[vineyardmike]

> why wouldn't Google be selling shovels

They do sell them - but through their struggling cloud business. Either way, Nvidia's margin is google's opportunity to lower costs.

> I can see Google's custom chips are 15x to 30x slower to train AI

TPUs are designed for inference not training - they're betting that they can serve models to the world at a lower cost structure than their competition. The compute required for inference to serve their billions of customers is far greater than training costs for models - even LLMs. They've been running model inference as a part of production traffic for years.

[uluyol]

Google most certainly uses TPUs for training.

[refulgentis]

This breaks my brain, because I know Google trains it models on TPUs and they're seen as faster, and if they're better at inference, and can train, then why is Nvidia in a unique position? My understanding was always it's as simple as it required esoteric tooling

[smueller1234]

Multiple types of TPUs.

(I work for Google, but the above is public information.)

[vineyardmike]

Because people generally don’t use TPUs outside of Google. The tooling is different, the access is metered through GCP, etc.

Nvidia is in a vaguely unique position in that their products have great tooling support and few companies sell silicon at their scale.

[refulgentis]

Correct, I'm pointing out politely that's in conflict with the person I'm replying to.

[koe123]

Possibly naive but I very much view CUDA and its integration into ML frameworks being nvidias moat