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SeKV: Adaptive KV Cache for Efficient Long-Context LLM Inference

SeKV introduces a resolution-adaptive KV cache with hierarchical semantic memory to address the memory bottleneck in long-context large language model inference. This approach aims to reduce GPU memory usage while preserving context fidelity better than existing compression or token eviction method...

Topic: LLM Inference Source: arXiv · arxiv.org Published 2026-06-30 05:18 UTC Fetched 2026-07-01 01:19 UTC

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As LLMs handle longer contexts, KV cache size grows linearly, making full GPU caching costly and inefficient. SeKV's method offers a more balanced solution for memory efficiency and context preservation, enabling more practical long-context inference.

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Score 80 Source Type arxiv Reposts 0 Topic Quality 65

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