Nano-vLLM: How a vLLM-style inference engine works
Architecture, Scheduling, and the Path from Prompt to Token When deploying large language models in production, the inference engine becomes a critical piece of infrastructure. Every LLM API you use — OpenAI, Claude, DeepSeek — is sitting on top of an inference engine like this. While most developers interact with LLMs through high-level APIs, understanding what happens beneath the surface—how prompts are processed, how requests are batched, and how GPU resources are managed—can significantly impact system design decisions. This two-part series explores these internals through Nano-vLLM, a minimal (~1,200 lines of Python) yet production-grade implementation that distills the core ideas behind vLLM, one of the most widely adopted open-source inference engines. Nano-vLLM was…
