Inference
Continuous Batching
A serving technique that schedules work one model iteration at a time, so requests can join or leave a running batch at every token step instead of waiting for the whole batch to finish.
In depth
A serving technique that schedules work one model iteration at a time, so requests can join or leave a running batch at every token step instead of waiting for the whole batch to finish. It was introduced as iteration-level scheduling in the Orca paper at OSDI 2022, which reported 36.9x higher throughput than NVIDIA FasterTransformer on GPT-3 175B at the same latency. Finished requests return immediately and new ones start without waiting, which is why vLLM, SGLang, and TGI all build on it.
Primary source: usenix.org
Example
In practice, developers reach for Continuous Batching when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Inference.
FAQ
Common questions
What is Continuous Batching?
A serving technique that schedules work one model iteration at a time, so requests can join or leave a running batch at every token step instead of waiting for the whole batch to finish.
Why does Continuous Batching matter for AI developers?
Continuous Batching sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Continuous Batching?
Developers Digest publishes tutorials and videos that cover Inference topics including Continuous Batching. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Constrained Decoding
A generation technique that restricts which tokens a model may pick at each step so the output is guaranteed to match a format such as a regular expression, a grammar, or a JSON Schema.
Inference
Speculative Decoding
An inference technique where a small, fast draft model proposes several tokens ahead and the large target model checks them all in one parallel forward pass.
Inference
Continued Pretraining
Running more next-token-prediction training on an already pretrained model using a large unlabeled corpus from a target domain, such as code, math, medicine, or a new language.
Put this concept to work
Get Smarter About AI Dev
New tutorials, open-source projects, and deep dives on coding agents - delivered weekly.
One email per weekReal code, not theoryFree forever