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arXiv · 2606.14779

Unified KV Pooling to Accelerate Long-Context LLM Serving

Minchul Kang·Changyong Shin·Jinwoo Jeong·Jaerim Park·Woohyun Kim·Bonyul Gu·Dongwoo Kang·Gyeongsik Yang·Chuck Yoo
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Abstract

Long-context LLM serving requires offloading KV caches to host-memory and SSDs, but existing mechanisms are not designed for such long contexts. We observe significant inefficiencies in current KV caching in long contexts: high serving latency ~30.7 s, exceeding the typical TTFT requirement of 10 s by more than 3x. Our in-depth analysis explains two major reasons: (1) retrieval is serialized through host-memory and SSD, leaving other host-memory modules and SSDs underutilized, and (2) SSD-based KV retrieval spends 84% of its time in the kernel filesystem rather than actual device access. To address the problems, we propose unified KV pooling, which aggregates multiple host-memory modules and SSDs into a single logical pool and distributes KV caches across devices based on their bandwidth. To eliminate the filesystem overhead, we design KV-passthrough, which bypasses the kernel filesystem and directly accesses SSD-resident KV caches from user space via SPDK. Across evaluations on LLaMA 3.1-8B, GPT-OSS-20B, and Qwen3-30B-A3B, unified KV pooling reduces TTFT in long-contexts ~4.1x over state-of-the-art techniques, all making under 10 s. It also reduces blocked I/O time by up to 23.2x by eliminating filesystem overhead.

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Minchul Kang, Changyong Shin, Jinwoo Jeong, Jaerim Park, Woohyun Kim, Bonyul Gu, Dongwoo Kang, Gyeongsik Yang, Chuck Yoo. 2026-06-10. Unified KV Pooling to Accelerate Long-Context LLM Serving. https://arxiv.org/abs/2606.14779

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