KV Cache Compression 900000x Beyond TurboQuant and Per-Vector Shannon Limit
This paper introduces a novel KV cache compression method that achieves 900,000x compression ratio, surpassing TurboQuant and approaching the theoretical per-vector Shannon limit. The technique enables efficient deployment of large language models with minimal memory overhead while maintaining high accuracy.