Inference & Serving · 2022

Orca: Continuous (Iteration-Level) Batching

Gyeong-In Yu, Joo Seong Jeong, Geon-Woo Kim, et al.

Introduced iteration-level (continuous) batching for transformer serving, letting the server add and retire requests at each generation step instead of processing a batch to completion, which sharply raised GPU throughput for LLM inference.

Editorial record

Plain-language summary

Traditional batching runs a whole batch of requests together until all finish, so short requests wait for long ones and the GPU sits underused. Orca schedules at the granularity of a single decoding iteration: after each token step it can inject newly arrived requests and remove completed ones, keeping the batch continuously full. Combined with selective batching that handles operations with different sequence lengths correctly, this delivered large throughput and latency gains and became the standard serving pattern later adopted by systems like vLLM.

Knowledge graph

Relationships

Antecedents

Source record

Provenance

Record ID
P-402
Record created
2026-07-13
Last reviewed
2026-07-14
Record version
2

Citation caveat: Citation metadata is approximate and marked unverified in the source dataset.