Kara: Efficient Reasoning LLM Serving via Sliding-Window KV Cache Compression

Researchers have introduced Kara, a method designed to reduce the high memory and latency costs associated with the long chain-of-thought sequences produced by reasoning AI models. By implementing sliding-window key-value cache compression, the technique improves inference efficiency and increases the throughput of large language models during complex generation tasks.