Building a Tiny Large Language Model from Scratch: A Complete Practice on a Single RTX 3090

This project implements the full lifecycle of a tiny Large Language Model (LLM) on a single NVIDIA RTX 3090 graphics card, covering key technologies such as model architecture design, data preprocessing, training loop, custom CUDA kernel development, and inference optimization. The core goal of the project is to prove that under limited consumer-grade hardware resources, it is still possible to deeply understand the details of the Transformer architecture and gain practical engineering experience.

Training and deployment of LLMs usually require massive computing resources, which deter many researchers. Based on Sebastian Raschka's classic work, this project completes the entire process of tiny LLM from design to inference on an RTX 3090 (24GB VRAM), aiming to break the barrier of resource constraints and help developers master the underlying principles and engineering practices of LLMs.

A complete yet streamlined Transformer decoder architecture is adopted, with core components including: Rotary Position Encoding (RoPE), multi-head self-attention (with causal masking), SwiGLU feed-forward network, and RMSNorm layer normalization. The optimal hyperparameter combination is determined through experiments to balance model size and hardware capabilities, ensuring smooth training and meaningful results generation on the RTX 3090.

Data aspect: Filter and clean open text datasets, use Byte Pair Encoding (BPE) to build an optimized vocabulary; Training strategies: Mixed Precision Training (AMP), gradient accumulation, warm-up + cosine annealing learning rate scheduling, regular checkpoint management; VRAM optimization: Gradient checkpointing, activation recomputation, 8-bit Adam optimizer for state compression.

Going beyond PyTorch abstraction, custom CUDA kernels are written: Fused attention kernels reduce memory bandwidth pressure and launch overhead; INT8 quantization kernels (including scaling factor calculation and dequantization logic) halve memory usage and improve throughput; Optimize tensor memory layout to increase cache hit rate and coalesced access efficiency.

Inference phase optimization strategies: KV cache management reduces redundant computation; Dynamic batching improves GPU utilization; Explore speculative decoding to accelerate the autoregressive generation process, enabling the trained model to run efficiently in practical applications.

Through this project, you can gain: Intuitive understanding of the Transformer architecture, large-scale model training skills (such as VRAM management), performance optimization capabilities at the algorithm and system levels, and understanding of the essence of GPU computing. Core insight: Resource constraints should not be an obstacle to learning and innovation; consumer-grade hardware can be used to carry out meaningful LLM research and development.

Building an LLM from scratch is the best way to understand this technology, and this project provides a feasible roadmap for developers. With the advancement of hardware and the evolution of optimization methods, it is believed that more and more developers can carry out LLM innovation on personal devices, break resource barriers, and promote the popularization of the technology.