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Comment by stingraycharles | Hacker News Reader

Google has their own TPUs so they don’t have any vendor lock-in issues at all.

OpenAI OTOH is big enough that the vendor lock-in is actually hurting them, and them making that massive deal with AMD may finally push the needle for AMD and improve things in the ecosystem to make AMD a smooth experience.

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Having your own ASIC comes with a huge sunk cost. One gets advantages from that, but it's still a lock, just a lock of a different color. But with Google money and manpower, management can probably pursue both paths in parallel and not care.

Google’s TPU’s are not powering Gemini or whatever X equivalent LLM you want to compare to.

This isn't true. Gemini is trained and run almost entirely on TPUs. Anthropic also uses TPUs for inference, see, e.g., https://www.anthropic.com/news/expanding-our-use-of-google-c... and https://www.anthropic.com/engineering/a-postmortem-of-three-.... OpenAI also uses TPUs for inference at least in some measure: https://x.com/amir/status/1938692182787137738?t=9QNb0hfaQShW....

I can assure you that most internal ML teams are using TPUs both for training and inference, they are just so much easier to get. Whatever GPUs exist are either reserved for Google Cloud customers, or loaned temporarily to researchers who want to publish easily externally reproducible results.

This comment is incorrect: https://storage.googleapis.com/deepmind-media/Model-Cards/Ge...

They are, even Apple famously uses Google Cloud for their cloud based AI stuff solely because of Apple not wanting to buy NVidia.

Google Cloud does have a lot of NVidia, but that’s for their regular cloud customers, not internal stuff.

What is powering Gemini?

TPUs: https://storage.googleapis.com/deepmind-media/Model-Cards/Ge...

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