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Crazy conglomerate discount on Alphabet if you can see TPUs as the only Nvidia competitor for training. Breaking up Alphabet seems more profitable than ever

wrsh07 2y ago
TPU is available to people outside of Google, but people prefer Nvidia It's not obvious to me that a hardware business manufacturing tpus for the general public is necessarily more valuable than one that benefits from tight integration with Google's internal software stack and datacenter tech It's orthogonal: tpu doesn't do much marketing and doesn't have to. Engineers at Google will use it because they have to and because of the huge cost advantage they get for using it. TPU is probably lacking the libraries that Nvidia has developed to be accessible to a broad array of use cases
- lanthissa 2y ago
  This is the thing that makes no sense to me, if TPU's are even close to nvidia its a business worth as much as search. They could spend billions on just dx and get 100x that back on investment. The whole TPU line makes no sense to me, if its as good as it says (which is does seem to be) sell it publicly and add a trillion to your market cap. The only way this makes any sense is if inside google people legitimately think google cloud is going to be bigger than nvidia+ a lot of azure&aws, which seems crazy
  - wrsh07 2y ago
    TPU is extremely high performance for Google because it can be optimized for Google's workloads. Google has absolutely world class data centers (think power efficiency) and TPU performs extremely well in them. (In turn: I expect Gemini is optimized to be trained and run on TPU) Google has vastly different constraints than an average startup or user of machine learning. The flexibility of Nvidia GPU's, the software, these things are really valuable for most people. Only recently with LLMs has the majority of uses started to look very very similar (some slightly modified transformer) Early versions of TPU required you to write your ml logic using a very restricted subset of tensorflow. (It had to be somewhat functional, etc) Normal people don't write code like that, and it's not worth it for them to re write a working model to run on a TPU because software engineers are expensive and Nvidia GPUs are really good and general.
    - wrsh07 2y ago
      There's a winner take all network effect: research happens on cuda, if you want an off the shelf solution you'll need to use cuda, etc

wrsh07 2y ago

TPU is available to people outside of Google, but people prefer Nvidia It's not obvious to me that a hardware business manufacturing tpus for the general public is necessarily more valuable than one that benefits from tight integration with Google's internal software stack and datacenter tech It's orthogonal: tpu doesn't do much marketing and doesn't have to. Engineers at Google will use it because they have to and because of the huge cost advantage they get for using it. TPU is probably lacking the libraries that Nvidia has developed to be accessible to a broad array of use cases

lanthissa 2y ago
This is the thing that makes no sense to me, if TPU's are even close to nvidia its a business worth as much as search. They could spend billions on just dx and get 100x that back on investment. The whole TPU line makes no sense to me, if its as good as it says (which is does seem to be) sell it publicly and add a trillion to your market cap. The only way this makes any sense is if inside google people legitimately think google cloud is going to be bigger than nvidia+ a lot of azure&aws, which seems crazy
- wrsh07 2y ago
  TPU is extremely high performance for Google because it can be optimized for Google's workloads. Google has absolutely world class data centers (think power efficiency) and TPU performs extremely well in them. (In turn: I expect Gemini is optimized to be trained and run on TPU) Google has vastly different constraints than an average startup or user of machine learning. The flexibility of Nvidia GPU's, the software, these things are really valuable for most people. Only recently with LLMs has the majority of uses started to look very very similar (some slightly modified transformer) Early versions of TPU required you to write your ml logic using a very restricted subset of tensorflow. (It had to be somewhat functional, etc) Normal people don't write code like that, and it's not worth it for them to re write a working model to run on a TPU because software engineers are expensive and Nvidia GPUs are really good and general.
  - wrsh07 2y ago
    There's a winner take all network effect: research happens on cuda, if you want an off the shelf solution you'll need to use cuda, etc