Best Hardware for OpenClaw in 2026 — Mac Mini vs Jetson vs Raspberry Pi

If you've decided to run OpenClaw as your self-hosted AI assistant, the next question is obvious: what hardware should you run it on?

I spent the last few months testing OpenClaw on everything from a Raspberry Pi 5 to a Mac Mini M4. Here's what I learned about the best hardware for OpenClaw — and why there's no single right answer.

Why Hardware Matters for OpenClaw

OpenClaw isn't just a chatbot. It orchestrates browser automation, manages multiple messaging channels (Telegram, WhatsApp, Discord), runs local LLM inference or proxies to cloud APIs, and handles real-time tool calls. That means your hardware needs to:

• Stay on 24/7 (it's an assistant, not an app you open)
• Handle concurrent I/O without choking
• Optionally run local AI models for privacy
• Be quiet and power-efficient enough for a desk or shelf

Let's look at the openclaw hardware requirements across four popular options.

Option 1: Raspberry Pi 5 (8GB) — ~€95

The Pi 5 is the cheapest entry point. With its quad-core Cortex-A76 and 8GB RAM, it can technically run OpenClaw's core services.

Pros:

• Dirt cheap
• Huge community, tons of accessories
• Low power (~5-10W)

Cons:

• No GPU acceleration — forget local LLM inference
• SD card I/O bottleneck (NVMe HAT helps, but adds cost)
• 8GB RAM is tight once you add Node.js, browser automation, and a database
• Thermal throttling under sustained load

Verdict: Good for experimenting. Not great for daily-driving OpenClaw with browser automation and multiple channels. If you're only proxying to cloud APIs (OpenAI, Anthropic) and running light workloads, it works — but you'll feel the limits. For a deeper comparison, check out the Raspberry Pi vs Jetson breakdown.

Option 2: Mac Mini M4 — ~€650+

The M4 Mac Mini is a beast. Apple Silicon's unified memory architecture, hardware media engine, and single-thread performance make it arguably the best consumer hardware for running AI workloads.

Pros:

• Incredible single-thread performance
• 16GB+ unified memory — great for local models
• macOS ecosystem, polished experience
• Quiet, compact, beautiful design

Cons:

• Price — €650 for the base model, and you probably want 24GB RAM (€880+)
• macOS quirks with headless operation and automation
• Overkill if you're not running large local models
• Not designed for 24/7 embedded/server use

Verdict: If budget isn't a concern and you want to run 7B-13B parameter models locally, the Mac Mini M4 is hard to beat. But for many OpenClaw users, it's more machine (and more money) than necessary. Looking for a more affordable path? See the Mac Mini alternative guide.

Option 3: Generic x86 Mini PCs — €150-400

The N100/N305 mini PCs flooding Amazon and AliExpress are surprisingly capable. You get an x86 platform with 16GB RAM, NVMe storage, and decent I/O.

Pros:

• Good price-to-performance ratio
• Standard Linux support
• Enough RAM for OpenClaw + light local models (quantized)
• Many options at every price point

Cons:

• No dedicated AI accelerator
• CPU-only inference is slow for anything meaningful
• Build quality varies wildly
• Fan noise on cheaper models

Verdict: A solid middle ground if you want standard Linux compatibility and don't care about on-device AI inference. Pick a fanless model with 16GB RAM and NVMe, and you'll have a reliable OpenClaw host.

Option 4: NVIDIA Jetson Orin Nano (ClawBox) — €399

This is what I personally run. The ClawBox is an NVIDIA Jetson Orin Nano packaged with a 512GB NVMe SSD and OpenClaw pre-installed.

Pros:

• 67 TOPS of AI compute — run local models with actual GPU acceleration
• 15W power consumption, completely fanless
• OpenClaw pre-installed and pre-configured
• Compact, silent, runs 24/7 without thinking about it
• CUDA ecosystem for future AI workloads

Cons:

• ARM64 — some x86 software won't run (though most server stuff works fine)
• 8GB unified RAM shared between CPU and GPU
• NVIDIA's JetPack ecosystem has a learning curve
• Less community support than Raspberry Pi or x86

Verdict: The sweet spot if you want local AI inference without Mac Mini prices. The 67 TOPS of dedicated AI compute means you can actually run quantized models on-device, and 15W means your electricity bill won't notice. The pre-installed OpenClaw setup means you're literally up and running in minutes.

The Comparison Table

My Recommendation

Here's how I'd break it down:

Choose the Raspberry Pi 5 if you're experimenting, learning, or only using cloud AI APIs. Budget-friendly and fun to tinker with.

Choose a Mini PC if you want standard x86 Linux, have existing infrastructure, and don't need local AI inference.

Choose the ClawBox if you want a dedicated, silent, always-on AI assistant with actual GPU acceleration at a reasonable price. It's the device I reach for when people ask me "what should I buy to run OpenClaw?"

Choose the Mac Mini M4 if budget isn't an issue, you want the most powerful local inference, and you're comfortable with macOS.

For a full breakdown of what OpenClaw needs to run smoothly, check the hardware requirements page.

Final Thoughts

There's no single "best hardware for OpenClaw" — it depends on your budget, your use case, and whether you want local AI inference. What I will say is: don't overthink it. OpenClaw runs on anything from a Pi to a workstation. Pick what fits your life, plug it in, and start building your AI assistant.

The hardware is the easy part. The fun part is what you do with it.

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Have questions about hardware compatibility? Drop a comment below or check openclawhardware.dev for detailed specs and benchmarks.