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arXiv Machine Learning By Joseph Kanichai, Tiziano De Matteis, Animesh Trivedi

Building py-kvcache: A Performance Characterization of External KV Caching for vLLM with NVMe SSDs

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The paper presents py‑kvcache, a new KV offload connector for vLLM that uses asynchronous direct I/O, bounded shared staging, and scheduler‑aware preloading to improve external KV caching performance on NVMe SSDs. Experiments across synthetic workloads, long‑context benchmarks, and production traces show that py‑kvcache can load 80k‑token prefixes 2.0× faster than LMCache, with preloading contributing an additional 1.34× speedup, and achieves overall performance within 4% of native vLLM KV Offload. The study highlights that cache effectiveness depends on transfer granularity, intermediate memory use, and scheduling timing rather than just device bandwidth, indicating that external KV caching should be considered a setup‑specific admission decision.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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