Clusters become personal (like PCs did)

The article itself reminds me of the enthusiasm I felt for plan9 when I first heard about it back in uni. I also thought everyone should have their own compute grids and that clustered computing was the future; of course now I realize there's a lot of reasons why that doesn't actually work. Considering this appears to be a start-up ad, I hope the author knows something I don't.

Uptime, self healing, reproducibility, separating the system from app. There's probably a half dozen more.

K8s comes with resource consumption tax certainly but for anything beyond the trivial it's usually justified.

> Separate VM's for different apps works well for isolation

Sounds inefficient along with a lot more work doing the plumbing than simply writing a 100 lines of yaml.

The "cooperative task" they're engaged in is just, broadly, meeting your needs, whatever they are.

The isolation is a desirable property, and I agree this is much preferable to a highly inter-coupled bunch of machines, and also that thia stretches the typical sense in which we refer to a "compute cluster", but I don't think it's an entirely invalid framing of the term.

Maybe they’re assuming some massive amount of compute will be necessary for future tasks? Self hosted LLMs? I’m currently finding it difficult to come up with more uses for my vps beyond hosting trillium and some personal applications I’ve made

A big advantage of clusters, and horizontal scaling in general, is the ability to easily dynamically scale to meet demand.

If you're running a system on a single machine that has N GB of memory and you need to scale to N+1, what do you do? Provision a new machine and migrate everything over?

No-one operates online real-time systems like this. Clusters make it much easier and less expensive to handle this.

On top of that, it's probably true that in some pure numerical problem-count sense, "most problems" don't need a cluster, but that's misleading. It's like saying "most businesses are mom-and-pop shops." Perhaps true, but it ignores hundreds of thousands of larger businesses, or even small business that have big data needs.

There are plenty of problems that involve large amounts of data, and that's increasingly true with ML applications.

I'm at a company of ~100 people which you've probably never heard of (classified as a "small" company in government stats, so not included in the hundreds of thousands figure I mentioned above.) We have 1.9 PB of data for our main environment. When we run processes that deal with it all, the clusters scale to thousands of vCPUs and tens of terabytes of RAM.

Several processes that run daily scale to 500+ vCPUs and many TB of RAM. For the latter, the data itself could probably fit in RAM on a humongous machine, but the CPUs wouldn't fit on a single machine. And we'd have to size the machines carefully every time we start them up. Clusters can scale up dynamically according to the demands of the jobs they're executing.

10Gbps is now very cheap and 100Gbps is viable at hobby scale. That's Ethernet. I don't know anything about CXL and so on.