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 released another tutorial. The AI world yawned, then read it. He has a habit of doing that.
His latest, nanochat, is an 8,000-line PyTorch codebase published on October 13, 2025. It is not another abstract lecture. It is a specific, executable plan for building a large language model from nothing.
He calls it a roadmap. It looks more like a skeleton key.
For engineers who view modern AI training as a corporate fortress, this is a set of blueprints. The project suggests the moat is not that deep. You can train a basic private AI in hours, not months, without a PhD or a datacenter. The promise is simple: transparency instead of magic.
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.
The value is in the constraints. Eight thousand lines of code is a weekend project, not a corporate SDK. It bundles the entire process, from a Rust tokenizer to a web UI.
It uses defined datasets like FineWeb-EDU. This is a conscious rejection of complexity. The question is what gets lost in the compression.
You will not build Claude in an afternoon. But you might finally understand how the pieces fit together.
Karpathy is selling comprehension, not competition. The models will be small. The performance will be academic.
The point is to make the black box slightly less black. When every major AI lab operates behind velvet ropes, a public schematic feels radical. It is less a tool and more a statement: this technology is not sorcery.
It is just code.
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
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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.
