A Coding Implementation on kvcached for Elastic KV Cache Memory, Bursty LLM Serving, and Multi-Model GPU Sharing

A tutorial shows how to use kvcached with vLLM to make KV-cache memory elastic during LLM serving.

It runs small Qwen2.5 models behind an OpenAI-compatible API and compares elastic vs. static KV-cache behavior under bursty traffic.

The tests measure GPU memory use and latency, then extend to a multi-model setup to watch memory shift across active inference jobs.

The setup highlights how dynamic KV-cache allocation can reduce wasted VRAM and improve single-GPU efficiency for multiple models.