nano-KvLLM: Integrating KV Cache Compression into nano-vLLM for Long-Context Inference

Hi everyone, I recently built nano-KvLLM, an easy-to-use lightweight inference framework based on nano-vLLM for efficient KV-cache management in LLM serving.

Github: https://github.com/TheToughCrane/nano-kvllm

A key goal of this framework is to preserve the original nano-vLLM code layout as much as possible, with only simple and minimal modifications, so that users can more easily learn from the codebase and develop their own extensions on top of it.

Right now, nano-KvLLM already supports KV-cache compression in the nano-vLLM execution pipeline. Users can quickly plug in and test their own compression methods, or build on top of the built-in support.

The project also includes a simple multi-turn chat demo KvChat with real-time KV-cache compression, currently based on Qwen3.

I hope nano-KvLLM can be useful for people who want to:

• learn the core ideas behind vLLM, and understand how KV-cache compression can be integrated into a real inference framework
• prototype their own inference or memory-management methods
• build and deploy personal LLM applications more easily

In the coming weeks, nano-KvLLM will continue expanding toward a more complete KV-cache management stack for LLM serving, including:

• KV-cache offloading
• KV-cache retrieval

I’ll keep working on this project over time, and sincerely hope it can be helpful to anyone exploring LLM inference. Thanks for your time.