 | Hey everyone! Recently, I released a blog on how to set up a cluster out of your Mac Minis for distributed training and inference Now it’s time to do the same with Raspberry Pis!
This is 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 a subtle amount of cabling and networking!
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’m trying to make possible for running models?
Hail LocalAI! PS: All this is for educational purposes only and not meant for getting performance at par with dedicated GPUs…well not that I have figured out a way to do it yet. Please use these guides and information you’ll get to learn the basics of how distributed learning is done! Thanks |
Key Takeaways
- The author is releasing a series of blogs on setting up small compute clusters for distributed AI training and inference.
- Early guides will cover setups with MacBooks, Mac minis, and Jetson devices, followed by Raspberry Pis.
- The goal is to help more people get started with running AI models using existing hardware like old laptops or smartphones.
- The author notes that distributed learning can be intimidating but becomes very cool once you start playing with it.
Originally published at reddit.com. Curated by AI Maestro.
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