OpenClaw System Requirements

Everything you need to run AI agents locally or in the cloud.

From Raspberry Pi to enterprise servers — find the setup that works for you.

🚀 Popular Learning Paths

🎓

Students

Free with .edu email

GitHub Student Pack →
💻

Quick Start

Browser-only learning

Try Colab →
🏠

Local Setup

Privacy-first approach

Install Guide →
🔧

Hardware Projects

Raspberry Pi & IoT

Build Something →

Find Your Perfect Setup

Not sure which option is right for you? Answer a few quick questions below to get a personalized recommendation.

Setup Wizard

👤 What describes you best?

🎓 Student: Learning AI/programming, have .edu email, limited budget
🏠 Hobbyist/Self-learner: Personal projects, evenings/weekends, own hardware
💼 Professional: Work projects, need reliability, budget for tools
👩‍🏫 Educator: Teaching others, need classroom setup, institutional resources

Free Options for Students & Educators

🎓 GitHub Education

  • Codespaces: 120 hours/month free
  • Copilot: Free AI coding assistant
  • Pro features: Private repos, actions
  • Apply: education.github.com

☁️ Google Colab

  • Free tier: 12 hours/day GPU access
  • Pro: $9.99/month for students
  • Hardware: T4/V100 GPUs included
  • Perfect for: Learning and experimentation

Quick Comparison: Choose Your Path

Setup Type Best For Cost Setup Time Hardware Needed
🎓 Student (Free) Learning, experimenting, portfolio $0 5 minutes Any device with browser
💻 Cloud/Virtual Quick start, no installation $0-50/month 2 minutes Any device with browser
🏠 Local Install Privacy, customization, learning $5-20/month 15 minutes 4GB+ RAM, 2GB storage
🔧 Hardware Projects Building physical agents, IoT $50-500 2-4 hours Raspberry Pi or similar
☁️ Professional Production, teams, 24/7 agents $50-200/month 1-2 hours Cloud infrastructure

👆 Use the Setup Wizard above for personalized recommendations based on your specific situation and goals.

Minimum System Requirements

These are the absolute minimum specs to run OpenClaw with cloud-based AI models:

Component Minimum Notes
CPU Dual-core 2.0GHz Any modern processor works
RAM 4GB More needed for local models
Storage 2GB free space SSD recommended for responsiveness
Network 1 Mbps stable For API calls and updates
GPU None required Only needed for local AI models

Works on older hardware: OpenClaw runs fine on 2015+ laptops, Raspberry Pi 4, and even some tablets. The AI processing happens in the cloud, so your device just needs to handle the interface.

Free Resources for Students & Educators

Learning OpenClaw doesn't have to cost anything. Here are the best free and discounted options for students, teachers, and self-learners.

GitHub Student Developer Pack

The best deal for students — free access to professional development tools:

  • GitHub Codespaces: 180 core hours/month (normally $432/month)
  • GitHub Copilot: AI coding assistant (normally $10/month)
  • DigitalOcean: $200 cloud credits
  • Heroku: Free dyno hours for app hosting
  • MongoDB Atlas: Free database hosting
# Apply with your .edu email https://education.github.com/pack # Instant approval for most domains # High school students: upload student ID

Cloud GPU Access for Learning

Google Colab

  • Free: 12 hours/day T4 GPU
  • Pro: $9.99/month unlimited
  • Perfect for: Modules 1-6
Free - $10/month

Kaggle Kernels

  • Free: 30 hours/week GPU
  • Hardware: P100, T4 GPUs
  • Perfect for: AI model training
Always Free

AWS Educate

  • Credits: $100-200/year
  • Hardware: EC2 GPU instances
  • Perfect for: Production projects
Free Credits

Local AI Models (Privacy-First)

For learners who want to keep everything private and local:

  • Ollama: Easiest setup, runs Llama 3, Code Llama, and other models
  • LM Studio: User-friendly GUI, model marketplace
  • GPT4All: CPU-only models, works on any laptop
  • Jan AI: ChatGPT-like interface for local models

Student tip: Start with free cloud options (Colab, Kaggle) for learning, then move to local models once you understand the basics. This gives you the best of both worlds — fast learning and complete privacy.

Cloud Deployment Options

Running OpenClaw in the cloud gives you 24/7 agent availability and better performance for sharing your agents with others.

Popular Cloud Providers

DigitalOcean Droplets

  • Basic: $12/month (2GB RAM, 1 vCPU) — Good for learning
  • Standard: $24/month (4GB RAM, 2 vCPU) — Recommended for most users
  • Performance: $48/month (8GB RAM, 4 vCPU) — Multiple agents
# Quick DigitalOcean setup (Ubuntu 22.04) sudo apt update && sudo apt upgrade -y curl -sSL https://install.openclaw.ai | sh openclaw setup --cloud --domain yourdomain.com

AWS EC2

  • t3.small: ~$15/month (2GB RAM, 2 vCPU)
  • t3.medium: ~$30/month (4GB RAM, 2 vCPU)
  • t3.large: ~$60/month (8GB RAM, 2 vCPU)

Google Cloud Platform

  • e2-small: ~$13/month (2GB RAM, 1 vCPU)
  • e2-medium: ~$27/month (4GB RAM, 2 vCPU)
  • e2-standard-2: ~$49/month (8GB RAM, 2 vCPU)

Free tier tip: Most cloud providers offer $200-300 in free credits. You can run a development OpenClaw instance for 3-6 months completely free.

Student discounts: With a .edu email, you can get massive discounts: GitHub Student Pack (free Pro), AWS Educate ($200 credits), DigitalOcean ($200 credits), Azure for Students ($100 credits). Total value: ~$1000+ in free cloud computing.

Docker Deployment

The easiest way to deploy OpenClaw in the cloud is with Docker:

# Pull the official OpenClaw image docker pull openclaw/openclaw:latest # Run with persistent storage docker run -d \ --name openclaw \ -p 8080:8080 \ -v openclaw-data:/data \ -e OPENAI_API_KEY=your_key_here \ openclaw/openclaw:latest # Access at http://your-server:8080

Local AI Models

For privacy, cost savings, or offline operation, you can run AI models locally. This requires significantly more hardware.

Hardware Requirements for Local Models

Model Size RAM Required GPU (Optional) Performance
7B parameters 8GB+ RTX 3060 (12GB) Good for basic tasks
13B parameters 16GB+ RTX 4070 (16GB) Balanced quality/speed
70B parameters 64GB+ RTX 4090 (24GB) x2 Excellent reasoning

Recommended Local Models

  • Ollama + Llama 3: Easy setup, good performance
  • LM Studio: User-friendly GUI interface
  • GPT4All: Runs on CPU, no GPU needed
  • Jan AI: ChatGPT-like interface for local models
# Install Ollama (macOS/Linux) curl -fsSL https://ollama.ai/install.sh | sh # Download and run Llama 3 ollama pull llama3 ollama serve # Configure OpenClaw to use Ollama openclaw config set ai.provider ollama openclaw config set ai.model llama3

Performance note: Local models are 10-100x slower than cloud APIs. A response that takes 2 seconds with ChatGPT might take 30 seconds locally. Plan accordingly.

Virtual Development Environments

Can't install OpenClaw locally? These browser-based environments let you learn and build agents from anywhere.

Browser-Based IDEs

GitHub Codespaces

  • Full VS Code in browser
  • 60 hours/month free
  • 4-core, 8GB RAM machines
  • Perfect for: Complete development

GitPod

  • 50 hours/month free
  • Pre-configured environments
  • VS Code or Theia IDE
  • Perfect for: Quick prototypes

Specialized Platforms

  • Google Colab: Jupyter notebooks with GPU access — great for AI model experimentation
  • Kaggle Kernels: Free GPU compute, massive datasets, community sharing
  • Observable: Interactive notebooks for data visualization and agent dashboards
  • RunKit: Node.js playground perfect for testing OpenClaw integrations

Chromebook users: Virtual environments are your best option! GitHub Codespaces gives you a full development environment that runs entirely in your browser.

Hardware Integration Projects

Once you've mastered the basics, take your agents into the physical world with these educational hardware projects.

Beginner: Raspberry Pi Voice Assistant

Cost: ~$100 | Skill Level: Beginner | Time: Weekend project

  • Hardware: Raspberry Pi 4B (4GB), USB microphone, speaker, SD card
  • What you'll build: Wake-word activated AI assistant
  • Skills learned: Hardware interfacing, voice processing, always-on agents
  • Perfect for: Module 7 (Hardware Week) projects
# Quick Raspberry Pi setup curl -sSL https://install.openclaw.ai/pi | sh sudo apt install pulseaudio-utils espeak-ng openclaw config set voice.input microphone openclaw config set voice.output speaker

Intermediate: Smart Home Agent

Cost: ~$200 | Skill Level: Intermediate | Time: Week-long project

  • Hardware: Raspberry Pi, smart plugs, temperature sensors, cameras
  • What you'll build: AI agent that controls your room/home
  • Skills learned: IoT protocols, sensor integration, automation
  • Perfect for: Science fair projects, Module 8-9 capstone

Advanced: Mobile Agent Companion

Cost: $0 (use your phone) | Skill Level: Advanced | Time: Month-long project

  • Platform: Android (Termux) or iOS (iSH Shell)
  • What you'll build: AI agent that lives in your pocket
  • Skills learned: Mobile development, battery optimization, edge AI
  • Perfect for: Competition projects, portfolio pieces

Expert: Edge AI with NVIDIA Jetson

Cost: $500-2000 | Skill Level: Expert | Time: Semester project

  • Hardware: Jetson Orin Nano or AGX Orin
  • What you'll build: AI agent with local model processing
  • Skills learned: GPU programming, model optimization, production deployment
  • Perfect for: University capstone projects, startup prototypes

Education discount: NVIDIA offers educational discounts on Jetson hardware through their Academic Program. Check with your school's engineering department.

Performance Optimization

Speed Up Response Times

  • Use SSD storage: 3-5x faster than traditional hard drives
  • Close unused applications: Free up RAM for OpenClaw
  • Stable internet: Use ethernet over WiFi when possible
  • Choose nearby AI servers: Some providers offer regional endpoints

Reduce AI API Costs

  • Use smaller models: GPT-3.5 vs GPT-4 for routine tasks
  • Cache responses: Enable OpenClaw's built-in caching
  • Shorter prompts: Be concise to reduce token usage
  • Local models for development: Test with free local models first

Memory Management

# Check OpenClaw memory usage openclaw status --memory # Clear conversation history to free RAM openclaw clear --conversations --older-than 7d # Optimize for low-memory systems openclaw config set performance.mode minimal openclaw config set cache.size 100MB

Network Optimization

  • CDN usage: Enable OpenClaw's CDN for faster asset loading
  • Compression: Enable gzip for API responses
  • Concurrent limits: Don't overwhelm slow connections

Common Issues & Solutions

Installation Problems

Permission denied during install

Solution: Run installer with appropriate permissions:
sudo curl -sSL https://install.openclaw.ai | sh

Python version conflicts

Solution: OpenClaw requires Python 3.8+. Install via pyenv:
pyenv install 3.11 && pyenv global 3.11

Runtime Issues

Slow response times

Check: Internet connection, AI API status, available RAM
Debug: openclaw debug --performance

API rate limiting

Solution: Reduce request frequency or upgrade AI provider plan
Config: openclaw config set ai.rate_limit 10

Hardware-Specific Issues

Raspberry Pi audio problems

Solution: Configure ALSA audio properly
sudo apt install pulseaudio pulseaudio-utils

GPU not detected for local models

Check: NVIDIA drivers, CUDA installation, GPU memory
Test: nvidia-smi should show your GPU

Getting Help

  • System diagnostics: openclaw doctor — comprehensive health check
  • Log files: ~/.openclaw/logs/ — check for error messages
  • Community support: OpenClaw Discord
  • Bug reports: GitHub Issues

Ready to Start Building?

Whether you're a student, educator, or self-learner — start with our free resources and build your first AI agent in 30 minutes.

Start Module 1 → Get Student Pack Try Google Colab

🎓 Students: Get $1000+ in free cloud credits | 💻 Everyone else: Local installation is free