> It is also important to note that, until recently, the GenAI industry’s focus has largely been on training workloads. In training workloads, CUDA is very important, but when it comes to inference, even reasoning inference, CUDA is not that important, so the chances of expanding the TPU footprint in inference are much higher than those in training (although TPUs do really well in training as well – Gemini 3 the prim…
Another factor is that training is always done with batches. Inference batching depends on the number of concurrent users. This means training tends to be compute bound where supporting the latest data types is critical, whereas inference speeds are often bottlenecked by memory which does not lend itself to product differentiation. If you put the same memory into your chip as your competitor, the difference is going to be way smaller.