Excited to introduce Puro-2B, along with its fully open recipe that lets you train a Qwen2-1.5B-level LLM on RTX 5090 GPUs for less than $5090! As someone who has always been curious about why LLMs work so well, one of the biggest challenges today is that we not only lack a good theory, but are also losing the ability to run controlled training experiments at scale, systematically vary key factors, and gather the empirical evidence needed to understand how LLMs learn from data. Training LLMs has simply become too expensive. There are now a few great open or almost open training recipes for models with billions of parameters, but reproducing them can still cost hundreds of thousands of dollars. As this makes many basic questions about LLMs inaccessible to academic labs, we really hope to bring this cost down far enough that academic labs can train and study genuinely capable models from scratch. Puro is a full-stack engineering effort to reduce training cost, which mixes engineering and research across data, optimization, numerical precision, hardware, and training strategy. It also builds on some of our own previous research: 📈 Curriculum Model Averaging (CMA): data curriculum designs have to work together with moderate learning rate decay and model averaging (arxiv.org/abs/2511.18903) ◉ Hyperball / MuonH: an optimizer wrapper that constrains weight and update norms, and directly controls the effective (angular) step sizes for stable and efficient training (arxiv.org/abs/2606.16899) There is still a long way to go, and we would be very happy to hear your feedback and ideas!