What Hardware Do You Need to Run OpenClaw?

Introduction

One of OpenClaw's great advantages is hardware flexibility. Unlike cloud AI services that require you to accept whatever infrastructure the provider chose, OpenClaw runs on hardware you own — from a $25 Raspberry Pi Zero to a $5,000 workstation with multiple GPUs. The right hardware choice depends on how you plan to use the agent: cloud models only, local models, 24/7 operation, multi-agent teams, or occasional personal use.

This guide maps hardware options to use cases with specific, practical recommendations. We'll cover minimum requirements, the community's favorite options, and what to choose if you want to run powerful local models without cloud API costs.

Minimum Specifications

OpenClaw itself is a Node.js service — lightweight and not particularly resource-intensive. The absolute minimum to run the gateway service with cloud-based LLM providers:

• CPU: Any dual-core processor made in the last 10 years
• RAM: 2GB (4GB recommended for comfortable operation)
• Storage: 5GB free disk space for installation, logs, and memory files
• Network: Stable internet connection (for cloud API calls and messaging webhooks)
• OS: Linux (Ubuntu 22.04+), macOS 13+, or Windows 10+ with WSL2

These minimums mean OpenClaw can run on very modest hardware when using cloud models for inference. An old laptop gathering dust in a drawer, a cheap VPS, or a Raspberry Pi 4 with 4GB RAM can all serve as capable OpenClaw hosts when using OpenAI or Anthropic for the AI processing.

If you want to run local models, the hardware requirements increase significantly — covered in the dedicated section below.

Mac Mini: The Recommended Option

The Mac Mini M4 (or M2 for budget-conscious buyers) is the community's overwhelming first choice for dedicated OpenClaw getting it running. Multiple factors converge to make it the optimal platform:

Apple Silicon efficiency: The M-series chips deliver exceptional performance per watt. An M4 Mac Mini idles at 4–8 watts and performs complex tasks at 15–20 watts. Running 24/7, this costs roughly $1–2/month in electricity. An Intel-based machine doing the same work would cost 5–10x more in power.

Unified memory architecture: Apple Silicon's unified memory (CPU and GPU sharing the same pool) is uniquely beneficial for local LLM inference. A Mac Mini M4 with 24GB RAM can run a 14B parameter model fully in GPU memory — something a PC with a 12GB VRAM GPU cannot do without offloading to CPU RAM.

Silent, always-on design: The Mac Mini is designed for continuous operation in home environments. Fanless in light loads, near-silent under sustained load, small enough to hide behind a monitor, and completely stable running 24/7 for months without intervention.

macOS ecosystem benefits: iMessage integration (unique to Apple hardware), Keychain for secure credential storage, Spotlight exclusion for privacy, and excellent launchd process management for reliable service operation.

Recommended Mac Mini configurations for OpenClaw:

• Cloud models only: Mac Mini M4 8GB ($600) — more than sufficient
• Small local models (7–13B): Mac Mini M4 16GB ($800)
• Large local models (30–70B): Mac Mini M4 Max 64GB ($2,000+)

Raspberry Pi for Budget Setups

The Raspberry Pi 5 with 8GB RAM is the budget champion — a capable OpenClaw host for around $80–120 including power supply and storage. It runs the Node.js gateway service easily with cloud models and provides 24/7 operation at 3–5 watts power consumption.

What it can do well:

• Run the OpenClaw gateway service continuously
• Handle all cloud model API communication (OpenAI, Anthropic, Google)
• Execute Skills including shell commands, file operations, and HTTP requests
• Maintain memory files and run heartbeat tasks

What it cannot do well:

• Run models larger than about 2–3B parameters at usable speed
• Handle memory-intensive Skills that require significant RAM
• Process large files or run complex browser automation reliably

Setup notes for Raspberry Pi: use a quality SD card (SanDisk Endurance series or similar) or better yet an SSD via USB 3. SD cards can fail under continuous write loads. Install Ubuntu Server 22.04 rather than Raspberry Pi OS for better Node.js compatibility. Configure a swap file of at least 4GB to handle memory spikes.

VPS & Cloud Hosting

A Virtual Private Server (VPS) is the right choice when you want 24/7 operation without dedicated physical hardware, need your agent accessible from anywhere without port forwarding, or want to easily scale up resources. Major providers:

Hetzner: The community favorite for European-hosted VPS. Exceptionally good value — a 2 vCPU, 4GB RAM instance runs about $5/month, with excellent network performance. Hetzner's data sovereignty in Germany is appealing for European users concerned about US cloud provider jurisdiction.

DigitalOcean: Well-documented, beginner-friendly, excellent community resources. A basic Droplet suitable for OpenClaw (with cloud models) costs $6/month. The "Droplet" metaphor and one-click app deployments make it accessible for users new to server management.

Fly.io: Particularly good for the Dockerized OpenClaw deployment pattern. Generous free tier for small machines, automatic geographic distribution, and an elegant deployment workflow.

VPS cons: ongoing monthly costs, the complexity of server management, and the fact that your API keys and memory files exist on infrastructure you don't physically control (though VPS providers typically offer strong contractual protections).

Hardware for Local Models

Running local models changes the hardware calculus significantly. Local inference requires substantial RAM (CPU inference) or VRAM (GPU inference) to load model weights.

For 7–8B parameter models (minimum viable quality): 8GB RAM (Mac) or a GPU with 8GB VRAM. Most modern mid-range laptops and the base Mac Mini qualify.

For 13–14B parameter models (good quality): 16GB RAM (Mac with unified memory) or a GPU with 12GB+ VRAM (RTX 3060 Ti or better).

For 30–70B parameter models (frontier-adjacent quality): 32–64GB RAM (Mac Studio or MacBook Pro with high-memory config) or multiple GPUs with combined VRAM, or consumer AI workstations like the NVIDIA DGX Spark.

NVIDIA GPU notes: CUDA acceleration in Ollama is exceptional. An RTX 4090 (24GB VRAM) runs 70B models at 40–60 tokens/second — faster than most use cases require. If you already own NVIDIA GPU hardware, it's a strong platform for local OpenClaw getting it running.

Power Consumption & Running Costs

*At $0.12/kWh average US electricity rate

For most users, the annual electricity cost of dedicated OpenClaw hardware is negligible — under $15/year for a Mac Mini. The hardware investment pays for itself quickly against the time saved, and the API costs saved by using efficient models or local inference dwarf the electricity expense.

Wrapping Up

Hardware choice for OpenClaw is pleasantly flexible. If you're starting out and want to test before committing, run it on your laptop or a cheap VPS. For a permanent, reliable deployment with cloud models, a Mac Mini M4 is the community's clear recommendation. For maximum privacy and zero ongoing API costs, the same Mac Mini running Ollama with a 14B or larger model delivers frontier-adjacent quality entirely on your own hardware. The right hardware is the one that matches your use case, budget, and privacy requirements — and OpenClaw runs well on all of them.