A question I don't see addressed in all these articles: what prevents Nvidia from doing the same thing and iterating on their more general-purpose GPU towards a more focused TPU-like chip as well, if that turns out to be what the market really wants.

They will, I'm sure.

The big difference is that Google is both the chip designer *and* the AI company. So they get both sets of profits.

Both Google and Nvidia contract TSMC for chips. Then Nvidia sells them at a huge profit. Then OpenAI (for example) buys them at that inflated rate and them puts them into production.

So while Nvidia is "selling shovels", Google is making their own shovels and has their own mines.

Deepmind gets to work directly with the TPU team to make custom modifications and designs specifically for deepmind projects. They get to make pickaxes that are made exactly for the mine they are working.

Everyone using Nvidia hardware has a lot of overlap in requirements, but they also all have enough architectural differences that they won't be able to match Google.

OpenAI announced they will be designing their own chips, exactly for this reason, but that also becomes another extremely capital intensive investment for them.

This also doesn't get into that Google also already has S-tier dataceters and datacenter construction/management capabilities.

It's not that the TPU is better than an NVidia GPU, it's just that it's cheaper since it doesn't have a fat NVidia markup applied, and is also better vertically integrated since it was designed/specified by Google for Google.

That's exactly what Nvidia is doing with tensor cores.

That's pretty much what they've been doing incrementally with the data center line of GPUs versus GeForce since 2017. Currently, the data center GPUs now have up to 6 times the performance at matrix math of the GeForce chips and much more memory. Nvidia has managed to stay one tape out away from addressing any competitors so far.

The real challenge is getting the TPU to do more general purpose computation. But that doesn't make for as good a story. And the point about Google arbitrarily raising the prices as soon as they think they have the upper hand is good old fashioned capitalism in action.

Nvidia doesn't have the software stack to do a TPU.

They could make a systolic array TPU and software, perhaps. But it would mean abandoning 18 years of CUDA.

The top post right now is talking about TPU's colossal advantage in scaling & throughput. Ironwood is massively bigger & faster than what Nvidia is shooting for, already. And that's a huge advantage. But imo that is a replicateable win. Throw gobs more at networking and scaling and nvidia could do similar with their architecture.

The architectural win of what TPU is more interesting. Google sort of has a working super powerful Connection Machine CM-1. The systolic array is a lot of (semi-)independent machines that communicate with nearby chips. There's incredible work going on to figure out how to map problems onto these arrays.

Where-as on a GPU, main memory is used to transfer intermediary results. It doesn't really matter who picks up work, there's lots of worklets with equal access time to that bit of main memory. The actual situation is a little more nuanced (even in consumer gpu's there's really multiple different main memories, which creates some locality), but there's much less need for data locality in the GPU, and much much much much tighter needs, the whole premise of the TPU is to exploit data locality. Because sending data to a neighbor is cheap, sending storing and retrieving data from memory is slower and much more energy intense.

CUDA takes advantage of, relies strongly on the GPU's reliance in main memory being (somewhat) globally accessible. There's plenty of workloads folks do in CUDA that would never work on TPU, on these much more specialized data-passing systolic arrays. That's why TPUs are so amazing, because they are much more constrained devices, that require so much more careful workload planning, to get the work to flow across the 2D array of the chip.

Google's work on projects like XLA and IREE is a wonderful & glorious general pursuit of how to map these big crazy machine learning pipelines down onto specific hardware. Nvidia could make their own or join forces here. And perhaps they will. But the CUDA moat would have to be left behind.

the entire organisation has been built over the last 25 years to produce GPUs

turning a giant lumbering ship around is not easy

They lose the competitive advantage. They have nothing more to offer than what Google has in-house.

Nothing in principle. But Huang probably doesn't believe in hyper specializing their chips at this stage because it's unlikely that the compute demands of 2035 are something we can predict today. For a counterpoint, Jim Keller took Tenstorrent in the opposite direction. Their chips are also very efficient, but even more general purpose than NVIDIA chips.

For users buying H200s for AI workloads, the "ASIC" tensor cores deliver the overwhelming bulk of performance. So they already do this, and have been since Volta in 2017.

To put it into perspective, the tensor cores deliver about 2,000 TFLOPs of FP8, and half that for FP16, and this is all tensor FMA/MAC (comprising the bulk of compute for AI workloads). The CUDA cores -- the rest of the GPU -- deliver more in the 70 TFLOP range.

So if data centres are buying nvidia hardware for AI, they already are buying focused TPU chips that almost incidentally have some other hardware that can do some other stuff.

I mean, GPUs still have a lot of non-tensor general uses in the sciences, finance, etc, and TPUs don't touch that, but yes a lot of nvidia GPUs are being sold as a focused TPU-like chip.

> what prevents Nvidia from doing the same thing and iterating on their more general-purpose GPU towards a more focused TPU-like chip as well, if that turns out to be what the market really wants.

Nothing prevents them per se, but it would risk cannibalising their highly profitable (IIRC 50% margin) higher end cards.