KV Cache Compression in Practice: Performance Comparison Between RKV and ChunkKV for Long-Context Reasoning

Addressing the KV cache memory bottleneck in long-context scenarios of large language models (LLMs), this article compares two compression techniques: RKV and ChunkKV. Key findings: ChunkKV's accuracy at a 10% aggressive cache budget is almost twice that of RKV; task types affect compression tolerance (summarization is robust, QA is sensitive); compression mainly extends context length rather than accelerating inference.

When modern LLMs process long documents, codebase analysis, or multi-turn dialogues, the memory usage of KV cache often exceeds model parameters (e.g., Qwen2.5-1.5B-Instruct occupies several GB or even over ten GB of memory when handling tens of thousands of tokens), limiting single-card sequence length, increasing inference latency, and raising deployment costs. Traditional solutions (model quantization, gradient checkpointing) sacrifice accuracy or add overhead, while KV cache compression reduces memory by selectively retaining key information.

RKV: Dynamically eliminates low-score tokens based on attention scores, adapts to input but may lose globally important tokens and increases computational overhead. ChunkKV: Splits context into continuous semantic chunks, retains complete chunks to maintain semantic continuity, avoids information fragmentation, and preserves more effective patterns at the same compression ratio.

In the LongBench benchmark (including 6 task types like NarrativeQA narrative understanding, Qasper academic QA, MultiFieldQA multi-domain QA), cache budget levels of 100% (baseline), 50%, 20%, and 10% were set, and Qwen2.5-1.5B-Instruct (bfloat16 precision) was used to evaluate compression performance degradation.

1. ChunkKV's Advantage in Aggressive Compression: At a 10% budget, the macro-average accuracy is twice that of RKV, as retaining continuous semantic chunks avoids context fragmentation.
2. Task Sensitivity: Summarization tasks (GovReport) maintain 77%-86% of baseline performance even at a 10% budget; few QA tasks retain over 40% performance at a 50% budget.
3. Compression and Latency: Compression does not reduce latency but instead increases overhead, as the computation of compression algorithms and non-continuous memory access offset memory benefits; its main value is extending context length.

Practical Recommendations:

1. Task-aware configuration: Use 10%-20% budget for summarization, keep over 50% for QA;
2. Prioritize ChunkKV for aggressive compression;
3. Clarify that the goal of compression is to extend context rather than accelerate;
4. Implement adaptive strategies to dynamically adjust cache.

Future Directions: Explore smarter semantic chunk segmentation, hybrid strategies of RKV and ChunkKV, and domain-specific compression schemes.