I’m beginning to challenge the assumption that datacenters are more efficient. I can get the same computing power out of a single Mac Mini 32 GB that I get from from an AWS virtual machine that costs hundreds of dollars per month. Even compared to cheap baremetal providers like Hetzner, the Mac Mini pays for itself in a few months of cloud costs. How exactly are datacenters more efficient? I don’t see it in the price. It may be the costs of centralizing large amounts of compute actually make it more expensive, not less, when accounting for profit margins, and considering the fact that base infrastructure (power, internet) is a given in every home anyway.

There are huge hidden costs in datacenter prices that are simply unnecessary for most casual users of compute. Salaries of staff to maintain datacenters, redundancy and high availability of nine 9s that are simply not required by most customers, as well as real estate costs are all non-existent costs in a homelab setup because those are living costs you pay for anyway, with or without a home server.

>I can get the same computing power out of a single Mac Mini 32 GB that I get from from an AWS virtual machine that costs hundreds of dollars per month.

This quickly breaks down when you're talking about large models that needs terabytes of memory to run[1]. There's no way that you're going to be able to amortize that for a single person.

[1] https://apxml.com/models/glm-51

The comment is about smaller models

Right, but what are you going to do with small models? If your time is worth anything at all you'd pay for the $100 claude code/codex pro subscription, rather than fumbling around with the models quantized enough to fit on your mac.

If you're building agentic processes (harnesses) for business processes local models are a great way to do that, while keeping your data, and any personal data, private.

If you're vibe coding a codex/claude subscription makes more sense as a more polished experience.

I don't vibe code, but I use self hosted models with codex for code review and snippet generation.

If small models keep improving for specific purposes and larger models have diminishing returns, then what?

E.g. I can see a world where you have a local model that is specialised just for producing code.

$100 isn't going to buy you much access to claude code when they start charging a profitable fee for using it.

Author here. The reason I wrote that local hardware is "sipping power most of the time" is because most of the time it's not doing LLM-related work. If you're just using your local machine (or eventually maybe even your phone) to do local LLM tasks, you're not doing that all day.

I agree that data centres will be set up to be more efficient, but we're also going to need fewer of them if local LLMs take off. If that's true, overbuilding data centres is more revenue pressure for AI companies.

Electricity is more expensive at home than where data centers are built, batch inference is more efficient at GPU/TPU inference per watt, power supplies in data centers are more efficient than in average consumer devices, entire racks can be fully powered off when not in use vs. standby power consumption, and of course the investment in hardware is amortized across many users in data centers. It allows more people to have access to larger models than everyone buying an M3 Ultra.

The economy of scale that data centers have is actually a good thing economically and environmentally for many kinds of demand.

I think that the most capable models will continue to be in high demand across the market until at least "a datacenter of PhDs" level of capability. At that point I can see a transition to more local model use if affordable consumer hardware is available (for the median human on Earth). If that turns out to be true then the hyperscaling will plateau at the level allowing sustained commercial/industrial "PhD"-level demand which we aren't at yet (all providers are still struggling to meet current demands).

What I was commenting on was the concept that a small model at home is somehow more efficient. To make a reasonable and fair comparison you would compare many people running a small model at home vs those same people using what would likely be a shared resource in a datacenter.

The core concept is that tokens/watt is tokens/watt ( for a given model of course ). A computer at home is actually less efficient overall because most of the time it is not doing tokens but still using a small footprint of power.

The revenue pressure is an interesting problem , but I suspect the actual demand math will be much more complicated.

I find local models interesting for sure, and run several on my own personal DGX cluster. I am however most certainly not power efficient!

Fully agree with you, smaller model are great for some tasks but the security concern on injection prompts etc is what really makes it for me. Great to run offline tasks etc, but whenever interacting outside the local network I still run Claude or ChatGPT depending on the task

It's technically odd and illogical, but practically probably correct and on-the-money, as the companies try to artificially create demand?