Problems / RAG and retrieval, easy

Embedding calls queue up

Every question is embedded first, but the embedding deployment allows 8 concurrent calls at 400 questions a second.

Free to open. Runs in your browser, no sign-up.

Your goals

Success ≥ 99%, time to first token p99 ≤ 2.5 s.

The traffic

400 requests a second for 30 seconds: 100% request (reads).

The system

What each component does in this design, and what calls it. The simulator mimics each one from these settings.

ComponentIts job hereSettings
Load balancerLoad balancerTraffic 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 serviceLoad balancer forwards to it.Calls Embedding model, 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
Embedding modelEmbedding modelAI app calls it.Embeddings turn text into vectors where similar meanings sit close together.–
GPU serving poolSelf-hosted serving poolAI app calls it.GPU servers batch requests together; throughput rises with batch size until memory runs out.2000 slots

Hints

Try it first. Open one hint at a time if you get stuck.

Hint 1

8 calls ÷ 25 ms ≈ 320 a second.

Hint 2

Follow one question through each retrieval stage; the stage that turns red is the one to fix first.

One way to solve it

Raise the embedding model’s concurrency.

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