PagedAttention and vLLM efficiently manage memory for decoding tasks in large language models, enabling multiple sampled outputs from a single user prompt while optimizing resource usage.
In many LLM applications, the ability to generate multiple outputs from a single prompt is crucial. PagedAttention facilitates this by allowing memory sharing among outputs, significantly enhancing efficiency.
Parallel sampling allows users to receive multiple outputs for a single input, leveraging shared key-value cache to minimize memory footprint, thereby optimizing the decoding process in LLM services.
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