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

Pratik Poudel

TL;DR

This work investigates stateful KV cache management for long-context LLMs, showing that generation quality deteriorates when the cache nears the model's architectural context limit due to positional encoding sensitivity. It reveals that eviction strategies must respect positional structure; simple contiguous gist retention can outperform high-retention, non-contiguous pruning that disrupts RoPE-based signals. Using Meta-Llama-3-8b-instruct in extended multi-turn benchmarks, the study demonstrates that positional integrity and proximity to context limits are critical for coherent outputs. The findings advocate for cache eviction approaches that preserve contiguous context blocks and align with architectural constraints, offering practical guidance for truly long, coherent interactions.

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.

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

TL;DR

This work investigates stateful KV cache management for long-context LLMs, showing that generation quality deteriorates when the cache nears the model's architectural context limit due to positional encoding sensitivity. It reveals that eviction strategies must respect positional structure; simple contiguous gist retention can outperform high-retention, non-contiguous pruning that disrupts RoPE-based signals. Using Meta-Llama-3-8b-instruct in extended multi-turn benchmarks, the study demonstrates that positional integrity and proximity to context limits are critical for coherent outputs. The findings advocate for cache eviction approaches that preserve contiguous context blocks and align with architectural constraints, offering practical guidance for truly long, coherent interactions.

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.

Paper Structure

This paper contains 25 sections, 2 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Background
  3. KV Caching in Autoregressive LLMs
  4. The Context Window Limit and Positional Encodings
  5. The Challenge of Stateful KV Cache Eviction in Extended Conversation
  6. Related Work
  7. Enhancing Model Capabilities for Extended Contexts
  8. Architectural Innovations for Sequence Processing
  9. Advancements in Positional Encodings and Context Window Extension
  10. Reducing the KV Cache Memory Footprint
  11. Cache-Focused Architectural Modifications
  12. KV Cache Quantization
  13. Dynamic Management of the KV Cache via Eviction and Prioritization
  14. Token Pruning and Eviction Strategies
  15. Hierarchical Storage Solutions
  16. ...and 10 more sections

Figures (2)

  • Figure 1: Performance Metrics Comparison (% Change from Baseline) with KV cache management strategies. Negative percentage means lower values for the metric compared to baseline.
  • Figure 2: KV Cache Growth per Turn for a conversation with 50+ turn reported only till turn 10(During end of Generation Phase). The cache eviction threshold is 600MB . Note how AttentionTop and EvictOldest operate above this threshold after Turn 3, reflecting complex intra-turn dynamics.