The GPU Ice Cube May Have a Freezer
A few weeks ago I wrote an article “The Second Life of a GPU.”
In that post I discussed a (hypothetical) scenario where GPUs retained value past their (presumed) 4-5 year useful life. What impacts would this have on clouds / neoclouds or the broader compute complex? Could compute contracts be “re-contracted” after their useful life? All some version of the same question - is the useful life of these chips actually much longer than 4-5 years.
Well, this week on Coreweave’s earnins call we got a great nugget related to this.
“What we are seeing today is that the upside of recontracting is real as we remain largely sold out of prior generations of NVIDIA GPUs in addition to the current SKUs. So as our earlier generation fleets roll off their original contract, they offer the potential to deliver strong returns in the subsequent years. We are seeing this across our Ampere and Hopper fleet.As an example, we recently signed an A100 contract that extends into 2029 at an attractive price. As a reminder, this SKU was introduced in 2020. Clusters of prior generations of architecture of our installed, energized production-grade compute already running at scale. They come with a proven ROI for customers.”
Then Jensen tweeted this:
Of course, both $CoreWeave, Inc.(CRWV)$ and $NVIDIA(NVDA)$ have a vested interested in the useful life of chips elongating. However - I believe them. And we now have some concrete data to demonstrate these chips do have a useful life that (for now!) extends to 9 (and maybe more) years.
One one hand you can say this is just representative of the current moment we’re in. Compute constraints still exist everywhere, so why not take any form of compute, regardless of how old it is. While true, you still wouldn’t sign a contract for an A100 chip if you thought it was worthless. And for someone like Coreweave a recontracting deal like this is (probably) meaningfully profitable. The initial purchase (ie debt used to fund the purchase) probably was amortized over the useful life of the chip. My guess is that debt is now fully paid off, and incremental revenue from the re-contracting comes without a heavy financing (interest) cost.
I really believe we’ll see more and more of this - chips contracting well beyond their useful life. One reason - the types of inference requests will continue to spread out in complexity. We’ll start seeing the most complex inference workloads become even more complex. And these probably require the latest generation of chips. However, the long tail will also grow. And some of these requests will be incredibly trivial. Those won’t require the latest and greatest. There will be a lot of value created in a software layer that can understand the request coming in and route to the right chip in real time.
Another reason - I continue to believe that the “compute constraints” will last much much longer than people think. So far, the risk has repeatedly been to the upside!
Here’s an analogy I like. The market has been pricing GPUs like a melting ice cube. Turns out, they may have a freezer! And that freezer is an exponentially growing curve of inference (and heterogeneity of inference requests)
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