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Cake day: August 24th, 2023

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  • The power usage isn’t even that much on an individual basis once it’s trained, it’s that they have to build these massive data centers to train and serve millions of users.

    I’m not sure it’s much worse than if nvidia had millions of customers using their game streaming service running on 4080s or 4090s for hours on end vs less than an hour of AI compute a day.

    It’d be better if we could all just run these things locally and distribute the power and cooling needs, but the hardware is still to expensive.

    You have apple with their shared GPU memory starting to give people enough graphics memory to load larger more useful models for inference, in a few more generations with better memory bandwidth and improvements, the need for these data centers for consumer inference can hopefully go down. These are low power as well.

    They don’t use CUDA though so aren’t great at training, inference only.







  • Most services today have user accounts so they can store your information, that way if you get a new phone for example, you can just sign into a new device and your content is there. So the maps, your configurations etc, are all sent to a remote server so it just works.

    Having to manually connect to the robot each time, when you might not even be in the same country when you want to connect, doesn’t work if it’s all done locally.

    Companies that want to offer connected services like this but are privacy centric could let you encrypt it before sending it so they can’t see what the contents are, but then you need to manage the idiot populace and their inability to properly secure this kind of information. Then, you start getting all sorts of support calls like I lost the encryption key and now I can’t access my map! What kind of stupid service makes me remap things when I change phones and forget something!?

    So there’s no really getting around sending some data back to the server, but even IF they have it unencrypted, there’s no reason other than corporate profit taking to also be using that data. They don’t have to, but they will and do.





  • Australia isn’t the greatest spot to run a data centre in general in terms of heat, but I do understand the need for sovereign data centres, so this obviously can’t work everywhere.

    What makes you think $3.5 million can’t be profitable? A mid sized hospitals heating bill can get into the many hundreds of thousands or into the millions even. Especially if it’s in a colder environment. A 5-6 year payback on that wouldn’t be terrible and would be worth an upfront investment. Even a 10 year payback isn’t terrible.

    These colder locations are the ideal locations for the data centres in the first place because they generally want a cooler climate to begin with, so they will gravitate to them when possible.

    Edit: And if you build a data centre with this ability to recoup heat, you could start building further commercial things in the area and keep the heat redistribution very close. You don’t need to travel very long distances. You do need to put some thought into where they go through and whats around or will be built around.






  • I just wanted to add one other thing on the hardware side.

    These H200’s are power hogs, no doubt about it. But the next generation H300 or whatever it is, will be more efficient as the node process (or whatever its called) gets smaller and the hardware is optimized and can run things faster. I could still see NVIDIA coming out and charging more $/flop or whatever the comparison would be though even if it is more efficient power wise.

    But that could mean that the electricity costs to run these models starts to drop if they truly are plateaued. We might not be following moores law on this anymore (I don’t actually know), but were not completely stagnant either.

    So IF we are plateaued on this one aspect, then costs should start coming down in future years.

    Edit: but they are locking in a lot of overhead costs at today’s prices which could ruin them.