Since agentic AI models run in the cloud, the local hardware running the agent's orchestration layer (API calls, tool use, task planning) can be minimal — possibly small enough to replace a cloud VPS.

0:52on a VPS instance in the cloud. Then Qualcomm offered to send me one of their Rubik Pi 3 development boards, which got me thinking. If the models are already running in the cloud, how much hardware does the agent itself really need? Could something this small replace a $28 per month VPS? Let's find out. The Rubik Pi 3 is a tiny single-board computer built around Qualcomm's Dragon Wing platform. It

1:16features an 8-core ARM CPU, 8 GB of RAM, 128 GB of onboard storage, gigabit networking, USB 3, and a dedicated AI accelerator. While that accelerator is one of the things that makes this computer special, for the purposes of this experiment, I'm not really using it. Remember, the models are remote. What I care about is whether this tiny machine is powerful enough to act as the orchestration layer for an AI agent.

1:42One thing I wasn't expecting was how easy it was to get up and running. The Rubik Pi 3 ships with Linux pre-installed, though I did decide to reflash it with Ubuntu Server using the Qualcomm Launcher utility, just so I can be sure for I was working with. From there, getting Open Claw running was pretty straightforward as well, using the official installer from the Open Claw website. To be honest, I've spent far more time

2:06fighting with Digital Ocean's so-called one-click Open Claw droplet when I went that route than I did getting the OS and agent up and running on the Rubik Pi. Once I had the agent up and running, it was time to test. I wanted to see how this hardware would support the typical kinds of tasks that I use agents for. For my testing, I came up with a short

2:25research question and a more involved request that would require that the agent write some code. The short task asked the agent to summarize the last three Open Claw releases from the GitHub releases page. For the more involved task, I asked it to look at my Hacker News favorites, infer the kinds of stories that I'm interested in, then build a small skill and tool to fetch the Hacker News front

2:48page, identify the posts I might care about, summarize them, and send me a briefing. This is a typical content collection task that I and many others have scheduled to run daily on Open Claw. So, what did I find? Well, first I want to caveat this by saying before you do that I'm sharing anecdotes here and not formal benchmarks, so don't yell at me in the comments. But, I found