[echelon]

We don't need rinky-dink RTX models that budget VRAM.

We need large scale open weights models just as capable as what's at the frontier.

And we need the ability to rent compute and spin up the weights easily. One-click, easy enough for anyone. Easier than nerd tools like ComfyUI, Claw, and node graph garbage.

Freedom is owning very large scale weights. Anything less is subsistence.

[ktallett]

We need to improve the waster and energy usage and this method doesn't. Most are not reinventing the wheel, a shared AI repository, communicated between online local computers would save a lot of need for these large models.

[simonw]

I'd love to see credible numbers on the energy usage of thousands of people running models on their own devices compared to sharing data center resources to run big models that serve many different people at the same time.

My hunch is that the energy/water usage of the data centers is a whole lot more efficient than everyone running at home, but I'd be interested in seeing real data on that.

[Windchaser]

Water usage goes up with data centers because more cooling is needed when you run the hardware harder.

So: if you're running the models on your own machine, presumably you're not running them as often, and air cooling is sufficient. But, at the same time, this is less efficient in terms of hardware use; the data centers need water cooling specifically because they're getting more bang from their buck from their hardware, by running their hardware harder.

So that's the tradeoff: more hardware-use efficiency means more water usage.

[echelon]

Almonds and avocados use two orders of magnitude more water.

You can start adjusting your consumption of those products.

[nok22kon]

that water is recirculated. nobody sends it down the drain after one loop

[verdverm]

With hardware like the Spark and Strix, the water usage is known to be zero, yea?

On the energy front, I assume less efficient, but I also think there is a tradeoff in efficiency versus freedom, that's why I have my own hardware.

[simonw]

The electricity used by hardware itself consumes water. When people talk about data center water usage they're often also including the water used in electricity generation.

[verdverm]

Ironically, 77% of my electricity comes from flowing water rather than the boiling it, ~90% renewable overall

Once we get to fully renewable (as a country), there should be no water involved in electricity generation as well.

[fluoridation]

All consumer hardware (not counting XOC) uses either air cooling or closed-loop liquid cooling, so the water usage is zero, always. Power is a little trickier. I'd assume it's less efficient, but also the total usage is less, because the user sometimes turns the machine off, and the hardware idles to a deeper sleep state than server hardware.

[cold_harbor]

the comparison misses that local LLM usage covers tasks you'd never send to an API — private code, offline work, medical notes. the baseline is 'local vs not-doing-it', not 'local vs cloud'

[simonw]

I have bad news about my private code and medical notes...

[Chu4eeno]

Looking forward to some pre/non-finetuned frontier model to leak, and people to start completing medical notes.

[echelon]

> Looking forward to some pre/non-finetuned frontier model to leak

I'm looking forward to this too. It'll be incredibly useful.

[echelon]

NO!

This is the wrong approach that will turn us into serfs. We need big honking models that do what the leading foundation hyperscaler models do to within a few percentage points of measured performance.

The small-scale models are not productive, and the duct tape solutions built on top of them are hobbyist-tier "year of Linux on desktop" toys.

I imagine fedora-wearing, crypto-shilling, coupon-cutting boffins every time I see small weights thing lauded as the future. This is the Pine Phone F-Droid of AI.

"SMS works most of the time on my phone, I swear! I don't really need my banking app!"

That is not big model energy.

Nothing outside of the top ten is worth spending any time on, and we need to focus on models that bridge the gap.

You're talking about impractical toys for highly technical people wasting their own time. That doesn't move the needle or have any economic impact on the competitive landscape.

We need sharp teeth that bite at the legs of the top-tier foundation labs and hold them back from running away with the prize.

We've been through this time and time again over the last thirty years. It's the same shaped problem as before. We don't need toys - we need real infra for real people paying money to do work. Not freeware for freeloaders who don't spend and invest in the problem space.

Large models fit that precisely, because it forces investment into a wide variety of open infra, routers, inference engines, etc. Not to mention the weights ecosystem itself.

[ktallett]

Firstly, unless you are the leader of any of the faangs, you are a serf on the whole, if you believe in that philosophy as being relevant.

We need the right tool for the job. Certain models have minimum energy expense no matter what the task is and that's often wasted, both on the scale of some tasks and also repetition.

There is a place and a need for large models, local models, and single purpose models. The same way there is a need for HPC and single board.

[echelon]

> if you believe in that philosophy as being relevant.

If you tell the open weights labs that you want tiny models, you'll get more shitty tiny models.

If tell them you don't want tiny models and you want big models, they'll change their focus.

> We need the right tool for the job.

The world has enough shitty tiny models. You can frankly distill your own without wasting the time of foundation labs.

> energy expense

You are so far removed from the discourse that I think I'm wasting my time talking to you. This is not about energy in the slightest.

> There is a place and a need for large models, local models, and single purpose models.

This is where 99.999% of the demand is. And if you look, you'll notice that most of the token usage is accruing to hyperscalers because the market isn't meeting this demand with enough alternatives.

The robotics industry will produce energy efficient models. Let them be the ones to press for that. Stop demanding cheapo LLMs, image, and video models. Big is all that matters in this domain.