nanoinfer: An Educational Practice of Building an LLM Inference Engine from Scratch

nanoinfer is a lightweight LLM inference engine designed specifically for learning purposes. Its core goal is to help developers understand the mechanisms of LLM inference through implementation from scratch. Its golden rule is to never call model.generate() or HF generation helper functions—forward propagation and generation loops are fully handwritten, using HF only for downloading weights, tokenization, and reading configurations. This project supports the Llama series and Qwen2.5 models, helping developers move from "being able to use" LLMs to "truly understanding" their underlying logic.

Original Author & Source

• Original Author/Maintainer: AustinJiangg
• Source Platform: GitHub
• Original Title: nanoinfer: A from-scratch LLM inference engine, built for learning
• Original Link: https://github.com/AustinJiangg/nanoinfer
• Update Time: 2026-06-14T10:15:52Z

Project Positioning

nanoinfer is an educational open-source project. Unlike projects that rely on mature frameworks, it aims to help developers master the internal mechanisms of LLM inference through implementation from scratch. The project structure consists of three parts: cpp/ (high-performance C++ implementation), nanoinfer/ (core Python engine), and tests/ (test cases).

Design Philosophy

Following the Llama family architecture, it adopts a dual-language implementation of Python and C++, providing a clear learning path: first understand the essence of inference, then gradually add optimization techniques.

Supported Models

Currently, it supports mainstream open-source models:

• Llama series (open-sourced by Meta)
• Qwen2.5 (Alibaba Tongyi Qianwen series) These supports allow developers to run popular LLMs while controlling the details of inference.

Handwritten Forward Propagation

Implemented manually layer by layer:

• Embedding lookup
• Positional encoding calculation
• Multi-head attention mechanism
• Feed-forward neural network
• Layer normalization
• Residual connections

Autonomous Generation Loop

The greedy decoding loop is fully implemented independently, with visible:

• Token-by-token generation process
• KV cache construction
• Attention weight calculation and application
• Sampling strategy selection logic

Value for AI Engineers

• Clear code structure, no framework black-box effect
• Full visualization of the inference process
• Modifiable experimental environment

Future Optimization Roadmap

• KV cache optimization: reduce redundant computation and improve long-sequence efficiency
• Continuous batching: increase throughput
• Paged attention: memory-efficient technology used by vLLM Optimizations will be implemented step by step in a teaching-friendly way to help understand the principles.

nanoinfer is suitable for the following scenarios:

• Teaching Demos: Show LLM inference principles in classes/workshops
• Research Experiments: Verify new attention mechanisms or sampling strategies
• Performance Benchmarks: Serve as a minimal baseline to compare with other engines
• Embedded Deployment: Understand LLM operation in resource-constrained environments It is recommended for developers who want to dive deep into the underlayers of LLMs to use this project—by implementing components with their own hands, they can build deep intuition.

nanoinfer represents an important direction for AI educational tools: exposing underlayer implementations instead of encapsulating APIs, allowing learners to build true understanding by reading and modifying code. As LLMs are widely applied, understanding inference mechanisms becomes increasingly important. nanoinfer provides valuable practical resources for AI education, helping developers move from "being able to use" to "truly understanding" LLMs.