Continuous Batching Implementation Using KV Caching

AI Impact Summary

This document details the implementation of continuous batching in large language models, leveraging attention mechanisms and KV caching to maximize throughput. The core concept involves processing multiple conversations in parallel, swapping them out as they complete, to optimize for high-load serving scenarios. This approach relies on projecting the input prompt into query, key, and value states, applying a causal attention mask to control token interactions, and then generating the next token in the sequence, a process known as prefill.