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Karpathy is Back with llm.c: A Pure C Implementation of GPT-2 in <1000 Lines

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Andrej Karpathy, a former member of OpenAI's founding team and former Director of AI at Tesla, has recently released his second educational project focusing on Language Models (LLMs). This project, called "llm.c," is a pure C implementation of the GPT-2 model with 124 million parameters, designed to be trained on a CPU using only C/CUDA, without relying on PyTorch.

For those unfamiliar with the term, Language Models (LLMs) are a type of artificial intelligence that can understand and generate human-like language. They have become increasingly popular in recent years, powering applications such as chatbots, language translation tools, and text summarization systems.

Components and Types

The "llm.c" codebase is a remarkable achievement, consisting of around 1,000 lines of code in a single file. This compact codebase allows for the training of the GPT-2 model on a CPU with 32-bit precision, making it an excellent resource for understanding the inner workings of language model training.

Karpathy chose to focus on the GPT-2 model because its model weights are publicly available, courtesy of OpenAI. The project utilizes C for its simplicity and direct hardware interaction, enabling a deeper understanding of the model's architecture and training process.

Benefits and Challenges

One of the key benefits of the "llm.c" project is its accessibility. By providing a concise and self-contained implementation, Karpathy has made it easier for developers and researchers to explore and understand the intricacies of language model training. This level of transparency and simplicity is crucial for advancing the field of AI and fostering a more inclusive and collaborative environment.

However, training language models is a computationally intensive task, and the current CPU/fp32 implementation of "llm.c" is still relatively inefficient. This means that training these models from scratch on a CPU is not yet practical. Instead, the project initializes with the GPT-2 weights released by OpenAI and fine-tunes them on a tokenized dataset.

Examples and Future Implications

Karpathy's work contributes significantly to the open-source community and the field of AI. This second educational project goes one step further in democratizing AI by showing how a model can be trained and optimized using a single file of code.

The project's repository includes code for downloading and tokenizing a small dataset, on which the model can be trained. While the current implementation is not optimized for training from scratch, Karpathy is actively working on improvements, such as:

  • Direct CUDA implementation for significantly faster training
  • Utilizing SIMD instructions, AVX2 on x86, and NEON on ARM (e.g., Apple Silicon) for CPU speedup
  • Exploring more modern architectures like Llama2 and Gemma


Andrej Karpathy's "llm.c" project is a remarkable contribution to the field of AI and the open-source community. By providing a pure C implementation of the GPT-2 model in under 1,000 lines of code, Karpathy has made it easier for developers and researchers to understand and explore the intricacies of language model training. As the project continues to evolve, with improvements in efficiency and the exploration of newer architectures, it has the potential to further democratize AI and foster a more inclusive and collaborative environment for innovation.

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