H2O cache is the heavy-hitter-oriented KV cache strategy that retains tokens with highest contribution to attention while evicting lower-utility states under memory constraints - it aims to preserve model quality during aggressive cache pressure.

What Is H2O cache?

• Definition: Cache management method prioritizing high-impact tokens identified from attention behavior.
• Selection Principle: Keeps heavy-hitter tokens that are repeatedly attended across decode steps.
• Operational Goal: Improve eviction quality compared with simple least-recently-used heuristics.
• Deployment Context: Useful in long-context inference where full KV retention is infeasible.

Why H2O cache Matters

• Quality Retention: Preserving influential tokens reduces degradation from cache trimming.
• Memory Efficiency: Allows tighter KV budgets while maintaining answer coherence.
• Latency Benefits: Smaller active cache can improve decode speed under load.
• Scalability: Supports longer sessions and larger concurrency in fixed-memory environments.
• Policy Precision: Importance-aware eviction aligns resource use with model behavior.

How It Is Used in Practice

• Attention Statistics: Collect token-level influence scores during generation to guide retention.
• Hybrid Eviction Rules: Combine heavy-hitter preservation with recency windows for stability.
• A/B Evaluation: Compare perplexity, factuality, and latency against baseline eviction methods.

H2O cache is an advanced eviction strategy for constrained KV memory budgets - heavy-hitter-aware retention can improve long-context quality under tight resources.