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

MarkTechPost / 4/26/2026

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Key Points

The article presents a tutorial on kvcached, a dynamic KV-cache system built on top of vLLM, aimed at changing how LLM KV cache memory is allocated on GPUs.
It walks through setting up an inference environment and deploying lightweight Qwen2.5 models via an OpenAI-compatible API to mirror a real serving workflow.
It outlines controlled experiments to evaluate how dynamic KV-cache allocation can improve GPU memory efficiency for bursty LLM workloads.
The tutorial also focuses on how this approach can support multi-model GPU sharing by making KV cache usage more elastic rather than fixed.

In this tutorial, we explore kvcached, a dynamic KV-cache implementation on top of vLLM, to understand how dynamic KV-cache allocation transforms GPU memory usage for large language models. We begin by setting up the environment and deploying lightweight Qwen2.5 models through an OpenAI-compatible API, ensuring a realistic inference workflow. We then design controlled experiments where […]

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