They do voluntarily offer a way to signal that the data GoogleBot sees is not to be used for training, for now, and assuming you take them at their word, but AFAIK there is no way to stop them doing RAG on your content without destroying your SEO in the process.
Arguably main OpenAI raison d'être was to be a counterweight to that pre-2023 Google AI dominance. But I'd also argue that OpenAI lost its way.
I think we can be reasonably sure that search, Gmail, and some flavor of AI will live on, but other than that, Google apps are basically end-of-life at launch.
Google will have no problem discontinuing Google "AI" if they finally notice that people want a computer to shut up rather than talk at them.
how you define big? My understanding they failed to compete with facebook, and decided to redirect resources somewhere else.
The hype when it was first coming to market was intense. But then nobody could get access because they heavily restricted sign ups.
By the time it was in "open beta" (IIRC like 6-7 mos later), the hype had long died and nobody cared about it anymore.
Nvidia is tied down to support previous and existing customers while Google can still easily shift things around without needing to worry too much about external dependencies.
Totally possible, but the second order effects are much more complex than "leader once for all". The path for victory for China is not war despite the west, but a war when the west would not care.
As long as "tomorrow" is a better day to invade Taiwan than today is, China will wait for tomorrow.
What I'm sure about is having a programming unit more purposed to a task is more optimal than a general programming unit designed to accommodate all programming tasks.
More and more of the economics of programming boils down to energy usage and invariably towards physical rules, the efficiency of the process has the benefit of less energy consumed.
As a Layman is makes general sense. Maybe a future where productivity is based closer on energy efficiency rather than monetary gain pushes the economy in better directions.
Cryptocurrency and LLMs seem like they'll play out that story over the next 10 years.
Am I misunderstanding "TPU" in the context of the article?
With simulations becoming key to training models doesn't this seem like a huge problem for Google?
To quote The Next Platform: "An Ironwood cluster linked with Google’s absolutely unique optical circuit switch interconnect can bring to bear 9,216 Ironwood TPUs with a combined 1.77 PB of HBM memory... This makes a rackscale Nvidia system based on 144 “Blackwell” GPU chiplets with an aggregate of 20.7 TB of HBM memory look like a joke."
Nvidia may have the superior architecture at the single-chip level, but for large-scale distributed training (and inference) they currently have nothing that rivals Google's optical switching scalability.
While the B200 wins on raw FP8 throughput (~9000 vs 4614 TFLOPs), that makes sense given NVIDIA has optimized for the single-chip game for over 20 years. But the bottleneck here isn't the chip—it's the domain size.
NVIDIA's top-tier NVL72 tops out at an NVLink domain of 72 Blackwell GPUs. Meanwhile, Google is connecting 9216 chips at 9.6Tbps to deliver nearly 43 ExaFlops. NVIDIA has the ecosystem (CUDA, community, etc.), but until they can match that interconnect scale, they simply don't compete in this weight class.
Why? To me, it seems better for the market, if the best models and the best hardware were not controlled by the same company.
The truth is the LLM boom has opened the first major crack in Google as the front page of the web (the biggest since Facebook), in the same way the web in the long run made Windows so irrelevant Microsoft seemingly don’t care about it at all.
Sparse models have same quality of results but have less coefficients to process, in case described in the link above sixteen (16) times as less.
This means that these models need 8 times less data to store, can be 16 and more times faster and use 16+ times less energy.
TPUs are not all that good in the case of sparse matrices. They can be used to train dense versions, but inference efficiency with sparse matrices may be not all that great.
https://docs.cloud.google.com/tpu/docs/system-architecture-t...
Does anyone have a sense of why CUDA is more important for training than inference?
What does it even mean in neural net context?
> numerical stability
also nice to expand a bit.
Further it's worth noting that the Ironwood, Google's v7 TPU, supports only up to BF16 (a 16-bit floating point that has the range of FP32 minus the precision. Many training processes rely upon larger types, quantizing later, so this breaks a lot of assumptions. Yet Google surprised and actually training Gemini 3 with just that type, so I think a lot of people are reconsidering assumptions.
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.
"Meta in talks to spend billions on Google's chips, The Information reports"
https://www.reuters.com/business/meta-talks-spend-billions-g...
sbarre•1h ago
blibble•1h ago
turning a giant lumbering ship around is not easy
sbarre•1h ago
sofixa•1h ago
Nothing prevents them per se, but it would risk cannibalising their highly profitable (IIRC 50% margin) higher end cards.
numbers_guy•1h ago
fooker•1h ago
bjourne•56m ago
llm_nerd•1h ago
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.
sorenjan•1h ago
LogicFailsMe•1h ago
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.
timmg•1h ago
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.
1980phipsi•1h ago
sojuz151•1h ago
HarHarVeryFunny•56m ago
Workaccount2•44m ago
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.