I am highly skeptical about data centers in space, but radiators don't need to be unshaded. In fact, they benefit from the shade. This is also being done on the ISS.
The JWST operates at 2kw max. That's not enough for a single H200.
AI datacenters in space are a non-starter. Anyone arguing otherwise doesn't understand basic thermodynamics.
The whole concept is still insane though, fwiw.
This is precisely why my didactic example above uses a convex shape, a pyramid. This guarantees each surface absorbs or radiates energy without having to take into account self-obscuring by satellite shape.
A very high end desktop pulls more electricity than the whole JWST... Which is about the same as a hair dryer.
Now you need about 50x more for a rack and hundreds/thousands racks for a meaningful cluster. Shaded or not it's a shit load of radiators
https://azure.microsoft.com/en-us/blog/microsoft-azure-deliv...
[0] https://developer.nvidia.com/deep-learning-performance-train...
Click the "Large Language Model" tab next to the default "MLPerf Training" tab.
That takes 16.8 days on 128 B200 GPU's:
> Llama3 405B 16.8 days on 128x B200
A DGX B200 contains 8xB200 GPU's. So it takes 16.8 days on 16 DGX B200's.
A single DGX (8x)B200 node draws about 14.3 kW under full load.
> System Power Usage ~14.3 kW max
source [1] https://www.nvidia.com/en-gb/data-center/dgx-b200
16 x 14.3 kW = ~230 kW
at ~20% solar panel efficiency, we need 1.15 MW of optical power incident on the solar panels.
The required solar panel area becomes 1.15 * 10^6 W / 1.360 * 10^3 W / m ^ 2 = 846 m ^ 2.
thats about 30 m x 30 m.
From the center of the square solar panel array to the tip of the pyramid it would be 3x30m = 90 m.
An unprecedented feat? yes. But no physics is being violated here. The parts could be launched serially and then assembled in space. Thats a device that can pretrain from scratch LLaMa 3.1 in 16.8 days. It would have way to much memory for LLaMa 3.1: 16 x 8 x 192 GB = ~ 25 TB of GPU RAM. So this thing could pretrain much larger models, but would also train them slower than a LLaMa 3.1.
Once up there it enjoys free energy for as long as it survives, no competing on the electrical grid with normal industry, or domestic energy users, no slow cooking of the rivers and air around you, ...
Nobody said sending a single rack and cooling it is technically impossible. We're saying sending datacenters worth of rack is insanely complex and most likely not financially viable nor currently possible.
Microsoft just built a datacenter with 4600 racks of GB300, that's 4600 * 1.5t, that alone weights more than everything we sent into orbit in 2025, and that's without power nor cooling. And we're still far from a single terawatt.
a different question is the expected payback time, unless someone can demonstrate a reasonable calculation that shows a sufficiently short payback period, if no one here can we still can't exclude big tech seeing something we don't have access to (the launch costs charged to third parties may be different than the launch costs charged for themselves for example).
suppose the payback time is in fact sufficiently short or commercial life sufficiently long to make sense, then the scale didn't really matter, it just means sending up the system described above repeatedly.
Either it does or it doesn't make financial sense, and if it does the scale isn't the issue (well until we run into material shortages building Elon's Dyson sphere, hah).