Vllm

Name the architectural shifts vLLM v1 made relative to v0

Recognize vLLM and the optimization that made it famous

Spell out TensorRT-LLM and pinpoint its production differentiator

Name TGI's maintainer and its niche among serving stacks

Identify PagedAttention and the project it ships with

vLLM defaults to a page size of 16. Pick what makes 1 and 1024 worse choices.

What problem does chunked prefill solve in production LLM serving?

How does prefix KV-cache sharing reduce serving cost across requests?

Multi-tenant SaaS with 500 customer-specific fine-tunes. Merge or swap?

How does PagedAttention enable prefix sharing across requests and what data structure makes it cheap?

When would you choose vLLM, TensorRT-LLM, SGLang, or TGI for a production serving deployment?

Match each production serving framework to its defining strength

What problem does PagedAttention solve and why does block-based allocation enable larger batches?

What is the primary mechanism by which PagedAttention increases serving throughput?

Why did continuous batching replace static batching for LLM serving?

What does the vLLM-style continuous-batching scheduler actually do at each step?

Put the vLLM-style continuous-batching scheduler steps in correct order for one iteration

Walk through paged attention end to end, page table, block lookup, and how it enables higher throughput.

What problem does paged attention (vLLM) solve, and what OS concept does it borrow from?