EEmicroGPT: 19,000× faster microgpt training on a laptop CPU (loss vs. time)
11 hours ago
- #Transformer Models
- #Hardware Efficiency
- #AI Optimization
- EEmicroGPT is a highly optimized, single-file, dependency-free C implementation of GPT training, designed for Apple Silicon.
- It achieves up to 19,000x faster training per sample compared to Python implementations by focusing on hardware efficiency.
- Key optimizations include SIMD vectorization, register usage, skipping unnecessary computations (like padding), and leveraging Apple's SME2 for matrix operations.
- The project demonstrates that understanding hardware and computational overhead can outperform raw compute power, especially for small models.
- EEmicroGPT's performance highlights the 'killer microsecond' problem in GPUs, where kernel launch overhead dominates small workloads.
- The implementation includes optimizations like fast approximate math functions (e.g., exp), batch processing, and cache-aware data layouts.
- It serves as a case study in the trade-offs between model capacity, batch size, and training speed, showing how faster iteration enables better hyperparameter tuning.
- The project connects small-scale optimizations to large-scale AI training, emphasizing the importance of efficiency in the AI revolution.