Show HN: NanoEuler – GPT-2 scale model in pure C/CUDA from scratch
One engineer just built a GPT-2 scale model from scratch in pure C/CUDA—no frameworks, no abstraction layers. Here's why that matters.
Why it matters
Open-source deep-dive into LLM fundamentals: a ground-up implementation in C/CUDA demonstrates how parameter-to-performance correlation works at the hardware level, valuable for engineers who want to understand (not just use) how models train and optimize on GPUs.
The key facts
14 to knowNanoEuler: GPT-2 scale model implemented in pure C/CUDA
No intermediary framework—direct model-to-GPU interface
Trained on Shakespeare.txt starting point
23 million parameter checkpoint demonstrated semantic understanding (e.g., line-start pattern recognition)
Author motivation: low-level infrastructure understanding + GPU optimization insights
Includes SFT (supervised fine-tuning) pipeline
Published on GitHub as open-source project
Hacker News traction: 11 points, 2 comments (modest uptake)
23 million parameter model trained on Shakespeare.txt
Pure C/CUDA implementation with no intermediary frameworks
Focus on understanding GPU optimization and parameter-data correlation
Includes SFT (supervised fine-tuning) for chatbot capability
Open-source GitHub project (nanoeuler)
Hacker News Show HN post with 11 points
Go to the source
Hacker Newsgithub.com
Publisher excerpt: Hi everyone, I started working on nanoeuler after the ban of anthropic's fable because my ambition and dream is to work in the AI field in anthropic. The two interesting reasons that led me to create nanoeuler were (1) interfacing with llm does not mean understanding how they are composed and (2),…