How to Make Clean, Maintainable Modifications to vLLM Using the Plugin System: A Practical Guide to Avoiding Forks and Monkey Patches

In the field of Large Language Model (LLM) inference, vLLM has emerged as the preferred engine for developers and enterprises alike, thanks to its high throughput and low latency. It supports core features such as continuous batching, efficient scheduling, and paged attention, seamlessly handling deployments ranging from small-scale models to large frontier systems. However, as business use cases deepen, many teams face a common challenge: how to customize vLLM’s internal behavior without disrupting its original architecture. You might want to adjust scheduling logic, optimize KV-cache handling, or integrate proprietary optimization solutions—these needs may seem straightforward, but they often hide pitfalls. …