Continuous Batching

Walk through how a prefix-cache hit changes the work that chunked prefill has to do

When does splitting prefill and decode onto separate replica pools actually pay off in production?

Trace how one slow request stalls every co-batched generation step.

Recognize vLLM and the optimization that made it famous

Name TGI's maintainer and its niche among serving stacks

Identify PagedAttention and the project it ships with

Describe how continuous batching interacts with the KV cache

What problem does chunked prefill solve in production LLM serving?

A chatbot team reports TTFT P99 jumped from 300ms to 1.4s overnight. Which root cause is most likely?

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?

What traces and metrics do you need to debug a P99 TTFT regression in production LLM serving?

What problem do DistServe and Splitwise solve by separating prefill and decode onto different GPUs?

Spot the errors in this description of continuous batching

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

How does Sarathi-Serve's chunked prefill change the latency profile of a serving system?

Why is batching the single biggest throughput lever for LLM decode?