The Core Ideas Behind Paged Attention for KV‑Caching

Idea 1: Memory Management

Modern memory management is built on virtual memory. Techniques that use non‑contiguous memory blocks—such as segmentation, inverted page tables, hashed page tables, scatter‑gather, and the buddy system—are the foundation for higher memory utilization. Paging implements these ideas, allowing a process’s page table to map virtual pages to physical memory and extending the mapping onto disk to form a full virtual‑memory system.

Idea 2: Paged Attention as Virtual Memory for KV‑Cache

Paged Attention treats KV‑caching as a paging problem. It is a non‑contiguous memory KV‑caching technique that uses a block table to map virtual blocks to physical blocks. The vLLM block concept aligns with page‑table mappings, enabling the block‑table to read physical memory through the page‑table.

Idea 3: Efficiency Gains in Specific Scenarios

In prompt‑heavy or multi‑output generation, a copy‑on‑write (CoW) strategy enables simple and efficient reuse of KV‑cache entries. During beam search, the large amount of forward‑reuse demand is also dramatically optimized by dynamic mapping combined with CoW.

Idea 4: Distributed High‑Concurrency Implementation

The vLLM paper aims to solve KV‑caching problems with Paged Attention. The core difficulty lies in asynchronous distributed scheduling. The scheduler’s role is to manage the KV‑Cache Manager’s allocation of CPU and GPU blocks. Coordination between workers and the scheduler enables distributed KV‑cache management, and the KV‑Cache Manager offers multiple management modes as a standardized exploration.

Idea 5: Low‑Level Optimizations

Global memory allocation and deallocation can be accelerated with lock‑free mechanisms such as a global free‑list and bump‑pointer allocation, achieving O(1) operations and avoiding lock contention. GPU virtual‑memory features (CUDA Virtual Memory Management APIs) can simplify Paged Attention; for example, Microsoft’s vAttention uses VMM APIs to decouple physical memory from virtual addresses, keeping a contiguous virtual layout while preventing physical fragmentation and preserving compatibility with FlashAttention.

Summary

Paged Attention builds on mature virtual‑memory concepts to create a paging‑based KV‑cache that reduces fragmentation, increases request throughput, and supports efficient reuse through copy‑on‑write. Its distributed scheduler and GPU‑aware block management enable high‑concurrency serving, while lock‑free allocation and CUDA VMM further optimize performance. The approach leaves room for future work on exploiting data redundancy.

References

https://developers.redhat.com/articles/2025/07/24/how-pagedattention-resolves-memory-waste-llm-systems

https://scalingknowledge.substack.com/p/an-introduction-to-vllm-and-pagedattention

https://cloudthrill.ca/what-is-vllm-features

https://www.aleksagordic.com/blog/vllm