Problems / RAG and retrieval, easy
A cross-encoder reranks the top 50 chunks for every question on one GPU with 16 slots, at 300 questions a second.
Free to open. Runs in your browser, no sign-up.
Success ≥ 99%, p99 ≤ 5 s.
300 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 Reranker, 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 |
| RerankerReranker | AI app calls it.A reranker reads each retrieved passage together with the question and reorders by relevance. | 16 slots |
| GPU serving poolSelf-hosted serving pool | AI app calls it.GPU servers batch requests together; throughput rises with batch size until memory runs out. | 2000 slots |
Try it first. Open one hint at a time if you get stuck.
16 slots ÷ 60 ms ≈ 270 a second.
Follow one question through each retrieval stage; the stage that turns red is the one to fix first.
Add reranker capacity (or rerank fewer chunks).