We explore "sleep-time compute," a method for optimizing the efficiency of large language model inference by pre-processing context during idle time. This involves giving an AI a body of information, which it then analyzes offline to anticipate potential future queries. Subsequent questions can be answered more quickly and potentially with less computational cost.

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