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 cluster pioneers first so siblings inherit the freshly cached prefix, a co-designed eviction hook protects
The paper addresses critical challenges in efficiently serving large language models, particularly as demand for LLM inference scales across various applications.
Improved KV cache management directly impacts the cost, latency, and throughput of LLM inference, making advanced AI models more economically viable and accessible.
This advancement enables more efficient allocation of computational resources for LLM serving, allowing for higher query rates and potentially larger models on existing hardware.
- · Cloud AI service providers
- · Companies deploying LLM-powered applications
- · AI infrastructure developers
- · Inefficient LLM serving systems
- · Companies with high LLM inference costs
Reduced operational costs for large-scale language model deployment.
Accelerated adoption and integration of sophisticated AI models into diverse products and services.
Increased competition among AI service providers due to more accessible and cheaper inference capabilities.
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Read at arXiv cs.AI