Building LLM Core Systems from Scratch: A Hands-On Learning Project Using C++ and Rust

This article analyzes the GitHub project llm-systems-from-scratch (by jayemscript), which implements core large language model components from scratch using C++ and Rust, covering tensor operations, automatic differentiation, neural networks, tokenizers, and a minimal Transformer pipeline. It provides developers with a practical path to deeply understand the underlying principles of LLMs, avoiding learning patterns that rely solely on high-level framework APIs.

With the widespread application of LLMs like ChatGPT and Claude, developers are eager to dive into their underlying principles. However, most existing resources stay at the theoretical level or high-level framework usage. This project emerged to address this gap, using system-level languages C++ and Rust to allow learners to touch low-level details such as tensor operations and backpropagation, and build a systematic understanding of LLM architecture.

The project uses a multi-language hybrid architecture:

• C++: Optimizes the tensor operation library using template metaprogramming and SIMD instructions;
• Rust: Provides memory-safe and efficient implementations via the ownership system and zero-cost abstractions;
• Python/JS Bindings: Supports high-level application calls using FFI or WASM technology; The modular structure facilitates independent compilation and testing, laying the foundation for extension and optimization.

Tensors are the foundation of deep learning. The project implements storage, indexing, and operations of multi-dimensional arrays from scratch, transparently showing details like memory layout and broadcasting mechanisms. The automatic differentiation implementation is based on backpropagation of computation graphs:

1. Forward propagation to build the computation graph;
2. Propagate gradients using the chain rule;
3. Update model parameters with gradients; This helps understand the complex computations behind PyTorch's backward() function.

The project implements core neural network components: fully connected layers, activation functions (ReLU/Sigmoid/Tanh), loss functions (mean squared error/cross-entropy), and optimizers (SGD/Adam), all with unit tests and benchmarks. The tokenizer uses the BPE algorithm from the GPT series: preprocessing → vocabulary building → encoding/decoding, fully demonstrating the conversion process from text to model input.

The project integrates all components to implement a minimal Transformer:

• Self-attention and multi-head attention mechanisms;
• Positional encoding to inject sequence position information;
• Feed-forward networks and layer normalization; Although smaller in scale than production models, it retains core ideas and can be used for small-scale language modeling experiments.

Practical Value: Build low-level intuition, improve debugging skills, guide performance optimization, and lay the foundation for innovation. Extension Directions: CUDA GPU acceleration, Flash Attention variants, model quantization and inference optimization. Learning Recommendations:

1. Read through the code structure to understand module division;
2. Start with tensor operations and implement/verify step by step;
3. Compare with frameworks like PyTorch to think about design trade-offs;
4. Try modifying and extending (e.g., new layer types or optimization algorithms).