PEEK: Predictive Queue-Informed KV Cache Management for LLM Serving

📰 ArXiv cs.AI

Learn how PEEK, a predictive queue-informed KV cache management system, improves LLM serving performance by efficiently scheduling and evicting cache entries

advanced Published 7 Jul 2026

Action Steps

Build a radix tree over the pending queue to identify prefix-sharing clusters
Implement a dual-walk algorithm to match the tree against the engine's prefix cache
Admit clusters to the cache based on longest-prefix-match
Configure PEEK for online or offline LLM serving regimes
Test PEEK's performance using metrics such as latency and throughput

Who Needs to Know This

Machine learning engineers and researchers working on large language models (LLMs) can benefit from PEEK to optimize their model serving performance and reduce latency

Key Insight

💡 PEEK's incremental radix tree and dual-walk algorithm enable efficient scheduling and eviction of cache entries, reducing latency and improving overall performance

Key Takeaways

Learn how PEEK, a predictive queue-informed KV cache management system, improves LLM serving performance by efficiently scheduling and evicting cache entries

Full Article

Title: PEEK: Predictive Queue-Informed KV Cache Management for LLM Serving

Abstract:
arXiv:2607.02525v1 Announce Type: cross Abstract: We present PEEK, a lightweight scheduling and eviction framework for both online (streaming) and offline (batch) LLM serving; this paper focuses on the online regime. PEEK maintains an incremental radix tree over the pending queue, exposing prefix-sharing clusters no existing engine surfaces. A low-overhead dual-walk matches the tree against the engine's prefix cache to yield longest-prefix-match for every waiting request; PEEK then admits cluste

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