Stanford CS336: Language Modeling from Scratch

Stanford University's CS336, "Language Modeling from Scratch," is a comprehensive technical course designed to move students beyond theoretical understanding to the actual implementation of a full-scale language model. Drawing a parallel to operating systems courses where students build an OS from scratch, CS336 requires students to develop every component of a language model, including data pipelines, transformer architectures, training systems, and alignment methods.

Course Objectives and Curriculum

The primary goal of CS336 is to provide a deep understanding of the entire language model lifecycle. The curriculum is structured as a progression from basic architecture to advanced scaling and alignment:

1. Core Architecture and Basics

Students begin by implementing the fundamental components of a standard Transformer language model, including the tokenizer, model architecture, and optimizer. This phase culminates in the training of a minimal language model.

2. Systems Optimization

This section focuses on the intersection of deep learning and systems engineering. Students must profile and benchmark their models and implement a memory-efficient, distributed version of their training code. A key technical requirement is the implementation of FlashAttention2 using Triton to optimize attention mechanisms.

3. Scaling and Data Engineering

Students explore the relationship between model size and performance through scaling laws, using a training API to project model scaling. The data engineering component involves converting raw Common Crawl dumps into usable pretraining data through rigorous filtering and deduplication.

4. Alignment and Reasoning

The final stage covers post-training techniques. Students apply supervised finetuning (SFT) and reinforcement learning (RL) to enable models to reason through mathematical problems. Optional modules include safety alignment methods such as Direct Preference Optimization (DPO).

Technical Prerequisites

CS336 is a 5-unit, implementation-heavy course with minimal scaffolding provided for assignments. The prerequisites are rigorous:

  • Software Engineering: High proficiency in Python is required, as the volume of code written is significantly higher than in typical AI classes.
  • Deep Learning Frameworks: Strong familiarity with PyTorch and an understanding of systems concepts like the memory hierarchy are essential for GPU optimization.
  • Mathematics: Proficiency in college-level calculus, linear algebra (matrix/vector operations), and basic probability and statistics (Gaussian distributions, mean, and standard deviation).
  • Machine Learning Foundations: Prior experience with basic machine learning and deep learning concepts (e.g., via courses like CS221, CS229, or CS224N).

Compute Requirements and Self-Study

While the course is designed for Stanford students, materials and video lectures are available online for self-study. The course recommends debugging on CPUs before moving to GPUs for training runs and benchmarking.

Recommended GPU Providers

For those following the course independently, several cloud providers are suggested for accessing B200 GPUs:

  • Modal: $6.25/hour (Sponsor)
  • Lambda Labs: $6.69/hour
  • RunPod: $4.99/hour
  • Nebius: $5.50/hour
  • Together: $7.49/hour (minimum 8 GPUs)

Community Insights on Hardware

Discussion among self-studiers indicates that the high-end B200 GPUs recommended by the course may not be necessary for the early phases. Some learners have successfully used NVIDIA RTX 4090s or even older cards like the RTX 2060 Super to reproduce basic results, though the course's official harness is optimized for Linux environments with NVIDIA GPUs and specific CUDA versions.

Academic Integrity and AI Policy

To ensure deep engagement with the material, the course enforces a strict AI policy. While LLMs like ChatGPT can be used for high-level conceptual questions or low-level programming syntax, using them to solve the assignment problems directly is prohibited. The course staff strongly encourages students to disable AI autocomplete tools (such as GitHub Copilot or Cursor Tab) to prevent superficial learning.

Learner Perspectives

Independent learners have noted the high difficulty and attrition rate of the course. One learner reported starting a study group of 30 people, which dwindled to 8 by the final session due to the challenge of the workload. Another noted that the 2025 version of the course took several months of part-time work to complete, highlighting the intensity of the debugging and thinking required in the first two assignments.

Sources