The article describes the implementation of the DeepSeek R1-zero style training for large language models (LLMs) using a single or multiple GPUs, with a focus on simplicity and efficiency. It highlights the capabilities of the nanoAhaMoment project, which includes full parameter tuning, multi-GPU support, and a full evaluation suite, while maintaining competitive performance with minimal complexity. The repository offers interactive Jupyter notebooks and scripts for training, complete with installation instructions and dependency management.