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  • Applicability of PagedAttention to Batched Inference

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The memory efficiency benefits of partitioning the key-value cache into non-contiguous, fixed-size blocks are exclusively realized when processing multiple inference requests simultaneously in a batch.

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Updated 2025-10-06

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Ch.5 Inference - Foundations of Large Language Models

Foundations of Large Language Models

Foundations of Large Language Models Course

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  • An LLM inference system is designed for high throughput by processing multiple, independent user requests simultaneously. These requests generate text sequences of widely varying lengths. The system developers observe that while the total memory allocated for key-value caches is high, much of it is often unused and unavailable for new requests. Which statement best analyzes why a memory management strategy that divides the key-value cache into non-contiguous, fixed-size blocks is particularly effective in this environment?

  • Inference System Memory Management Analysis

  • The memory efficiency benefits of partitioning the key-value cache into non-contiguous, fixed-size blocks are exclusively realized when processing multiple inference requests simultaneously in a batch.

  • Memory Management in Concurrent LLM Inference

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