How to Run Karpathy's Autoresearch on Vast H100s

Andrej Karpathy has introduced autoresearch, a framework that enables an AI agent to autonomously conduct machine learning research experiments, optimizing model architecture and hyperparameters without human intervention. By deploying a single H100 GPU overnight, the framework can execute around 100 experiments, each lasting five minutes, allowing the agent to modify, train, evaluate, and decide on the retention of improvements. This innovation addresses the bottleneck in ML research by automating the repetitive experiment loop, thus freeing researchers to concentrate on more complex tasks requiring human intuition. The framework features a simplified implementation of nanochat, a small GPT model trained on FineWeb-Edu, which the AI agent adapts within its constraints. A guide for running autoresearch on Vast.ai includes instructions for setting up the necessary environment and monitoring the experiments, requiring only a short setup time and accounts on Vast.ai and Claude Code.

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