[d_burfoot]

The business plan makes sense to me. They are a company that is focussed specifically on building AI data centers, which is a huge part of the economy at the moment. The big cloud players know about generic data centers, but there are likely big efficiency wins to be gained by specializing on AI. There is also the geopolitical angle: European countries (and others!) will likely trust a UK-based company more than one of the American BigCos. NVidia is a great partner and investor for them: NScale will buy billions worth of NVDA chips, and also send information and learnings about the unique needs of the market to the chipmaker.

That being said, financial engineering tricks like depreciation and tax sheltering are of course hugely important in the global economy. It's likely that NVDA has a lot of cash sitting in Europe that it doesn't want to repatriate because it would have to pay taxes on it.

[cmiles8]

It makes sense till it doesn’t. History tells us that suppliers funding demand like this tends to end badly.

[vel0city]

> there are likely big efficiency wins to be gained by specializing on AI

I've seen this suggested before as well, but I don't think I've heard a lot of actual concrete things. What big efficiency wins are to be had specializing on "AI" datacenters as opposed to what the past mega hyperscalers have done? What techniques seem to be out there that cloud providers and others have slept on? What makes them so different, in terms of operating a datacenter?

I'm genuinely asking.

[mghackerlady]

I know nothing about the inner workings of a large scale AI datacenter but I'd imagine the power and cooling requirements are more specialised than your average datacenter that mostly has to handle transmitting large amounts of data over the internet (not that that isn't computationally expensive, I just imagine LLMs (especially at the current scale of their deployment) are much more demanding)

[vel0city]

> I'd imagine the power and cooling requirements are more specialised than your average datacenter

But are they actually doing things differently than the high compute parts of the hyperscaled datacenters? Are there radical new ways of distributing heat in the datacenter that only makes sense at that level of energy usage per square foot? Is AI energy use that much higher per square foot of other high-compute parts of datacenters, or is it just that its now something like 90% of the floor plan versus maybe only 50-60%?

> handle transmitting large amounts of data over the internet

I certainly can't speak for all datacenters, and I've never been in a hyperscaler datacenter. But of all the datacenters I've spent time in, the space for the outside network connectivity was rather small compared to the rest of the space for storage and compute. Think a few small office suites dedicated to outside networks coming in and connecting to the clients in the datacenter compared to a medium to large sized warehouse full of compute and storage.

[zozbot234]

There's "high compute", and then there's proper HPC. AI these days is way more on the HPC end of the scale. The GPUs are doing computations using 2-bit and 4-bit numbers and not 64-bit, but everything else is going to be comparable.