this post was submitted on 01 Mar 2026
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Ya, the economies of how much total space / material for the global network is similar, although lets say higher due to losses in efficiency in distributing it over so many dishes, but in terms of how big any individual radiator is and how much space each one is going to take, the smaller sizes make it easier to manage. Trying to figure out a 150-200m^2^ ~~solar~~ ~~panel~~ radiator is a lot easier than trying to figure out a 1km^2^
The individual power of each satellite having to use a mesh network to train might not be fast enough, maybe they'll still use land based ones for training, but no single person needs more compute than what a satellite can provide. So from the inference / customer computation side of things, it isn't a problem.
edit: I meant radiator, not solar panel
edit: looks like blackwells can run sunstained at 88c, so that will help a bit more as well on size, the calculator now says 103m^2^ instead of 127m^2
Not necessarily. There are inference schemes where spreading MoE models across 40+ GPUs with a fast interconnect yields better efficiency.
The coolant still needs to remain relatively cool to hold that silicon temperature, though. Practically it can't be like 60C.
Ah, ya that makes sense, whatever the numbers the the chip can be the coolant will be less.