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I presume the counterargument is that inference hosting is commoditized (sort of like how stateless CPU-based containerized workload hosts are commoditized); there’s no margin in that business, because it is parallelizable, and arbitrarily schedulable, and able to be spread across heterogenous hardware pretty easily (just route individual requests to sub-cluster A or B), preventing any kind of lock-in and thus any kind of rent-extraction by the vendor.

Which therefore means that cards that can only do inference, are fungible. You don’t want to spend CapEx on getting into a new LOB just to sell something fungible.

All the gigantic GPU clusters that you can sell a million at a time to a bigcorp under a high-margin service contract, meanwhile, are training clusters. Nvidia’s market cap right now is fundamentally built on the model-training “space race” going on between the world’s ~15 big AI companies. That’s the non-fungible market.

For Intel to see any benefit (in stock price terms) from an ML-accelerator-card LOB, it’d have to be a card that competes in that space. And that’s a much taller order.



Intel does make cards aimed at this space too:

https://www.intel.com/content/www/us/en/products/details/pro...

Coincidentally, it has 128GB of RAM. However, it is not a GPU, is designed to do training too and uses expensive HBM.

Modern GPUs can do more than inference/training and the original poster asked about a GPU with 128GB of RAM, not a card that can only do inferencing as you described. Interestingly, Qualcomm made its own card targeted at only inferencing with 128GB of RAM without using HBM:

https://www.qualcomm.com/news/onq/2023/11/introducing-qualco...

They do not sell it through PC parts channels so I do not know the price, but it is exactly what you described and it has been built. Presumably, a GPU with the same memory configuration would be of interest to the original poster.


Back in January, someone on Reddit claimed the list price was $16k.


It's competing against Nvidia H100s, which cost $25k. It's cheap, at least by the norms of the space.




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