This research critically evaluates Key-Value cache compression techniques for Large Language Models from a practical implementation perspective.

• Analyzes mainstream KV cache compression solutions with focus on real-world application efficiency
• Identifies key implementation challenges that prevent widespread adoption in production
• Provides engineering insights to reduce memory consumption while maintaining performance
• Offers practical recommendations for optimizing LLM serving systems

This work matters for Engineering teams because it bridges the gap between theoretical compression algorithms and their practical deployment, potentially enabling more efficient and cost-effective LLM serving at scale.

Rethinking Key-Value Cache Compression Techniques for Large Language Model Serving