arXiv Machine Learning By Haolin Tian, Yuzhe Liu, Tonghan Wang

Every Cache Entry Earns Its Place: Global Allocation of Resolution and Coverage for KV Cache Compression

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arXiv:2608. 07001v1 Announce Type: new Abstract: As large language models (LLMs) process increasingly long contexts, KV cache storage and repeated access have become a major bottleneck.

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arXiv Machine Learning
Sep 17

GroupKV: Hierarchical KV Cache Management for Long-Context Diffusion LLM Inference

GroupKV is a lightweight hierarchical KV cache management system designed for long‑context diffusion large language model (dLLM) inference. It partitions the context into contiguous groups and uses coarse‑to‑fine sparse selection, cross‑layer consistency for predictive prefetching, and a staleness correction mechanism to keep the cache coherent amid dynamic KV updates. The approach also incorporates streaming prefill to lower peak memory usage, achieving up to 48× longer serviceable context, 3.73× faster inference in offload‑based settings, and competitive task accuracy.

By Jinhao Wang, Zhexin Hu, Kangjie Zhou, Xin Zhou, Fangfang Liu