Recency/Frequency Adaptive KV Caching for Large Language Model Serving
Key-value (KV) caching is a powerful technique for accelerating large language model inference and generation. Inference workloads are large and diverse, which makes them difficult to cache effectively. Existing cache management strategies adopt the least-recently-used policy for evicting cache blocks. However, LRU leads to multiple unrelated workloads flushing each other's caches. To address this, we integrate adaptive caching that dynamically allocates cache space between recently and frequently occurring KV blocks. Evaluations show that it improves the KV cache hit rate by up to 10.8% and reduces time to first token by up to 12.6% over naive vLLM on synthetic document question answerin...
| Authors | Yang Shen, Meghana Madhyastha, Robert Underwood et al. |
|---|---|
| Primary category | cs.DC |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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