Karpathy's nanoGPT and nanochat Explained | Generated by AI
Overview
Both nanoGPT and nanochat are open-source projects created by Andrej Karpathy (former OpenAI/Tesla AI lead) to teach the internals of large language models (LLMs) through minimal, from-scratch implementations in PyTorch. They are educational tools rather than production-ready systems, emphasizing simplicity and readability over optimizations.
- nanoGPT (released in 2023): A lightweight framework for training GPT-like transformer models on raw text data, focusing solely on the pretraining phase.
- nanochat (released in October 2025): An expanded, full-stack evolution of nanoGPT, enabling end-to-end training, fine-tuning, inference, and deployment of a ChatGPT-style conversational AI.
Key Differences
Here’s a side-by-side comparison:
| Aspect | nanoGPT | nanochat |
|---|---|---|
| Primary Focus | Pretraining a GPT model on unstructured text (e.g., Shakespeare dataset). | Full pipeline: Pretraining + fine-tuning for chat + inference in a web UI. |
| Scope | Minimal transformer implementation (~400 lines of core code). No chat interface. | ~8,000 lines total, including RLHF-like fine-tuning, sampling, and a Streamlit-based chat demo. |
| Training | Causal language modeling on next-token prediction. | Extends to supervised fine-tuning (SFT) and preference optimization (e.g., DPO) for dialogue. |
| Inference | Basic text generation. | Interactive chat mode with system/user/assistant prompts, temperature sampling, and safety filters. |
| Hardware/Cost | Trainable on a single GPU (e.g., 125M params in hours). | Similar efficiency; claims “best ChatGPT $100 can buy” via cheap cloud GPUs. |
| Inspirations | Teaches transformer basics. | Builds on nanoGPT + modded-nanoGPT (gamified version); aims for a complete “ChatGPT clone.” |
| Use Case | Learning LLM pretraining. | Building/deploying a basic conversational AI from scratch. |
| GitHub Stars | ~30k+ (as of 2025). | Newly released; rapid growth to 10k+ in days. |
In essence, nanoGPT is like building the “brain” of a GPT model, while nanochat adds the “mouth” (chat interface) and “ears” (dialogue handling), making it a more complete tutorial for modern LLM apps. If you’re diving in, start with nanoGPT for fundamentals, then nanochat for chat-specific extensions.
References
- nanochat GitHub Repo
- Karpathy’s Announcement on X
- Forbes Article on nanochat
- Medium Post: From nanoGPT to NanoChat