Title: Quiz 3 — Production Inference Fundamentals and the vLLM Engine
Course: AIINFRA 200: Production Inference Serving & GPU Orchestration > Section: Week 03 — Production Inference Fundamentals and the vLLM Engine

Q: What research institution originated vLLM?
- Stanford HAI
- UC Berkeley's Sky Computing Lab
- MIT CSAIL
- NVIDIA Research
A: UC Berkeley's Sky Computing Lab

Q: PagedAttention's primary purpose is to enable request batching.
- True
- False
A: False

Q: In PagedAttention, what is the GPU-memory analog of an operating system's "page"?
- A model weight tensor
- A fixed-size KV-cache block
- An entire request's context window
- A CUDA stream
A: A fixed-size KV-cache block

Q: Name the two scheduling phases that vLLM's V1 engine deliberately separates so that one doesn't block the other.
Feedback: Sample/expected answer: Prefill and decode

Q: A team is building an interactive customer-support chatbot where users expect near-instant replies. Which metric should they prioritize when tuning their vLLM deployment?
- Maximum tokens/sec throughput at any batch size
- Time-to-first-token (TTFT) and tail latency
- Total GPU utilization percentage
- Number of model architectures supported
A: Time-to-first-token (TTFT) and tail latency

Q: Increasing batch size always improves every performance metric uniformly, so serving engines should maximize batch size by default.
- True
- False
A: False

Q: Which command launches an OpenAI-API-compatible vLLM server for a given model?
- `vllm run <model-id>`
- `vllm serve <model-id>`
- `vllm start --api <model-id>`
- `vllm launch --openai <model-id>`
A: `vllm serve <model-id>`

Q: Name at least two of the five parallelism strategies vLLM supports for distributing inference across multiple GPUs or nodes.
Feedback: Sample/expected answer: Any two of: tensor parallelism, pipeline parallelism, data parallelism, expert parallelism, context parallelism