Inference & Serving · 2022
Orca: Continuous (Iteration-Level) Batching
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
CombinesEvidence: Direct
PagedAttention / vLLM
Continuous batching combined in vLLM
P-401
Source record
Provenance
- Record ID
- P-402
- Record created
- 2026-07-13
- Last reviewed
- 2026-07-14
- Record version
- 2
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