Autonomous AI Research with Autoresearch on Vast.ai - Vast.ai Documentation: Affordable GPU Cloud Marketplace

Introduction

Autoresearch is Andrej Karpathy’s framework for autonomous AI-driven ML research. The idea is simple: point an AI agent (Claude Code) at a small but real LLM training setup and let it experiment autonomously overnight. The agent modifies the model code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats, running ~12 experiments per hour, ~100 overnight. This guide walks you through setting up autoresearch on a Vast.ai GPU instance with Claude Code as the autonomous research agent.

Prerequisites

A Vast.ai account with credits
An Anthropic account for Claude Code
SSH key pair (add your public key in your Vast account settings)
Vast CLI installed on your local machine

Install the Vast CLI if you haven’t already:

pip install vastai
vastai set api-key YOUR_API_KEY

Rent a GPU Instance and Set Up

Autoresearch requires a single NVIDIA GPU with 80GB VRAM (H100 or A100 80GB). It needs CUDA 12.8+ and about 50GB of disk for the repo, data, and dependencies.

Use Template
Manual Setup

Use the Autoresearcher template to launch a pre-configured instance with uv, Claude Code, and autoresearch already installed.

Learn more about templates

Templates are reusable configurations that bundle a Docker image, environment variables, and startup scripts into a one-click launch.

Search for available instances:

vastai search offers 'gpu_ram>=70 num_gpus=1 cuda_vers>=12.8 disk_space>=50 reliability>0.95' -o 'dph+'

Pick an instance ID from the results and rent it using the template:

vastai create instance INSTANCE_ID \
  --template_hash 934769670bfd9bc5e05d8696ef340c2b \
  --disk 50

Wait for the instance to be ready, then SSH in:

vastai show instances
ssh -p PORT root@HOST_IP

The template installs everything on first boot (~10 minutes). You can monitor progress with tail -f /var/log/provisioning.log.

The template automatically configures Claude Code permissions (Read, Edit, Write, Bash) in .claude/settings.json so it can run experiments without prompting, no manual setup needed.Once provisioning completes, skip ahead to Launch Autonomous Research.

Search for available instances:

vastai search offers 'gpu_ram>=70 num_gpus=1 cuda_vers>=12.8 disk_space>=50 reliability>0.95' -o 'dph+'

Pick an instance ID from the results and rent it:

vastai create instance INSTANCE_ID \
  --image vastai/pytorch \
  --disk 50 \
  --ssh \
  --direct

Wait for the instance to be ready:

vastai show instances

Once the status shows running, get your SSH connection details:

vastai ssh-url INSTANCE_ID

SSH in

ssh -p PORT root@HOST_IP

Vast instances start in a tmux session by default. This keeps your processes running if your SSH connection drops, essential for overnight research runs.

Install uv

uv is the package manager used by autoresearch:

curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env

Install Claude Code

Claude Code requires Node.js:

curl -fsSL https://deb.nodesource.com/setup_22.x | bash -
apt-get install -y nodejs
npm install -g @anthropic-ai/claude-code

Clone and install

cd /workspace
git clone https://github.com/karpathy/autoresearch.git
cd autoresearch
uv sync

Prepare the data

This downloads training data from HuggingFace and trains a BPE tokenizer. Takes about 2 minutes:

uv run prepare.py

Data is cached in ~/.cache/autoresearch/, you only need to run this once.

Run a baseline experiment

Verify everything works by running a single 5-minute training experiment:

uv run train.py

After ~5 minutes you’ll see output like:

---
val_bpb:          0.995583
training_seconds: 300.3
total_seconds:    349.8
peak_vram_mb:     45060.2
mfu_percent:      39.57
total_tokens_M:   497.0
num_steps:        948
num_params_M:     50.3
depth:            8

The key metric is val_bpb (validation bits per byte), lower is better. Note this baseline number; Claude will try to beat it.

Configure permissions

Claude Code normally asks for permission before running commands or editing files. For autonomous overnight research, you need to pre-approve the tools Claude will use. Create a settings file in the autoresearch directory:

mkdir -p /workspace/autoresearch/.claude
cat > /workspace/autoresearch/.claude/settings.json << 'EOF'
{
  "permissions": {
    "allow": [
      "Read",
      "Edit",
      "Write",
      "Bash"
    ]
  }
}
EOF

This tells Claude Code to run these commands without asking, essential for unattended operation.

Launch Autonomous Research

Start Claude Code

cd /workspace/autoresearch
claude

When Claude Code starts, log in to your Anthropic account:

/login

This will give you a URL to open in your browser. Follow the prompts to authenticate, then you’re ready to go. Kick off the research loop:

Hi have a look at program.md and let's kick off a new experiment! let's do the setup first.

Claude will:

Read program.md for the research guidelines
Create a fresh git branch (e.g. autoresearch/mar10)
Run the baseline experiment
Begin the autonomous loop, modifying train.py, training for 5 minutes, evaluating, keeping improvements, discarding regressions
Log all results to results.tsv

Claude runs indefinitely until manually stopped. Each experiment takes ~5 minutes, so you can expect ~12 experiments/hour and ~100 experiments overnight. Each iteration also uses Claude API tokens.

What Claude can modify

Claude has full freedom to edit train.py, the model architecture, optimizer, hyperparameters, batch size, model size, training loop. The only constraints are:

prepare.py is read-only, the evaluation harness and data loading are fixed
No new packages, only dependencies in pyproject.toml
5-minute time budget, every experiment runs for exactly 5 minutes

Monitoring progress

In another tmux pane (Ctrl+b then %), you can watch the experiment log:

watch -n 30 cat /workspace/autoresearch/results.tsv

Or check the git log to see what Claude has tried:

cd /workspace/autoresearch
git log --oneline -20

Cleanup

When you’re done, download your results and destroy the instance:

# From your local machine — copy results
scp -P PORT root@HOST_IP:/workspace/autoresearch/results.tsv ./results.tsv

# Destroy the instance
vastai destroy instance INSTANCE_ID

Destroying an instance permanently deletes all data on it. Make sure to copy any results you want to keep before destroying.

Additional Resources

Autoresearch GitHub repo
Nanochat, the full training framework autoresearch is derived from
Claude Code documentation
Vast.ai CLI reference

​Introduction

​Prerequisites

​Rent a GPU Instance and Set Up

Learn more about templates

​SSH in

​Install uv

​Install Claude Code

​Clone and install

​Prepare the data

​Run a baseline experiment

​Configure permissions

​Launch Autonomous Research

​Start Claude Code

​What Claude can modify

​Monitoring progress

​Cleanup

​Additional Resources

Introduction

Prerequisites

Rent a GPU Instance and Set Up

SSH in

Install uv

Install Claude Code

Clone and install

Prepare the data

Run a baseline experiment

Configure permissions

Launch Autonomous Research

Start Claude Code

What Claude can modify

Monitoring progress

Cleanup

Additional Resources