$1T AI training cluster before 2031?
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Ṁ6501
2031
73%
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In "Situational Awareness: The Decade Ahead", Leopold Aschenbrenner estimates that the largest training clusters will cost over one trillion dollars around 2030.

Clarifications:

  • $1T worth of computers and associated data center infrastructure (e.g. building, cooling, networking; does not include the cost of the power plant)

  • Computers must be networked together and training one model. They do not need to synchronize weights at each gradient step.

  • Value of data centers will be estimated with reasonable depreciation. So, $1T of purchase price in TPUv1s would not count.

  • Nominal dollars.

  • So, if the nominal value of the datacenter being used to train a model less depreciation ever crosses $1T, this market resolves YES.

This is one of a series of markets on claims made in Leopold Aschenbrenner's Situational Awareness report(s).

Other markets about Leopold's predictions:

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I think asynchronous training distributed between multiple datacenters will cause issues with this question's resolution

bought Ṁ50 NO

Considering how quickly GPUs are improving I'm guessing the training FLOPs vs intelligence curve would be basically flat well before you would reach $1 trillion in 2031

Edited title to match language of other [LA:SA] markets. (No change to meaning.)

If Google has spent 1T on all of their tpus and do distributed training does that count?

Or does this require a single data center?

@wrhall I’ll count “$1T worth of computers, networked together, training one model”. A few details:

  • Value of data centers will be estimated with reasonable depreciation. So, $1T of purchase price in TPUv1s would not count.

  • $1T nominal dollars

opened a Ṁ200 YES at 30% order

@Tossup Seems fair! I don't know where they'll get the money (how big is their war chest?), but if you count 2 generations of tpus it feels plausible. Not sure if we will be able to get accurate accounting from them in 2030, but let's see