That is comparing an all to all switched Nvlink fabric to a 3D torus for TPUs. Those are completely different network topologies with different tradeoffs.
For example the currently very popular Mixture of Experts architectures require a lot of all to all traffic (for expert parallelism) which works a lot better on the switched NVlink fabric as opposed where it doesn't need to traverse multiple links in the torus.
It's the one exception in the semiconductor supply chain where Europe is still leading. For all other parts of the value creation Europe is either a niche player at best or completely absent, well into the actual application layer.
But these social third places have also shifted. Younger generations aren't going out as much but e.g. playing video games specifically with other close friends is very popular.
Inference throughout scales really well with larger batch sizes (at the cost of latency) due to rising arithmetic intensity and the fact that it's almost always memory BW limited.
If it's just filtered out in the training sets, adding the information as context should work out fine - after all this is exactly how o3, Gemini 2.5 and co deal with information that is newer than their training data cutoff.
Something where you're reachable for any legal purposes- in Germany this sadly remains a physical address.
There are various service which offer a 'virtual' address with digital forwarding of letters for less than 10Eur/Month, so it's not an insurmountable obstacle.
Mixture of experts involves some trained router components which routes to specific experts depending on the input, but without any terms enforcing load distribution this tends to collapse during training where most information gets routed to just one or two experts.
It's not quite waste heat because the cold side of thermal power plants wants to be colder than district heating temperatures for best efficiency. There is some loss in electrical efficiency compared to non cogeneration plants, but the combined efficiency is a lot higher.
Apparently Chinese mainstream silicon PV modules are already a bit cheaper at ~0.14USD/W right now.
Article doesn't talk about efficiencies but it seems production perovskite modules are slightly lower than their silicon counterparts, which will affect downstream costs like land or mounting hardware a bit.
Has been really common in HPC for quite a while. I presume the higher interconnect/network of hpc favour the higher density of liquid cooling. Hardware utilization is also higher compared to normal datacenters, so the additional efficiency vs air cooling is more useful.
Intels surprisingly fast 14nm processors come to mind. Born of necessity as they couldn't get their 10 and later 7nm processes working for years. Despite that Intel managed to keep up in single core performance with newer 7nm AMD chips, although at a mich higher power draw.
If you invest a lot of money into very expensive Nvidia training hardware you certainly want to run them as close to 24/7 as possible.
Dispatchable load usually means oversizing the dispatchable consumer to get the same overall output. This is already uneconomical for even particularly energy intensive industries (e.g. aluminum smelting). I would assume server hardware is a lot more capital intensive than that.
Interestingly enough the price for these giant heatpumps is pretty much in line with domestic ~10kw units.