconds.
Free to open. Runs in your browser, no sign-up.
Success ≥ 99%, p99 ≤ 5 s.
20 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 Real-time 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 |
| Real-time GPU poolSelf-hosted serving pool | AI app calls it.GPU servers batch requests together; throughput rises with batch size until memory runs out. | 8 slots |
Try it first. Open one hint at a time if you get stuck.
Batch APIs trade latency for price.
Nothing is overloaded; it’s just slow.
Serve live traffic from a real-time endpoint: give it the capacity, keep batch for offline work.