If you are buying a Mini PC to run OpenClaw, find out where the language model will run before you shop. OpenClaw’s Gateway can connect to a hosted model provider, while local inference runs the model on your own machine. The second option can require much more memory, storage, and GPU capacity. A powerful graphics card may be unnecessary if the model stays in the cloud.

OpenClaw’s model-provider quickstart describes hosted providers, and its local-model guide separately explains hardware-aware local setup. The computer’s job changes with that choice: the Gateway handles agent orchestration, while a local model has to fit and run on the host.

Key Takeaways

  • Choose hosted inference if your Mini PC will run only the Gateway and agent tools.
  • Choose local inference only after selecting a model and checking its memory, disk, and GPU requirements.
  • Treat a documented minimum as a floor for setup, not a promise of good speed or reliable agent turns.
  • Test the actual tools and prompts you plan to use before making the computer your permanent server.

Decide between hosted and local inference

With a hosted model, the Mini PC runs OpenClaw and connects to the provider over the network. This reduces local model resource needs, but requests go to the provider you configure. Check its pricing, privacy terms, availability, and credentials before using it with sensitive messages or files.

With a local model, your computer also downloads and runs the model. OpenClaw supports managed llama.cpp setup and connections to local servers such as Ollama or LM Studio. The official guide says memory depends on model weights, context size, runtime, and other work on the host. GPU memory and disk space also affect which setup fits.

If privacy motivates local inference, read the provider’s data and retention terms too. Local processing can keep model prompts on the host, but the agent may still connect to chat services, external websites, or other configured tools. The OpenClaw security guide recommends defining the trust boundary and tool access for the complete deployment.

Check the workload before comparing Mini PCs

Write down the exact tasks the agent should handle. A short personal assistant prompt and a long tool-enabled conversation have different context needs. Local model recommendations also vary by runtime and quantization, so compare specifications for the selected model rather than relying on a generic “AI-ready” label.

OpenClaw’s local-model documentation describes an 8 GiB host-memory floor for its smallest curated recipe. That is a minimum for that setup, not a general recommendation for every model or a promise that the system will respond quickly. The same page says larger recipes need more memory and that the setup checks available RAM, GPU memory, and disk space before downloading.

When you compare two Mini PCs, check these items against the current model guide:

  • Memory: system RAM and, for GPU inference, available video memory.
  • Storage: free space for the operating system, downloaded model, prompt history, logs, and updates.
  • Cooling: whether the case can sustain the workload without excessive heat or noise.
  • Network: stable access to your model provider and messaging channels.
  • Power behavior: whether the device resumes after a power outage and remains awake when the Gateway needs to receive events.

Do not compare a model that merely loads with one that completes a real agent turn. The OpenClaw guide recommends testing actual tasks before making a local model the default. Run the intended prompt, let the agent call the tool you plan to enable, and review the response quality and timing.

Buy for the chosen model, not an “AI PC” label

If the test uses a hosted provider, focus on reliable storage, network access, memory for OpenClaw and its tools, and a power setup suitable for a server. Ask the seller for the exact RAM and drive configuration because a Mini PC model name can cover multiple configurations.

If the test uses local inference, choose the provider and model first. Then compare that model’s current requirements with the hardware and leave capacity for the rest of the system. A GPU only helps when the runtime can use it and the model fits in its memory. Check compatibility and supported backends in the model documentation before paying for a graphics upgrade.

Once the device is running, keep its Gateway private and restrict who can send it commands. The home-server OpenClaw checklist covers that baseline, while our local-model memory checklist explains the workload side. If you are selecting the device for a team, decide first whether everyone belongs in one OpenClaw trust boundary.