h2o cache

**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.