Bron: X
Semiconductor expert and former AMD employee shares some valuable insights about the GPU market, NVDA, and AI:
- The expert thinks that even if the newer generations of GPUs improve performance by only 10-25% from your base compute, that is still enormous for many of the companies building AI models and a big reason to upgrade to the newest chip.
- In terms of META, he believes they will continue to buy the newest and highest-performing GPU for research. Still, for running some of the recommendation models in the background, the H100 and A100 might be enough, as you don't need the most cutting-edge GPUs to run some of the models.
- The expert thinks the cycle for upgrading the GPUs is 3-4 years, so the back-of-the-napkin math of the AI accelerator market being around $250B per year would make sense.
- When it comes to the demand for the NVDA H100 in the hyperscallers, the experts said that the demand is very high and that AWS might not give you access if you only want to rent it for a week or less over the guys who are renting it for a month or two months.
- Even though AMZN, GOOGL, META, and MSFT are all developing their own chips, the expert still thinks they have a few years to catch up, as many of these chips are still immature products.
- He gives an interesting example of GOOGL, where, for internal research, they use TPUs but still buy NVDA GPUs to offer them on their public-facing cloud to entice customers and keep them happy on the cloud.
- NVDA's CUDA is one of the main reasons why NVDA is so dominant.
- Down the road, the expert still thinks that the hyperscalers and META will be able to save a lot of money by bringing these chips in-house, but this will not mature overnight, as the software for programming these chips is not that simple.