KV Cache and LMCache for Faster Coding Agents

I explain how KV caching reduces repeated prompt processing in long-running AI agent sessions, and compare vLLM’s built-in caching options with LMCache. The comparisons cover single-document queries, large document sets, server restarts, and coding-agent workloads across several hardware and model configurations.

- KV cache basics and the differences between no cache, GPU prefix cache, CPU offload, and LMCache
- Tests involving cache capacity, concurrent requests, shuffled documents, and recovery after restarts
- Coding-agent performance, trade-offs, and guidance on when built-in caching or LMCache may be appropriate

For developers running local models and coding agents, this offers practical context for choosing a caching setup. You’ll learn which options to try based on GPU memory, system RAM, and whether cache persistence across restarts matters.