Don’t overcomplicate it. OpenClaw is surprisingly lightweight for an AI assistant. This page covers what you actually need — from bare minimum to running local AI models.

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Quick answer: what do you need?

Minimum

1 vCPU · 1 GB RAM · 10 GB Messaging only.

Recommended

2+ vCPU · 4 GB+ RAM · 20 GB Full features + browser automation.

Power User

4+ cores · 16 GB+ RAM · GPU Local AI models.
  • 4 GB RAM (8 GB recommended; see the tiers below for what runs on less)
  • Modern x86_64 or ARM64 CPU
  • Node.js 22+
  • Always-on internet connection
  • Runs on Linux, macOS, or Windows

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Detailed tiers

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Minimum — messaging only

  • Specs: 1 vCPU, 1 GB RAM, 10 GB storage, 1 Mbps+
  • Can do: Telegram and web messaging, simple text commands, basic API integrations, scheduled messages, and additional OpenClaw channels when configured
  • Can’t do: browser automation, local AI models, multiple concurrent tasks, image processing
  • Examples: $5/mo VPS · Raspberry Pi 4 (2 GB) · old laptop
  • Specs: 2+ vCPU, 4 GB+ RAM, 20 GB+ storage, 10 Mbps+, Linux preferred
  • Can do: full browser automation (Playwright), web scraping & research, multiple messaging platforms, image analysis via API, complex workflows, voice messages
  • Can’t do: large local AI models, real-time video
  • Examples: $10–20/mo VPS · Raspberry Pi 5 (8 GB) · ClawBox Connect · Mac Mini

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Power User — local AI models

  • Specs: 4+ cores, 16 GB+ RAM, 100 GB+ SSD, 25 Mbps+, NVIDIA GPU, Linux (Ubuntu/Debian)
  • Can do: everything above, plus local LLMs (1B–8B; 1–4B is the sweet spot on ClawBox Connect), local image generation, on-device speech recognition, fully offline operation, multiple browser instances
  • Can’t do: 70B+ models need more VRAM → see ClawBox Workstation
  • Examples: ClawBox Connect (67 TOPS) · gaming PC with GPU · ClawBox Workstation (DGX Spark)

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Why dedicated hardware?

Running OpenClaw on dedicated hardware vs a shared VPS or your daily-use laptop:

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Local model performance (ClawBox Connect, 67 TOPS)

Measured on a production ClawBox with Ollama (warm runs, num_predict=200, Ollama’s own eval_rate; power and thermals from tegrastats over 176 samples): ~11.4 W average, 19.2 W peak, 61.8 °C peak, no throttling. Most people read at 5–8 tok/s, so the 1–4B class generates faster than you can read. Full methodology: We Benchmarked a Production ClawBox.
For larger models, use your own cloud API key (Claude, GPT, Gemini) — see Choose Your AI Provider. For large models fully local, see ClawBox Workstation (128 GB unified memory, up to ~200B params).

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Skip the DIY

Get ClawBox — pre-configured, ready in 5 minutes

ClawBox ships with Recommended+ specs, OpenClaw pre-installed, dual-band Wi-Fi 5 (802.11ac) and Bluetooth 5.0. Manual DIY setup is typically 2–4+ hours (plus waiting for hardware).