arXiv cs.AIPaper
DCKV: Learned Transform Coding of the Prompt KV Cache in Hybrid Language Models
KV cache compression is a real scaling lever for deployed models. Getting 55x compression with under 2 points loss means longer contexts become economical on constrained hardware. The trick is conditioning compression on prompt tokens and neighboring positions, not treating the cache as generic data. For builders: this directly reduces serving costs for long-context applications. For infrastructure teams: this is a production-ready technique worth implementing.