Hardware for AI Agents

This Isn't a Laptop You Close at Night

Standard advice for buying a computer optimizes for portability, screen quality, or raw benchmark performance. None of those things matter much here.

An agent stack runs continuously. It handles scheduled jobs at 3 AM. It processes incoming messages while you're in a meeting. It runs research tasks in the background while you do other work. The machine needs to handle sustained, mixed workloads — not just occasional bursts — without becoming a heat source, a noise problem, or a power drain you notice on your bill.

That's a different set of requirements. And it points to a different kind of hardware.

Why We Run on Apple Silicon

Our deployment runs on a Mac mini. It sits in a bedroom. It has been running for months without interruption.

It has never once been audible.

That's the detail that matters most and gets mentioned least in hardware comparisons. When your infrastructure runs 24 hours a day, silence isn't a luxury — it's a requirement. A machine that spins up fans under load is a machine that degrades your environment every time it works hard. Apple Silicon was engineered to avoid exactly that.

The technical reason is the architecture. M-series chips use a unified memory design — the CPU, GPU, and Neural Engine share the same high-bandwidth memory pool instead of passing data between separate chips. For AI workloads, which constantly move large amounts of data between compute units, this is significantly more efficient than conventional designs. The result is real performance delivered with less heat and less power draw.

In practice: it runs serious AI workloads continuously, stays cool, stays quiet, and draws around 10–20 watts under typical load. A comparable x86 machine doing similar work would draw 3–5× more power and would need active cooling to manage the heat.

The Honest Comparison

OpenClaw runs on Mac, Linux, and Windows. All three work. But they're not equivalent for this use case.

Platform	Verdict	Notes
Apple Silicon Mac Recommended	Best overall	Silent, efficient, reliable. Best local inference performance per watt. Lowest friction for setup and maintenance. What we deploy on.
Linux Viable	Good for experienced users	Legitimate option, especially on a dedicated server or VPS. More configuration overhead. Things that just work on macOS sometimes require troubleshooting. Better budget option if you're comfortable in the terminal.
Windows Works	Adds friction	OpenClaw installs and runs. But comparable hardware runs hotter and louder, and Windows is a less natural environment for the command-line tooling this stack depends on. More background maintenance. Not what we'd choose.
GPU Rig Specialty	Overkill for most	If you're running very large local models at scale, there's a case for dedicated GPU hardware. For the workflows most small teams care about, a Mac mini outperforms anything in its price range — without the noise, heat, and power cost.

Why This Hardware Is a Long-Term Bet

The direction of AI development is toward local compute. That's not a prediction — it's already happening.

Models that required a data center two years ago now run on a laptop. Models that required a high-end GPU last year now run on a Mac mini. The trajectory isn't slowing — it's accelerating, and the efficiency curve is moving faster than the capability curve. Every year, meaningfully more capable models fit into meaningfully less compute.

What this means practically: the agents running on cloud APIs today will increasingly run on your own hardware. Faster. With no per-token cost. With no data leaving your machine at all. Apple Silicon is already capable of running serious local models — Llama, Qwen, Phi, Mistral — at speeds that are genuinely useful for production work. The M4 Pro has enough unified memory to hold models that would have required a dedicated GPU rig a year ago.

Every generation of Apple Silicon meaningfully increases what's possible locally. The machine you buy today will run models next year that don't exist yet — models substantially more capable than what's available now. You're not buying hardware for today's workload. You're buying into a compute architecture that is scaling in exactly the direction this technology is heading.

When local inference catches up to cloud inference — and it will, for most tasks — the operators already running local infrastructure will have a real advantage. The hardware will be paid for. The system will be configured. The only thing that changes is the model you point it at.