[cheptsov]

It’s so ridiculous to see TPUs being compared to NVIDIA GPUs. IMO proprietary chips such as TPU had no future sure to the monopoly on the cloud services. There is no competition across the cloud services providers. The only way to access TPUs is through GCP. As the result nobody wants to use them regardless of the technology. This is the biggest fault of GCP. Further the road, the gap between NVIDIA GPUs and Google TPUs (call it „moat“ or CUDA) is going to grow.

The opposite situation is with AMD which are avoiding the mistakes of Google.

My hope though is that AMD doesn’t start to compete with cloud service providers, e.g. by introducing their own cloud.

[hiddencost]

TPUs will thrive regardless of public adoption; Google's internal demand for TPU is such that they could buy every TPU ever produced.

[roughly]

One thing worth note here - TPUs are optimized for a fairly constrained set of operations. Google’s had good success with them, but, like many of the other Google architectural choices, this will constrain Google’s technical choice space in the future - if they’ve gone all in on TPUs, future Google machine learning projects will be using the sets of operations the TPUs excel at because that’s what Google has a lot of, not necessarily because that’s the optimal choice. This will have knock-on effects across the industry due to Google’s significant influence on industry practice and technical direction.

[hustwindmaple1]

Every major Cloud vendor is trying to develop their custom AI ASIC. Putting Google aside, Amazon has trainium/inferentia, which Anthropic uses quite extensively. Microsoft is doing sth. similar, although they are quite behind. OpenAI is doing it. Meta is doing it. That's why the stock price of Broadcom/Marvell soared.