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The most interesting thing to discover would be if one of them is that far behind, because they're succeeding on their own/someone not-Nvidia's silicon. The pu
by gpm 2y ago
The most interesting thing to discover would be if one of them is that far behind, because they're succeeding on their own/someone not-Nvidia's silicon.
The public in-house projects that I'm aware of (but as far as I know haven't fully replaced demand for Nvidia GPUs) include:
- Google's "TPU" (in production, publicly rentable)
- Amazon/AWS's "Trainium" (in production, publicly rentable)
- Meta's "MTIA" (in production)
- Microsoft's "Maia 100" (I'm unclear on their status)
- Tesla's "D1" (I'm unclear on their status)
- HarHarVeryFunny 2y agoxAI 100K H100 cluster would be ~$2B Is this trailing year NVIDIA sales, or order book ?
- jsnell 2y agoThe numbers in the article? They're neither. They're the revenue for just Q2, not the full trailing year.
- jsheard 2y agoAre these distinct architectures, or is it an ARM situation where nearly everyone is gluing the same IP cores together in slightly different configurations?
- taktoa 2y agoDefinitely not an ARM situation.
- zaphar 2y agoI'm not sure about all of them but Google's TPU is custom to them and not shared architecture.
- pclmulqdq 2y agoThey are distinct architectures, but mostly do the same thing. Pretty much all of them have a few small control cores that run matrix multiply and vector reduction units. The instruction set on all of them is different, but the broad strokes of the architecture are the same.
- rajman187 2y agoMTIA will be for inference initially. Another to add to the list is wafer maker Cerebras https://www.forbes.com/sites/craigsmith/2024/08/27/cerebras-speeds-ai-by-putting-entire-foundation-model-on-its-giant-chip/ https://www.forbes.com/sites/craigsmith/2024/08/27/cerebras-...
- solidasparagus 2y agoIt's not just the hardware, it's the software stack too and my understanding is that they aren't very good. Even TPUs aren't great if you aren't either (1) doing something extremely standard and a little bit old (e.g. not forefront of research and the stack has already been optimized for your model) or (2) in Google with access to the people who build the stack. Maybe it is working for Meta or Tesla where things can be vertically integrated, but for the public clouds, they have to buy NVIDIA for their customers.
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- bradleyjg 2y agoDoes TSM have the capacity to scale up anything new right now?