Hey everyone!

Recently, I released a blog on how to setup a cluster out of your Mac Minis for distributed training and inference

Now its time to do the same with Raspberry Pis!

Why Raspberry Pis?

quite cheap (30-50 dollars) easy to use full blown OS the size of a credit card (small enough for edge projects)!

This is a part of my current series where I’ll be releasing blogs and guides around learning distributed learning and building your own small compute clusters.

The goal is simple: help more people get started with running and training AI models using the hardware they already have lying around. Old laptops, MacBooks, Mac minis, Jetson Nanos, Raspberry Pis, even phones and tablets.

Distributed learning often feels intimidating from the outside, but it’s genuinely one of the coolest areas in systems and AI once you start playing with it yourself.

Before we get into the fun stuff like distributed inference and training, the first few posts will focus on setting up hardware properly and building a working cluster environment, basically subtle amount of cabling and networking!

The early guides will specifically cover setups around:

• MacBooks and Mac minis (Done!)
• Jetson devices
• Raspberry Pis (This one hehe)

After that, we’ll move into quick demos (smolcluster ) , and gradually learn the fundamentals side-by-side while actually running models across devices.

I’m building this alongside smolcluster, so a lot of the content will stay very hands-on and practical instead of purely theoretical.

Hopefully this helps more people realize that distributed AI systems are not something reserved only for giant datacenters anymore.

There is just one question I want to answer: are heterogenous clusters, like what I am trying to make above, even possible for running models?

Well, we'll know and till then do read me blog and let me know what you all think! Any comment, feedback etc are very welcome. (pls be gentle since its my first time writing one all by myself haha)

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