Problems / LLM serving, easy
You serve an open-weights model on your own GPU pool with 32 slots. Requests take about a second; traffic is 50 a second.
Free to open. Runs in your browser, no sign-up.
Success ≥ 99%, p99 ≤ 5 s.
50 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 GPU serving 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 |
| GPU serving poolSelf-hosted serving pool | AI app calls it.GPU servers batch requests together; throughput rises with batch size until memory runs out. | 32 slots |
Try it first. Open one hint at a time if you get stuck.
32 slots ÷ 1 s ≈ 32 requests a second.
Add GPUs (replicas of the serving pool).
Double the serving pool’s capacity.