Inference
Prefill
The first phase of LLM inference, where the model processes the entire input prompt in parallel and builds the KV cache before producing any output.
In depth
The first phase of LLM inference, where the model processes the entire input prompt in parallel and builds the KV cache before producing any output. Because all prompt tokens are handled at once, prefill saturates GPU compute even at small batch sizes. Its duration grows with prompt length, which makes it the main driver of time to first token on long contexts.
Primary source: arxiv.org
Example
In practice, developers reach for 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 Prefill?
The first phase of LLM inference, where the model processes the entire input prompt in parallel and builds the KV cache before producing any output.
Why does Prefill matter for AI developers?
Prefill 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 Prefill?
Developers Digest publishes tutorials and videos that cover Inference topics including Prefill. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Input vs Output Token Pricing
The split pricing model where LLM APIs charge one rate per million tokens you send (input) and a higher rate per million tokens the model generates (output).
Inference
Prefix Caching
An inference-engine optimization that keeps the KV cache from earlier requests and reuses it when a new request starts with the same tokens, so the shared prefix skips prefill.
Inference
Decode Phase
The second phase of LLM inference, where the model generates output tokens one at a time, each step reading the KV cache built so far and adding to it.
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