RAG & Retrieval
Chunked Prefill
A scheduling technique that splits a long prompt's prefill into equal-sized chunks and processes them over several iterations instead of in one pass.
In depth
A scheduling technique that splits a long prompt's prefill into equal-sized chunks and processes them over several iterations instead of in one pass. Each batch pairs one prefill chunk with many in-progress decode steps, so the chunk saturates compute while the decodes ride along cheaply. Introduced in the SARATHI paper, it stops long prompts from stalling other users' streams and reduces idle bubbles under pipeline parallelism.
Primary source: arxiv.org
Example
In practice, developers reach for Chunked Prefill when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Chunked Prefill?
A scheduling technique that splits a long prompt's prefill into equal-sized chunks and processes them over several iterations instead of in one pass.
Why does Chunked Prefill matter for AI developers?
Chunked Prefill sits in the RAG & Retrieval 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 Chunked Prefill?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Chunked Prefill. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
RAG & Retrieval
Chunking
The process of splitting large documents into smaller, overlapping segments for embedding and retrieval in RAG systems.
RAG & Retrieval
Context Recall
A RAG evaluation metric that measures how much of the information needed to answer a question was actually retrieved.
RAG & Retrieval
Multi-Head Latent Attention (MLA)
Multi-head latent attention is an attention design introduced by DeepSeek that compresses keys and values into a small shared latent vector, so the model caches that latent instead of full per-head keys and values.
Put this concept to work
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