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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...
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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