This content originally appeared on DEV Community and was authored by LumGenLab
Ever wondered what it takes to build a transformer from absolute scratch? No PyTorch, no TensorFlow, just raw C++ and mathematical determination.
The Challenge
Most GPT implementations today rely on heavyweight frameworks that abstract away the core mechanics. I wanted to understand every matrix multiplication, every gradient calculation, and every optimization step. So I built LumGPT – a complete GPT implementation in pure C++.
The Constraints
My hardware isn’t exactly cutting edge:
- AMD Phenom Triple-Core @ 2.4GHz (2008 era)
- 2GB DDR2 RAM with only 700MB free
- No GPU (GTX 210 doesn’t count)
- Regular HDD storage
The question was: can you train a transformer on hardware that predates the transformer paper?
What I Built
LumGPT includes everything you’d expect from a production transformer:
- Multi-head attention with causal masking
- Layer normalization (pre-LN like GPT-2)
- Feed-forward networks with GELU activation
- AdamW optimizer with weight decay
- Advanced sampling (temperature + top-k)
- Custom tensor operations optimized for cache efficiency
The Results
Memory footprint: 32MB
CPU usage: 45%
Training time: 8 minutes per 200 iterations
Loss progression:
Step 0: 4.5875
Step 200: 3.1597
Step 2000: 3.2377
The model converged reasonably well on TinyShakespeare, but here’s where it gets interesting.
The Dataset Experiment
TinyShakespeare has 40,000 lines but only 65 unique characters. I tried something different: a custom dataset of 202 modern jokes (Nasiruddin collection) with only 3,000 lines but 82 unique characters.
The smaller dataset with richer vocabulary actually showed better learning characteristics. Sometimes data quality beats quantity.
Technical Deep Dive
Memory Management
Every tensor operation is optimized for cache locality. No dynamic allocations during training loops. Thread-local RNG for reproducibility without global state.
Mathematical Precision
All gradients computed from first principles. Layer norm backward pass implements the full mathematical derivation, not approximations. Combined softmax-cross entropy gradients for numerical stability.
The Attention Implementation
// Scaled dot-product with causal masking
matmul(Q_head, K_head_T, scores);
double scale = 1.0 / sqrt(d_k);
// Apply causal mask
for (size_t i = 0; i < seq_len; ++i) {
for (size_t j = i + 1; j < seq_len; ++j) {
scores.data[i * seq_len + j] = NEG_INF;
}
}
No shortcuts. Every operation follows the mathematical definitions exactly.
What’s Next
This is just version 1. The next iteration will include:
- 4-bit quantization with QAT
- RoPE positional embeddings
- ALiBi attention bias
- Eigen 3.4.0 integration
- Custom inference optimizations
Why This Matters
Framework abstractions are useful, but they can hide fundamental understanding. Building from scratch taught me why certain architectural choices matter, how gradients actually flow through transformers, and where the computational bottlenecks really are.
Plus, proving that meaningful AI can run on decade-old hardware opens possibilities for edge deployment and democratized access.
The Code
The complete implementation is open source on GitHub. It’s production-grade code, not just an educational exercise.
Building your own transformer is challenging but incredibly rewarding. You gain intuition that no amount of framework usage can provide.
What’s your experience with implementing models from scratch? Have you tried building transformers without frameworks?
This content originally appeared on DEV Community and was authored by LumGenLab