Editorial illustration for Karpathy Unveils Nanochat: 8,000-Line PyTorch LLM Training Roadmap
Nanochat: Karpathy's 8K-Line Guide to Building Your Own LLM
Karpathy releases nanochat: 8,000-line PyTorch LLM roadmap (Oct 13 2025)
Andrej Karpathy has a knack for demystifying complex AI technologies. His latest project, nanochat, promises to transform how developers understand large language model (LLM) training.
The renowned AI researcher isn't just releasing another tutorial. He's providing a practical, no-nonsense roadmap that could dramatically lower the entry barriers for machine learning engineers wanting to build their own AI models.
Imagine condensing months of machine learning complexity into an 8,000-line PyTorch codebase. That's exactly what Karpathy has accomplished with nanochat, turning what once seemed like an impenetrable black box into something approachable and digestible.
For developers tired of complex, overwrought AI frameworks, this could be a game-changer. The project suggests you don't need a PhD or massive computational resources to understand - and build - modern language models.
So how exactly does Karpathy break down this intricate process? His approach might just revolutionize how we think about AI model development.
Launched on October 13, 2025, Karpathy’s nanochat project is an open-source LLM coded in roughly 8,000 lines of PyTorch. It gives you a straightforward roadmap on how to train a language model from scratch and make your own private AI in a couple of hours. In this article, we will talk about the newly released nanochat and how to properly set it up for the training step by step.
The nanochat repository provides a full-stack pipeline to train a minimal ChatGPT clone. It takes care of everything from tokenization to the end web user interface. This system is a successor to the previous nanoGPT.
It introduces key features such as supervised fine-tuning (SFT), reinforcement learning (RL), and enhanced inference. The project has a number of significant components. It incorporates a new Rust-built tokenizer for high performance.
The training pipeline employs quality data such as FineWeb-EDU for pretraining. It also employs specialized data such as SmolTalk and GSM8K for post-training fine-tuning.
Andrej Karpathy's nanochat project offers an intriguing glimpse into demystifying large language model development. The open-source initiative, built in roughly 8,000 lines of PyTorch, provides developers a remarkably accessible pathway to creating personalized AI models.
What stands out is the project's core promise: training a language model from scratch in just a few hours. This could dramatically lower the technical barriers for individuals interested in understanding AI's inner workings.
The repository's full-stack pipeline suggests a pragmatic approach to model creation. By breaking down complex machine learning processes into a clear, step-by-step roadmap, Karpathy has potentially made AI development more approachable for hobbyists and professionals alike.
Still, questions remain about the practical limitations of an 8,000-line buildation. How strong will these custom models be? What trade-offs exist between simplicity and performance?
Ultimately, nanochat represents more than just code. It's an educational tool that invites developers to peek under the hood of large language models, transforming an often opaque technology into something tangible and explorable.
Further Reading
- Andrej Karpathy's 2025 LLM Year in Review – Andrej Karpathy has outlined paradigm shifts of LLMs in 2025, including fast inference engines ... - Radical Data Science
- Andrej Karpathy demonstrates nanochat scaling models by adjusting only depth values. - Radical Data Science
- It provides a full ChatGPT-style LLM, including training, inference and a web Ui, that can be trained for as little as $100: This repo is a full-stack ... - Simon Willison's Blog
Related Reading
LLMs & Generative AI Ant Group unveils Ring-1T, first open-source trillion-parameter reasoning model
LLMs & Generative AI ChatGPT Health Event Shows AI Modernizing Dev Workflows, GitLab Unveils Plans
LLMs & Generative AI Gen AI app sessions up fivefold, downloads jump 778% as ChatGPT leads traffic
From Archives Veo 3.1 Launches with Enhanced Flow AI Filmmaking Features, 275+ Creations
From Archives OpenAI to Restrict ChatGPT to 18+ Users
Common Questions Answered
How many lines of code does Karpathy's nanochat project use in PyTorch?
The nanochat project is coded in approximately 8,000 lines of PyTorch. This compact codebase provides a full-stack pipeline for training a minimal ChatGPT clone, making LLM development more accessible to developers.
What is the primary goal of Karpathy's nanochat project?
The nanochat project aims to demystify large language model training by providing a straightforward roadmap for developers to create their own AI models from scratch. It offers a practical approach that can potentially reduce the complexity and time required to develop a personalized language model.
How quickly can developers train a language model using nanochat?
According to the project description, developers can train a language model from scratch in just a couple of hours using the nanochat repository. This dramatically reduces the typical time and technical barriers associated with developing AI models.