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Comment by Der_Einzige | Hacker News Reader

I'm 99.999% sure that the claim of "all deep learning for Gemini on their own silicon" is not true.

Maybe if you restrict it similarly to the Deepseek paper to "Gemini uses TPU for the final successful training run and for scaled inference" you might be correct, but there's no way that GPUs aren't involved for at minimum comparability and more rapid iteration reasons during the extremely buggy and error prone point of getting to the final training run. Certainly the theoretical and algorithmic innovations that are often being done at Google and do make their way into Gemini also sometimes using Nvidia GPUs.

GCP has a lot of, likely on the order of at least 1 million GPUs in their fleet today (I'm likely underestimating). Some of that is used internally and is made available to their engineering staff. What constitutes "deep learning for gemini" is very up to interpretation.

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That's a strange position to take with such high certainty. Google has been talking about training on TPUs for a long time. Many ex and current employees have been on the record talking about how much nicer the Google internal training infra using TPUs is. GPU is an afterthought for Google's inference and non-existent in training.

Internally, TPU is much cheaper for the same amount of compute compared to GPU, so I don't see much reasons why they need to use GPU. Probably >99% of compute budgets are spent on TPU. It might be true if you say these <1% still counts, but I guess it is pretty safe to say all of its meaningful production workload are running on TPU. It is simply too expensive to run a meaningful amount of compute on non-TPU.

Just to clarify, TPU has been in development for a decade and it is quite mature these days. Years ago internal consumers had to accept the CPU/GPU and TPU duality but I think this case is getting rarer. I guess this is even more true for DeepMind since itself owns a ML infra team. They likely be able to fix most of the issues with a high priority.

You seem to think GPUs are better than TPUs for rapid iteration. Why is that? There's no inherent reason why one is more suited to rapid iteration than another; it's entirely a matter of developer tooling and infrastructure. And Google famously has excellent tooling. And furthermore, the tooling Google exposes to the outside world is usually poorer than the tooling used internally by Googlers.

It was a few years ago since I last played with Googles hw, but iirc TPUs were inflexible and very fast. Worked well for linear and convolutional layers but could not accelerate certain LSTM configurations. For such networks GPUs were faster. It wouldn't surprise me the least if TPU hardware support lagged behind what the latest and greatest LLMs require for training.

Google is the creator of JAX and XLA. Maybe the developer laptops have Nvidia GPUs and they do some testing there, but for Google there is literally no point in bothering with CUDA, pytorch or any other ecosystem strongly focused on Nvidia GPUs.

In my experience JAX is way more flexible than pytorch the moment you want to do things that aren't training ML models. E.g. you want to build an optimizer that uses the derivative of your model with respect to the input.

Honestly Pytorch is weird imo, I'm surprised people love it so much.

Loss.backward? Tensor.grad? Optimizer.zero grad()? With torch.no_grad()?

What is with all these objects holding pointers to stuff? An ndarray is a pointer to memory and a shape my dudes. A gradient is the change in a scalar function w.r.t to some inputs.

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