Show HN: NanoEuler – GPT-2 scale model in pure C/CUDA from scratch
Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.
A from-scratch GPT-2-scale training/inference implementation in pure C and CUDA with no PyTorch or framework dependencies—essentially a single-file C codebase plus CUDA kernels you can read end to end. It's a learning and auditing reference, not a production stack: useful if you want to understand exactly what every kernel and gradient does without a black-box runtime, but expect to bring your own scaling, tooling, and optimization work before it touches your serving path.
A GPT-2-scale model implementation is being built from scratch in pure C/CUDA, with the repo exposing source, CUDA code, Makefile, sample data, and a training log rather than relying on PyTorch or higher-level runtimes. For production LLM teams, the practical value is as a minimal, inspectable reference for kernels, memory layout, and training mechanics; it is not a drop-in serving stack, but it can help debug performance assumptions below the framework layer.