Stateful KV Cache Management for LLMs: Balancing Space, Time, Accuracy, and Positional Fidelity

TL;DR

This paper investigates how different KV cache management strategies impact large language model inference quality, emphasizing the importance of preserving positional coherence and respecting architectural limits to maintain generation fidelity.

Contribution

It introduces empirical analysis of cache eviction strategies, highlighting the critical role of positional integrity and proposing guidelines for effective cache management in LLMs.

Findings

Cache size nearing context window degrades quality sharply.

Disrupting positional encodings worsens output coherence.

Simple contiguous cache strategies outperform complex eviction methods.

Abstract

The Key-Value (KV) cache is integral to efficient autoregressive inference in large language models (LLMs), yet its unbounded growth in stateful multi-turn scenarios presents major challenges. This paper examines the interplay between KV cache management strategies, the architectural context limits of models like meta-llama/Meta-Llama-3-8b-instruct, and the often-overlooked integrity of positional encodings. Through empirical analysis using a stateful benchmarking framework, we show that LLM generation quality degrades sharply when the accumulated KV cache approaches or exceeds the model's trained context window (e.g., 8192 tokens for Llama 3), a failure mode distinct from GPU memory exhaustion. Common eviction strategies, even high-retention ones (e.g., 99% via AttentionTop), can worsen performance if they disrupt positional coherence. Because LLMs rely on consistent positional signals (e.g., RoPE), compacting a cache by removing non-contiguous tokens can scramble these signals and lead to degenerative outputs. We further show that simple strategies preserving contiguous context blocks (e.g., keeping an initial "gist") can yield more coherent generations than complex or positionally disruptive ones. We advocate for eviction techniques that respect architectural limits, preserve positional structure, and view "cache health" holistically beyond mere size.