On paper, building a cluster feels powerful. More machines. More compute. More scale. In reality, it’s more cables, more configuration, more edge cases, and more things that can go wrong - multiplied by the number of nodes you have.
The physical side alone is non-trivial. Power, cooling, networking, rack space, cable management - everything becomes more complex the moment you move from one machine to several. Instead of solving one problem, you’re solving the same problem four times. And if you misconfigure something? Congratulations, you misconfigured it everywhere.
But the real friction isn’t hardware. It’s software.
Especially in the AI space, clustering tools are in an awkward phase. Enterprise-grade solutions assume you have massive bandwidth, specialized networking, and serious infrastructure budgets. Hobbyist solutions assume you have unlimited time and patience to debug obscure issues. There’s very little in between.
And with AI workloads, you’re often dealing with bleeding-edge software. That means quirks, unstable behaviors, undocumented edge cases, and a lot of trial and error. Fixing something once is annoying. Fixing it across multiple machines gets old very quickly.
That’s why I don’t recommend clustering unless:
- You’re explicitly trying to learn distributed systems.
- You need it for professional reasons.
- You genuinely require horizontal scale.
If you’re just experimenting at home or trying to run large models locally, clustering can turn a fun project into a frustrating one. And once it stops being fun, what’s the point?
For most people, vertical scaling still makes far more sense. Buy the most capable single machine you can reasonably afford. Max out RAM. Keep it simple. One box, one OS, one place to debug. The performance gains from adding nodes are rarely linear, and diminishing returns kick in faster than people expect.
Also, let’s talk about the “AI” branding everywhere. Just because a computer has enough RAM or a decent GPU doesn’t magically make it an “AI machine.” That label is mostly marketing. We’ll probably look back at this naming phase the same way we look at “multimedia PCs” from the 90s.
One more thing that matters to me: independence. If I test hardware, I don’t agree to content control. Embargo dates are fine. PR clarifications are fine if I made a factual mistake. But editorial control is not. Trust is the only real currency in public technical work. Without it, none of this is worth doing.
If you have serious budget and want to run large models locally today, get the most powerful single machine you can - something like a maxed-out Mac Studio with massive unified memory. It’s quiet, compact, and dramatically simpler to manage. Only start thinking about clusters once you’ve genuinely outgrown a single node.
Clustering absolutely has its place. But it’s not the default answer. Not for most people. Not yet.
And definitely not just because it sounds cool.