Like most, we’re chasing new solutions for optimizing tokenomics in AI deployments. We've recently been researching KV cache optimization strategies for AI-assisted coding. It's particularly hard to get high KV-cache reuse rates using standard prefix caching, because with each request, a coding agent needs to load code from one or more solution files into context,…
The result: smaller storage footprint, less network bandwidth, fewer GPUs per user, and HBM freed up for bigger models or longer contexts.