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 created by a contributor to DeepSeek, whose name appears on the technical reports of models like DeepSeek-V3 and R1. Despite its minimal codebase, it implements the essential features that make vLLM production-ready: prefix caching, tensor parallelism, CUDA graph compilation, and torch compilation optimizations. Benchmarks show it achieving throughput comparable to—or even slightly exceeding—the full vLLM implementation. This makes it an ideal lens for understanding inference engine design without getting lost in the complexity of supporting dozens of model architectures and hardware backends.
In Part 1, we focus on the engineering architecture: how the system is organized, how requests flow through the pipeline, and how scheduling decisions are made. We will treat the actual model computation as a black box for now—Part 2 will open that box to explore attention mechanisms, KV cache internals, and tensor parallelism at the computation level.
The Main Flow: From Prompt to Output
The entry point to Nano-vLLM is straightforward: an LLM class with a generate method. You pass in an array of prompts and sampling parameters, and get back the generated text. But behind this simple interface lies a carefully designed pipeline that transforms text into tokens, schedules computation efficiently, and manages GPU resources.
From Prompts to Sequences
When generate is called, each prompt string goes through a tokenizer—a model-specific component that splits natural language into tokens, the fundamental units that LLMs process. Different model families (Qwen, LLaMA, DeepSeek) use different tokenizers, which is why a prompt of the same length may produce different token counts across models. The tokenizer converts each prompt into a sequence: an internal data structure representing a variable-length array of token IDs. This sequence becomes the core unit of work flowing through the rest of the system.
The Producer-Consumer Pattern
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