The Minimum Viable AI Agent Server

Key Takeaways

When people talk about AI and compute nowadays, that conversation is almost entirely about GPUs and accelerators, but I think that's going to change. Don't get me wrong, accelerators are important because frontier LLMs require enormous amounts of a very specialized kind of compute when they're doing a very particular thing, and that's inference. But when it comes to using AI agents like Open Claw and Hermes for personal or work productivity, we're usually using models hosted in the cloud, and the inference is frankly somebody else's problem. The agent itself or the agent framework or harness is typically focused on a different kind of heavy lifting. It's spending its time orchestrating tasks like calling APIs, searching the web, coordinating tools, running shell commands, or even just waiting on external systems. Until now, I've mostly treated my Open Claw agents like any other server workload and stuck them up on 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 features 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. One 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 fighting 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 research 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 page, 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 the results interesting nonetheless. Let's take a look at a run of the short task on my two Open Claw agents, Ace, which is the Digital Ocean droplet, that's a virtual machine with two virtual CPUs and 4 GB of RAM, and Thunda, which is running on the Rubik Pi. >> [music] >> If you missed the admittedly terrible pun here, let me explain. Thunda is named after Thunder Comm, which is the company that produces the Rubik Pi. Thunder Comm itself is a pretty punny name as the company is a joint venture between Thundersoft and Qualcomm. Enough of that. As you can see, Ace finished the task in about 29 seconds, while Thunder finished it in 32 seconds. Technically, that's about a 10% difference, which is significant. But, in practice, it wasn't really noticeable. More importantly though, I found that the variation in how the agent approached the task from run to run contributed way more significantly to run times than the differences between the machines. For example, in early versions of the simple research task, the agent tried to figure out the latest Open Claw releases by inspecting the local Open Claw source code. Needless to say, that took a lot longer than just looking at the releases page. The more complex task made this even clearer. In one run, the Rubik Pi agent finished in 1 minute and 7 seconds, while the VPS took 1 minute and 47 seconds. In another, the Rubik Pi finished in 1 minute and 39 seconds, while the VPS finished in 1 minute and 18 seconds. So, my takeaway was not that one machine was clearly faster than the other. It was that, as was the case with the simpler task, the difference between the Rubik Pi and the VPS was mostly in the noise. The bigger differences came down to what the agent decided to do. In one run, the VPS tried to use the browser, which was way slower than direct web fetches. In another, it found an online database of Hacker News posts and skipped some of the steps entirely. Meanwhile, in some runs, the Rubik Pi did more planning and even spun up sub-agents to write the code. A more rigorous approach to benchmarking would require a lot more runs and perhaps rejecting outliers, but it didn't really take that for me to see that the Rubik Pi is competitive to the VPS that I've been running for months. That said, there's one thing I need to be super clear about. If you're comparing the Rubik Pi and the VPS in isolation, they're roughly equivalent for these kinds of workloads. But, if you're like me and you're used to interacting with ChatGPT or Claude or Gemini, then waiting 30 seconds for a simple task can feel pretty slow. So, I ran the same release summary task directly in Chat GPT using a comparable model. The result came back in 12 seconds on both an M4 Max MacBook Pro and an iPhone 16 Pro. That's a pretty significant gap. Given everything we've talked about, that shouldn't be too surprising. First off, those are substantially more expensive devices. But, hardware is only part of the story. I suspect that Open Claw itself accounts for a significant portion of the difference. Open Claw is a general-purpose agent framework with lots of hooks, abstractions, and layers designed for maximum flexibility and extensibility. While Chat GPT is a closed system that is heavily optimized for performance and responsiveness. So, while this experiment started as a hardware comparison, it's clear that the software stack plays a huge role in the overall experience. Another thing that this experiment really highlights for me is why people mostly talk about vibes when evaluating agents. Real benchmarking of these systems is hard. In this case, the biggest source of variation wasn't the thing I was trying to investigate, the hardware, but rather it was the agent itself. Different tool choices, different plans, and different execution paths often had a bigger impact on runtime, a much bigger impact on runtime in fact, than whether the task was running on a VPS or the Rubik's Pi. And beyond the agent, at least for these workloads, the harness matters just as much as the hardware. So, where does this leave me? For me, there are three practical takeaways. First, I'm probably not switching to a setup like this for my everyday interactive AI usage. For that, running Chat GPT, Claude, or Gemini directly on my laptop or phone is simply faster and more convenient. That said, I do see a lot of value in systems like Open Claw for asynchronous workloads, things that run on schedules, things that run on triggers, things that gather information, summarize content, or perform background automation while I'm doing something else. For those kinds of tasks, responsiveness matters much less, and the Rubik Pi performed pretty well. In fact, based on what I've seen so far, I will be shutting down my $28 a month VPS and running my Open Cloud style agents on the Rubik Pi. A Rubik Pi costs about $280, so figure a 10-month payback period, which isn't too bad. Finally, this experiment raised at least as many questions as it answered. I'm particularly interested in comparing different agent frameworks and understanding where the real bottlenecks are. If that's something you'd like to see more of, let me know in the comments. Hope you found this useful, and I'll see you in the next one.