Dynamic Batching: Balancing LLM Throughput and Latency
Dynamic batching groups LLM requests like a bus that leaves on a schedule or when full, whichever comes first. This improves throughput in inference servers by avoiding long waits. The footgun: all requests in a batch are still held hostage by the slowest one.
Why it exists
GPUs excel at parallel work, but LLM inference can fail to use them fully. Processing requests one-by-one is inefficient. Static batching helps by grouping requests, but it's too rigid: it waits for a fixed number of requests, causing delays for the first arrivals. Dynamic batching was created to solve this initial delay problem.
The mental model
Think of dynamic batching like a city bus. The bus leaves on a strict schedule (a timeout) or as soon as it's full (a max batch size), whichever happens first. This approach balances the goal of filling every seat (maximizing GPU utilization) with the need to keep moving (maintaining low latency).
How it works
An LLM inference server using dynamic batching collects incoming requests. It has two triggers to start processing a batch. The first trigger is the batch size limit; if enough requests arrive to fill the batch, it's sent for processing immediately. The second trigger is a timeout; if the first request in a queue has been waiting for too long, the server sends the current (partially filled) batch for processing. This prevents any single request from waiting indefinitely.
When to use it
Use dynamic batching in production LLM services where the arrival rate of requests is unpredictable. It offers a better trade-off between throughput and latency compared to static batching. It's a solid intermediate optimization when you need to improve GPU utilization without implementing more complex strategies like continuous batching.
When not to use it
Dynamic batching is inefficient when request completion times vary widely. Because the entire batch must wait for the slowest request to finish, one long generation task can stall many short ones, leaving the GPU underutilized. For workloads with highly variable output lengths, continuous batching is a superior approach as it frees up GPU slots the moment a request is finished.
One canonical example
An inference server receives two requests in the same dynamic batch. The first asks for a single-sentence summary, and the second asks for a 500-word article. The GPU generates the summary in under a second, but that result cannot be returned to the user. It must wait for the entire 500-word article to finish generating before the whole batch is considered complete.
Interview question
In which scenario would dynamic batching be least effective for an LLM inference server?
- a.When requests within a batch have widely varying output lengthsCorrect
- b.When the server needs to prioritize individual request latency over overall throughput
- c.When all requests in a batch have similar, short processing times
- d.When requests arrive at a highly unpredictable rate
Why? this is the answer
The card states that dynamic batching is inefficient "when request completion times vary widely" because the entire batch must wait for the slowest request. Therefore, widely varying output lengths (which lead to varying completion times) make it least effective. While dynamic batching involves a latency/throughput trade-off, its primary inefficiency arises from this variability, not just a general preference for individual latency.
Just read this? Test yourself on what you have been reading.
Read the original → bentoml.com
- #llm
- #inference
- #optimization
- #batching
- #gpu
You just looked this up. Could you explain it out loud?
That is the part interviews actually test. Tezvyn takes questions like this one and gives you what the interviewer is really checking, the answer that lands, and the mistake that ends the conversation, in the four minutes before your next meeting.
The iPhone app is on the way
We are building it. Until it lands, nothing here is held back from you: every interview card, your saved cards, streaks and the job board all work in Safari, plus hundreds of free practice quizzes of thirty questions each. Sign in and it all carries over to the app the day it arrives.
Want it as an icon? Tap Share at the bottom of Safari, then Add to Home Screen. It opens full screen and the cards you have read stay available offline.
We are hiring for this. Open roles that interview on llm — each one lists the topics its interview covers.
See open roles