Over the past few years, we have seen a lot of AI-market-related metrics, starting from petaflops of performance and going all the way up to gigawatts of power consumption. A rather unexpected metric is perhaps the one from Morgan Stanley (via @Jukanlosreve) that counts the wafer consumption of AI processors. There are no surprises, though: Nvidia controls the lion's share of wafers designated for AI and is set to increase its domination in 2025 as it chews through up to 77% of the world's supply of wafers destined for AI applications.

While Nvidia is operating at an unprecedented scale and continues ramping up production dramatically, AMD's share of AI wafer usage will actually decline next year. The figures also cover other industry heavyweights like AWS, Google, Tesla, Microsoft, and Chinese vendors.

Morgan Stanley’s analysis is the best in the industry. It’s data you won’t find anywhere else… pic.twitter.com/FhGwaf2Ux6February 8, 2025

If you expand the above tweet, you can see that Morgan Stanley predicts that Nvidia will dominate AI semiconductor wafer consumption in 2025, increasing its share from 51% in 2024 to 77% in 2025 while consuming 535,000 300-mm wafers.

AI-specific processors, such as Google TPU v6 and AWS Trainium, are gaining traction but remain far behind Nvidia's GPUs. As such, AWS's share is set to fall from 10% to 7%, while Google's share is projected to fall from 19% to 10%. Google allocates 85,000 wafers to TPU v6, while AWS dedicates 30,000 to Trainium 2 and 16,000 to Trainium 3, according to Morgan Stanley's projections.

As for AMD, its share is expected to drop from 9% to 3% as its MI300, MI325, and MI355 GPUs — the company's main offerings — have wafer allocations ranging from 5,000 to 25,000 wafers. Notably, this doesn't mean that AMD will consume fewer wafers next year, just that its percentage of the overall share will decline.

Intel's Gaudi 3 processors (named Habana in the graph) will remain around 1%.

Tesla, Microsoft, and Chinese vendors hold minimal shares. This may not be a problem, though. Tesla's Dojo and FSD processors have limited wafer demand, which reflects their niche role in AI computing. Microsoft's Maia 200 and its enhanced version have similarly small wafer allocations because these chips remain secondary for the company as it continues to use Nvidia's GPUs for training and inference.

By: DocMemory
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