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A $100M GPU deal doesn't close on an exchange. It closes in a group chat, off a voice note. Today we're launching Stoa: an RFQ marketplace for GPUs. $300M+ in RFQs in our first month. Post what you need. Vetted dealers bid blind. Firm quotes within 48 hours. We...

120,497 views • 1 month ago •via X (Twitter)

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CoreWeave CEO: The GPU Depreciation Debate is Nonsense Being Pushed by Short Sellers CoreWeave CEO Michael Intrator: “ My take on the GPU depreciation debate is that it's nonsense.” “It's a debate that is being brought to the forefront by some traders that have a short position in the stock and they're trying to talk down.” “Look, here's what we know when we buy infrastructure: we're a success-based company, right? We're a small company, on a relative basis, compared to the enormous companies that we're competing with.” “And so our clients come into us and they buy compute for five years, for six years. Our average contract is five years.” “So any commentary by anyone, either inside or outside of the industry, that this stuff becomes obsolete in 16 months or whatever nonsense they're spewing, it doesn't in any way match up with the facts on the ground.” “The fact on the ground is, they're buying it for five years.” “And my approach to this has always been, if people are willing to pay me for it, it still has value. Pretty simple way of approaching it.” “We use a six year depreciation. We believe that the GPUs will last in excess of six years, but we felt like that was a fair and reasonable approach to a technology cycle that's moving at this velocity.” ------------------------------------------ Our episode is sponsored by the New York Stock Exchange - a modern marketplace and exchange for building the future. It all happens at the NYSE 🏛 -

The All-In Podcast

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38,220 views • 1 month ago

Dylan Patel of SemiAnalysis says a worse GPU with better storage and memory now beats the best chip without them, so buying the newest GPU alone no longer wins inference. So, an AMD GPU with more memory can outperform Nvidia in some cases. "So what we have is we have over $80 million of compute, GPUs from Nvidia, AMD, TPUs from Google, Trainium from Amazon, and we run this benchmark constantly on the newest inference engine, newest drivers, newest PyTorch version, whatever it is." "Every day it runs on an automated CI, and we run it on all the latest Chinese models, from GLM, Zhipu, Moonshot, Kimi, Alibaba, all these models we run." "Initially, when we were benchmarking the difference between these chips and different engines, different schemes for parallelism, we were just running it fixed context length." "But now with Agent X, we've analyzed over $5 million worth of Claude Code traces. This is real production traffic that people have donated to us as well as internally generated. Now we know what the actual agent workload looks like." "And then as we implement that and run those benchmarks, it turns out yes, the chip you're using is very important, but now even more important is how are you handling this memory offload?" "And so while an Nvidia GPU is faster than an AMD GPU in most cases, because AMD GPUs have more memory, they actually end up outperforming in some cases." "Or you can have a worse GPU, but a much better storage solution, and now you can outperform what the best GPU can do without those solutions. So just buying the newest and latest GPU alone doesn't get you the best inference economics." "Actually, you need to layer in all these other innovations including storage and memory." [ Who's the top player on your chart? ] "That really is a difficult multivariable problem. And generally that means you need to have, yes, you need to have the best GPU, a GB300, but you also need to have the best storage solutions. And so I won't spoil who's the best right here, but I will say that storage solutions matter a lot and memory solutions matter a lot, as does your front-end networking. That matters a lot."

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