TON Reddit Mentions | ChartExchange

\> Hyperscalers have aggressively stretched server and GPU depreciation schedules from 3-4 years out to 5-6 years on the balance sheet.
There is a good reason for this: we're literally running into problems with physics. Smaller nodes, on the chip side, and thermal constraints on the plant/data center side don't have good solutions. It looks like NVIDIA is about to drop a new gen of GPU's - and it's not looking like a big gain on performance per watt. Spending more power and more cooling to do less work isnt enough of an upside.
There has recently been a bunch of hype around PFAS immersive cooling. The thing is that unlike earlier data centers the current ones need to run large, physically contiguous blocks of equipment for optimal efficiency. Unlike other cloud operations you cant defer the repairs for long, if at all.
\> These loans are collateralized by GPUs that are depreciating faster than the underlying credit facilities amortize.
I can rent capacity for 2.50 an hour - it would take 2 years just to pay off the GPU.
But if I want to rent a cluster, a large continuous block of GPU's thats a different matter - it's more like 12 bucks an hour, with a large commitment. Meanwhile I have to pay for storage, bandwidth, and regular compute - all at painful markups (cloud computing has always been massively profitable, now you have to pay more for it when its GPU adjacent).
I can name at least two companies with NON LLM workloads who are looking for these deals, cash in hand and simply can NOT find the capacity - and there are a TON of them out there.
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None of this makes what your saying "wrong". Without deeper insight into the pipeline of what has been bought, delivered, installed and is operating and a sense of actual demand (not only for AI but for research) its hard to say how much impact it has.

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