The answer
Many implementations cache intermediate attention data from earlier positions to reduce repeated work. The exact caching strategy depends on the model and inference system.
How does an LLM generate a complete response one token at a time?
Does the model always recompute the full prompt for every token?
More FAQs in How does an LLM generate a complete response one token at a time?
The application can stream decoded output as tokens become available. Displayed chunks may combine several tokens or wait for complete characters before appearing.
A generation limit or another stopping condition may be reached before the sentence is complete. Applications should detect truncation and handle it appropriately.
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