Minimum and Recommended Hardware Specs
Minimum: 2GB RAM, 2 CPU cores, Node.js 22+. Recommended for personal use: Mac Mini M4 with 16GB RAM. For business: Mac Mini M4 with 24GB+ RAM or a VPS with 4GB+ RAM. Mac Mini is preferred for always-on personal agents. VPS (DigitalOcean, Hetzner, Vultr) is preferred for cloud availability. Docker is recommended for all production deployments.
Specific Hardware Recommendations
For personal use, the Mac Mini M4 with 16GB unified memory is the best value option. It draws minimal power (under 15W idle), runs silently, and provides more than enough performance for OpenClaw with multiple skills active. For business deployments handling higher message volumes, upgrade to the 24GB model to allow comfortable headroom for Docker containers, log storage, and concurrent skill execution. If you plan to run a local LLM via Ollama alongside OpenClaw, the Mac Mini M4 Pro with 48GB RAM is the minimum for usable inference speeds with 30B parameter models.
VPS and Cloud Options
Cloud hosting works well if you need high availability or prefer not to maintain physical hardware. See our hosting comparison for a detailed breakdown of providers. A 4GB RAM, 2 vCPU droplet on DigitalOcean ($24/month) handles most personal workloads. For business use, an 8GB RAM, 4 vCPU instance ($48/month) provides solid performance. Hetzner offers the best price-to-performance ratio in Europe, while Vultr and Linode are strong alternatives in the US. All cloud deployments should use Docker Compose for repeatable setup and easy updates.
Budget-Friendly Options
If you are on a tight budget, you can run OpenClaw on any modern computer you already own, including older laptops or a Raspberry Pi 5 with 8GB RAM for basic workloads. Used Mac Minis from previous generations (M1 or M2) can be found for $300-400 and still handle OpenClaw comfortably since the heavy computation happens on the LLM provider's servers, not your local machine. The only scenario where local hardware matters significantly is if you want to run local AI models, which demands substantially more RAM and GPU power.
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