It doesn't have to be a full replacement. Meta for example is already running both. Interesting how they state [1]:
"This announcement is one step in our ambitious infrastructure roadmap. By the end of 2024, we’re aiming to continue to grow our infrastructure build-out that will include 350,000 NVIDIA H100 GPUs as part of a portfolio that will feature compute power equivalent to nearly 600,000 H100s."
350k NVDA GPUs, but the compute power of 600k. See here for how quickly their silicon is advancing [2].
No one is saying NVDA will go away. But the stock is priced for near perfect growth projections. NVDA's second biggest customer like Meta cutting back even just a bit will hit NVDA's bottom line. That's the stock risk.
"This announcement is one step in our ambitious infrastructure roadmap. By the end of 2024, we’re aiming to continue to grow our infrastructure build-out that will include 350,000 NVIDIA H100 GPUs as part of a portfolio that will feature compute power equivalent to nearly 600,000 H100s."
350k NVDA GPUs, but the compute power of 600k. See here for how quickly their silicon is advancing [2].
No one is saying NVDA will go away. But the stock is priced for near perfect growth projections. NVDA's second biggest customer like Meta cutting back even just a bit will hit NVDA's bottom line. That's the stock risk.
[1] https://engineering.fb.com/2024/03/12/data-center-engineerin...
[2] https://ai.meta.com/blog/next-generation-meta-training-infer...