Principle:Ggml org Llama cpp KV Cache Management
| Knowledge Sources | |
|---|---|
| Domains | KV_Cache, Memory |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
KV Cache Management is the principle of efficiently storing and reusing key-value attention states across transformer decoding steps.
Description
This principle covers the data structures and algorithms for managing the key-value cache that stores attention states from previously processed tokens. During autoregressive generation, each new token only needs to attend to cached keys and values rather than recomputing attention over the entire sequence. The implementation includes standard KV caches, ISWA (Incremental Sliding Window Attention) variants for models with mixed attention patterns, and fine-grained cell-level management for cache slot allocation.
Usage
Apply this principle when managing memory for transformer inference contexts, implementing cache eviction policies, handling multi-sequence batching with shared cache prefixes, or supporting models with sliding window attention.
Theoretical Basis
In transformer models, the self-attention mechanism computes attention scores between the current token's query and all previous tokens' keys, then produces a weighted sum of their values. Without caching, this requires O(n^2) computation per generated token. The KV cache stores previously computed key and value tensors so that each new token only requires O(n) computation. Cache management involves slot allocation (mapping sequence positions to cache cells), defragmentation (compacting sparse cache entries), sequence operations (forking, copying, and removing sequences), and memory-efficient storage formats. The ISWA variant handles models that use different attention window sizes across layers, maintaining separate caches for full-attention and sliding-window layers.