I audited Stanford's CS336 and built an LLM from scratch for $353 (andytimm.github.io)

In December, a participant completed Stanford’s CS336: Language Models From Scratch, which emphasizes the importance of hands-on experience in building large language models (LLMs). The course is designed to bridge the gap between theory and practical application, helping researchers and engineers deepen their understanding of LLM architectures and the intricacies of their implementations. Assignments require students to construct a tokenizer, build a basic Transformer, implement Flash Attention, and tackle data cleaning, among other tasks. This rigorous approach aims to equip participants with essential skills and insights for pursuing meaningful research and innovation in AI. The significance of CS336 for the AI/ML community lies in its focus on foundational knowledge, promoting a deeper comprehension of the underlying technologies that power modern LLMs. By engaging in practical implementations, students learn about critical system limitations and trade-offs related to GPU resource management, model scaling, and efficient training strategies. The course’s high expectations and collaborative learning environment foster a more hands-on approach to tackling design challenges, emphasizing the necessity for technical expertise in an era of growing complexity in AI models. The auditing experience was manageable, costing approximately $353, reflecting the evolving landscape of AI education that balances theoretical frameworks with essential practical applications.