You Ask, I Answer: How to Build a Cheap AI Sidekick Box?

In today's episode, Leslie asks how do I build a cheap second box, a secondary computer, to do AI tasks so um so that I could free up my primary computer. Here's the challenge. The words cheap and AI don't go very well together these days, and a big part of that is because of inference costs of compute costs. Every vendor on the planet that makes AI tools has vacuumed up all of the graphics processing units and chips that you would normally build this kind of box at home with. So it's very, very expensive.

So the big here's the big question: do you want to do inference at home? Meaning that the AI model runs on your hardware, or do you want just a separate orchestration machine that is cheap that connects to a cloud provider of some kind to do the inference there? And that comes down to your budget. The gold standard right now for inference boxes is something like a DGX Spark or an ASUS GX10. That's gonna run about $4,000 US.

And it consumes a lot of power, so there'll be like 200 watts of power that be added to your power bill too. But that allows you to run completely self-contained, totally independent of the cloud, and you can run really great models like Quen 3.6 for that. That is not cheap. $4,000 is not cheap. There's in a world where $4,000 is like, oh yeah, I'll just uh at least for the people I hang out with, that is not cheap.

I'm sure if you hang out with billionaires, they're like, oh yeah, that's super cheap, I'll buy high. No, no, I don't I don't have those friends. If you want to spend less than a thousand dollars or less than 500, you cannot do inference at home. Period. End of story.

Um you could absolutely get a super inexpensive box like a Raspberry Pi 5 for like a hundred bucks or 200 bucks. Set it up as an orchestration computer that runs something like Ubuntu Linux and connects to a cloud provider, something like Grok with a Q, Cerebrus, Deep Infra, all these different AI companies that have hardware of their own, and many of them have what are called zero data retention APIs. These are APIs that do not keep copies of stuff. So once they've done the inference, they've returned the result to you, they no longer have a record of it. And so, from a privacy preserving perspective, those are good options for things that are not truly sensitive data.

That for under $500 is going to be your best bet. If you spend $150 on a Raspberry Pi, get it configured, get it on your network, and that's going to consume like 13 watts, right? It's very, very, very low power. That basically acts as an orchestration machine that you can then connect via you know the various different connectors or or services to your inference provider, you end up with kind of the best of both worlds. So it all comes down to the level of privacy that you expect to have.

If you do not expect any level of privacy, great. Possibly even for you know you spend the money on the box, get Linux up and running, and you don't spend any money on it. Um my suggestion would be to also take a look at uh companies that offer coding plans. So a uh inference provider like DeepRinfra or whatever is all gonna be pay as you go, and depending on the model that you use under the hood, that can be you know anywhere from 10 cents per uh per you know million tokens in and 50 cents per million tokens out, all the way to like $3 each way if you're using something like GLM5. Though if you use a coding plan, something like Minimax's M3 coding plan out of their Singapore data center, then that acts more like something like a clawed code or clawed co-work max subscription where you get a certain amount of usage every month, and that usage you get is billed at a flat rate.

For an individual, that's a great plan. So I would take a look at again if they're using non-sensitive data, something like Minimax's M3 coding plan is a is a terrific option. But your data is not super private there, right? So uh they do not have a zero data retention plan for that. But it is um the data is being processed in a jurisdiction, like unless you live in Singapore, uh, your data is being processed outside of the jurisdiction you live in.

And that sometimes is enough. Uh particularly if the the jurisdiction of the provider is not friendly with the jurisdiction of the place that you operate in, so they might not be as inclined to share their data, uh, even if served a lawful warrant, assuming that the your your local law enforcement even knows how to do that. So those would be those are like the the two different options. Cheap box as an orchestrator, plus a cloud plan, which it can either be pay as you go, or token plan, or expensive box that is entirely self-contained, all depends on your needs. Thanks for the question.

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