Building LLM Core Systems from Scratch: A Multilingual Deep Learning Practice Project

Project Basic Information

• Project Name: llm-systems-from-scratch
• Original Author/Maintainer: jayemscript
• Source Platform: GitHub
• Project Link: https://github.com/jayemscript/llm-systems-from-scratch
• Release/Update Date: 2026-06-01

Core Content

This open-source project focuses on educational purposes, teaching step-by-step how to build the core systems of large language models (LLMs) from scratch. It implements core logic using C++ and Rust, and provides Python/JavaScript bindings. It covers key components such as tensor operations, automatic differentiation, neural networks, tokenizers, and a minimal Transformer pipeline, helping developers understand the underlying working principles of LLMs.

Large language models have become a hot technology in the AI field, but most developers still lack an in-depth understanding of their internal principles. Although there are many open-source models available for direct use, few developers master the underlying construction logic, leading to:

1. Difficulty in deep optimization when using models;
2. Difficulty in debugging when models have issues;
3. Lack of a clear learning path for beginners in AI system development.

This project aims to fill this knowledge gap. As an educational practice tutorial, it helps developers understand the core components of LLMs from scratch, rather than pursuing production environment performance.

The project adopts a multilingual implementation strategy:

• Core Computing Logic: Written in C++ to pursue maximum execution efficiency;
• Memory-Safe Implementation: Provides a Rust version to demonstrate the memory safety features of modern system languages;
• Multi-Ecosystem Support: Supports Python and JavaScript through binding layers, making it easy for developers from different backgrounds to access.

This design reflects the trend of modern AI system development: core performance code is implemented in low-level languages, and upper-layer interfaces are open to a wide developer ecosystem.

The project covers the implementation of core LLM components:

1. Tensor Operation System: Implements basic operations such as addition, multiplication, and matrix operations, helping to understand underlying concepts like memory layout, broadcasting mechanism, and gradient propagation;
2. Automatic Differentiation Engine: Supports dynamic computation graphs, allowing runtime dynamic adjustment of graph structures, suitable for research and educational scenarios;
3. Neural Network Layers: Implements fully connected layers, activation function layers, normalization layers, etc., demonstrating the specific implementation of forward/backward propagation;
4. Tokenizer: Implements the basic Byte Pair Encoding (BPE) algorithm, helping to understand the process of converting text to numerical values;
5. Minimal Transformer Pipeline: Integrates all components, demonstrating core mechanisms such as self-attention, positional encoding, and multi-head attention.

Learning Value

Provides a step-by-step learning path for developers who want to deeply understand LLMs, helping them master the underlying principles.

Practical Suggestions

Recommended learning sequence:

1. Master tensor data structures and basic operations;
2. Learn automatic differentiation principles and the application of the chain rule;
3. Implement basic neural network layers and understand forward/backward propagation;
4. Learn tokenization algorithms and master the process of converting text to numerical values;
5. Integrate components to implement a complete Transformer inference process.

At each stage, you can compare with PyTorch/TensorFlow implementations to deepen your understanding.

Reasons why the project chose C++ and Rust as core languages:

• C++: Provides fine-grained hardware control and extremely high execution efficiency, making it the first choice for production-level deep learning frameworks;
• Rust: Ensures memory safety while having performance close to C++, representing the development direction of system programming languages.

The existence of Python/JavaScript bindings reflects pragmatism, allowing developers from different backgrounds to learn and experiment in familiar ways.

Limitations

As an educational project, it does not pursue production-level performance and is not suitable for direct use in large-scale model training or production deployment.

Outlook

Future expansion directions:

• Add CUDA support to demonstrate GPU parallel computing;
• Implement distributed training to demonstrate the challenges of large-scale model training.

Understanding the underlying principles is crucial for solving problems using production-level frameworks such as PyTorch/TensorFlow.

llm-systems-from-scratch fills the gap between "using LLMs" and "understanding LLMs", providing a solid starting point for developers who want to master large language models at the principle level. In today's rapidly developing AI technology, the ability to deeply understand underlying principles will become increasingly important.