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RaBitQCache: Rotated Binary Quantization Enhances KVCache for Long-Context LLMs
RaBitQCache introduces a sparse attention framework using randomized rotated binary quantization to improve Key-Value cache efficiency in long-context large language model inference. This approach addresses limitations of existing methods that rely on fixed-budget retrieval or costly proxy scores.
Efficient KV cache management is critical for scaling LLMs to longer contexts without prohibitive computational costs. RaBitQCache's method could enable more scalable and cost-effective long-context LLM inference.
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Score 86
Source Type arxiv
Reposts 0
Topic Quality 65
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