When running LLMs at scale, the real limitation is GPU memory rather than compute, mainly because each request requires a KV cache to store token-level data. In traditional setups, a large fixed memory block is reserved per request based on the maximum sequence length, which leads to significant unused space and limits concurrency. Paged Attention improves this by breaking the KV cache into smaller, flexible chunks that are allocated only when needed, similar to how virtual memory works. It also allows multiple requests with the same starting prompt to share memory and only duplicate it when their outputs start to differ. This approach greatly improves memory efficiency, allowing significantly higher throughput with very little overhead.
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Horizontal Clues