chanjoongx/microgpt-efficiency
"Everything else is just for efficiency." — Karpathy's microgpt benchmarked across scalar autograd, NumPy, and PyTorch (RTX 5080)
This project helps machine learning engineers understand the real-world performance differences when building small Generative Pre-trained Transformers (GPTs). It takes a basic GPT algorithm and runs it using different underlying computational methods, showing how much faster your model trains. You input the same GPT architecture and training data, and it outputs precise measurements of how much faster each method runs compared to the simplest version.
Use this if you are a machine learning engineer or researcher trying to optimize the training speed of small language models and want to understand the performance impact of choosing different numerical computation libraries or hardware.
Not ideal if you are looking for a pre-built, production-ready GPT model or if you are not interested in the low-level efficiency differences between mathematical computation backends.
Stars
11
Forks
2
Language
Python
License
MIT
Category
Last pushed
Feb 21, 2026
Commits (30d)
0
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