SentenceKV introduces a novel approach that reduces memory usage and improves inference speed by organizing key-value caches at the sentence level rather than token level.

• Achieves up to 1.7x speedup over traditional caching methods
• Reduces memory consumption while maintaining model quality
• Leverages sentence-level semantic patterns to intelligently cache information
• Compatible with existing KV caching optimization techniques

This innovation addresses critical engineering challenges for deploying LLMs with long contexts, making large language models more practical and cost-effective for real-world applications.

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