Why Stanford’s Free CS336 LLM Course Is the Ultimate Hands‑On AI Lab

Stanford offers the open‑source, free CS336 course titled “Language Modeling from Scratch,” aimed at teaching participants how to build a large language model from the ground up. The course follows a “minimal scaffolding” philosophy, requiring students to write all core components themselves, resulting in roughly ten times more code than typical AI courses.

The syllabus consists of 18 lectures covering tokenization, PyTorch basics, hyper‑parameter choices, Mixture‑of‑Experts (MoE), GPU/TPU parallelism, scaling laws, inference optimization, data handling, supervised fine‑tuning (SFT), reinforcement learning from human feedback (RLHF), and verifiable reward‑based RL. Recent material uses DeepSeek V3 as a concrete SOTA example.

There are five assignments, each intentionally challenging. Assignment 1 asks students to implement a BPE tokenizer, model architecture, and optimizer from scratch and train a minimal language model without using existing libraries. Assignment 2 moves to the system layer: students implement FlashAttention 2 in Triton (a GPU‑kernel language) and adapt the training code for multi‑GPU distributed execution. Assignments 3‑5 involve fitting scaling‑law formulas to small‑model results, cleaning and deduplicating Common Crawl data for pre‑training, and applying supervised fine‑tuning followed by RL to teach the model to solve math problems step‑by‑step.

The instructors are Tatsunori Hashimoto, an assistant professor in Stanford’s CS department, and Percy Liang, director of the Center for Research on Foundation Models (CRFM). Hashimoto has over 30 k citations, while Liang exceeds 100 k citations. Three teaching assistants support the homework.

Students are advised to disable AI code‑completion tools such as GitHub Copilot or Cursor Tab while working on the assignments to ensure deep understanding. They may consult ChatGPT for syntax or concept questions but must not let it solve the tasks, nor copy existing implementations.

All course materials—including videos, slides, readings, and homework—are publicly available. The Stanford CS336 GitHub organization hosts 22 repositories (https://github.com/stanford-cs336). Video recordings are on Stanford Online’s YouTube channel, with a complete 18‑lecture playlist for the Spring 2026 offering.

The only unavoidable cost is compute. Assignments that train models require GPUs; the course suggests first verifying correctness on CPU before moving to GPU. Cloud providers listed on the course site charge roughly $6.25 per hour for a B200 GPU, with a $30 monthly free credit from sponsor Modal.

Prerequisites include solid Python skills, familiarity with PyTorch, and an understanding of memory hierarchy concepts. The workload is estimated to be four to five times that of classic Stanford courses like CS224n or CS229, leading some community members to describe the course as “hardcore enough to make you question life.” Consequently, the course is not suited for rapid completion but is strongly recommended for anyone aiming to work in AI infrastructure, model training, or who wants a deep, hands‑on grasp of LLM internals.