<ul><li>Efficient LLM inference can be achieved through SentenceKV, a novel sentence-level semantic KV caching approach.</li><li>SentenceKV addresses the limitations of traditional token-level caching methods by considering semantic relationships between tokens.</li><li>By compressing sentence representations into concise semantic vectors, stored on the GPU, SentenceKV reduces memory overhead and improves computational efficiency.</li><li>Extensive evaluations show that SentenceKV outperforms existing methods in terms of efficiency, memory usage, and model accuracy.</li></ul>

SentenceKV: Efficient LLM Inference via Sentence-Level Semantic KV Caching

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