NVDA 주가와 TPU의 위협, GPU를 대체할 수 있을까? 🤔

TPUs are objectively better than GPUs for LLMs and anything that just requires pure matrix maths, probably by around 20-30%. GPUs in theory are more versatile and if there's a significant regime change in how these models operate could be better placed however I think this will be unlikely. Nvidia's actual advantage is their closer relationship with TSMC and booking all of their manufacturing.

No youre not. I think Jensen was throw off guard by all the tough questions and lost his cool. Thats why I think NVDA stock price will no longer go up much more than the semi index will.

The answer is the size of the market. As long as the size of the market is bigger than what can be filled, there is room for everybody to grow.

Nvidia were never irreplaceable in the chain like ASML or TSM. They were early, but that advantage doesn’t last forever.

>The answer Jensen gave was: “yeah well maybe TPUs are faster and cheaper for gen AI workloads, but they can’t do other stuff as well such as video games and molecular biology or mining"

He didn't say this. He said Nvidia GPUs still win on total cost of ownership. This is the most important point. Google has never submitted their benchmarks to https://mlcommons.org/benchmarks/ which is what the industry uses to measure AI chips.

If Nvidia is winning in total cost of ownership, then there's no reason to build on TPUs except that if you are just desperate for compute like Anthropic and OpenAI are.

GPUs are far more general purpose, can be used in any cloud so no vendor lock in, and if it works on your RTX 5090, it will work on a Vera Rubin.

I know this sub has a hardon for Google at the moment but there is a reason why AWS, Microsoft, Google, Meta all buy as many Nvidia GPUs as they possibly can.

It’s already starting to show up. Listen to Amazon and Google er specifically what they are saying about their own chips.

Jensen is right in that TPUs offer a limited scope in their capabilities. An NVIDIA accelerator is as general purpose as it can be. Hence why a chip like those made by NVIDIA are the ones used for things other than gen AI. It remains to be seen if there is such a large market for non-llm stuff.

So, here's my opinion.

TPUs are really strong at "matrix math" which is great for LLMs.

But less so for Agentic AI where GPUs are more useful. Which is probably what Jensen was talking about.

Another use case is for robotics, humanoid robotics. Where Nvidia is used by the big robotics labs like Figure AI. So, when humanoid robots become a reality and scales up, Nvidia will play a major part because they've been developing the tools and "operating system" with their GR00T for a long time.

So, LLMs TPUs win. But for Agentic workloads (complex ones anyway), and humanoid robots, Nvidia has the edge.

Developers in companies, except for major hyperscalers with the resources to invest in a TPU stack (like large tech companies and banks), are likely to stick with the most accessible option, CUDA, for the foreseeable future. but yes it would hurt but the pie would only get bigger by than.

The Gen AI boom literally only started 3.5 years ago. The market is so huge and growing, there’s room for multiple players to win long term. AI workloads will only get more complex and demanding when related fields like robotics, autonomous vehicles, and quantum computing take off.

Even NVDA GOOG and AMZN collectively won’t be able to fulfill all demand, leaving room for AMD and INTC to grow.

I believe TPUs are more cost-effective but GPUs are more general purpose and more flexible in different scenarios like research, robotics, simulations. Nvidia has a strong moat on the developer side with Cuda and offer a whole ecosystem with networking and tooling.

GPUs are still required for training. TPUs are only for inferencing.

The idea that TPUs are threatening Nvidia's GPU market is short sighted.

I think what Google, Amazon, and Microsoft are developing in-house are being done so to fit a use case within those companies. Be good at supporting a business area more targeted to themselves so the feedback loop, scale, whatever jargon leads back to improving their business and cash flow. So NVDA is the leader in general, will remain so for minimum of the next few years, and is creating a fortress of a balance sheet. I would expect them to get their eyes on the horizon in the next 5 years, though. Put their cash to work.

To work with TPUs  and get the best performance you need Jax and XLA. To work with GPUs and get the best performance you use PyTorch and the nvidia software stack. The latter is way easier to use and debug than what google is providing.

Hardware may be better, but in terms of software Nvidia GPUs are hard to beat.

NVDA represents about 10% of TSMC business, when are the TPU getting built

The real answer that Jensen ignored is that Nvidia’s biggest moat at this point is their future contracted factory time with TSMC.

Stock is sideways since summer 25 for a reason. TPU is one. NVIDIA maybe won’t go up forever like Apple or Google

Yes it's a good competitor, but Google has been designing and using TPUs for years and years now, which didn't stop Nvidia from being the leader.

AMD cards are Nvidia competitors too. And look how well they're doing in comparison to Nvidia - most compute providers even don't offer them and when they do, it's with big discounts and in low quantity - they're second class compute provider. TPUs would be third class compute provider here. Nvidia has the massive advantage of CUDA being accessible as well to normal consumers in gaming GPUs, having workstation GPUs as well as data-centre GPUs. While TPUs are only available from specific providers and you need to dedicate thousands of engineering hours to make efficient training of your custom architecture on them - possible but the reference implementation is going to be Nvidia and Nvidia is what you'll find in offer by a random data center compute provider for a long while now.

Jensen's comments are about the workload. That workload can take on a bunch of different forms, some of which we don't even know the shape of. What Nvidia have always been the best at is understanding where the hardware needs to be in the future. They've done this on GPU design and now do it on networking and the software to drive the entire system. The narrative around 'GPU vs TPU' is extremely narrow and represents just a small piece of the picture.

We are just seeing the start of the fruits of the Blackwell deployments coming online and the training they have powered. The upgrade to RV is incoming and the TCO of serving these massive training and inference workloads for multi-trillion sized models is just starting to factor into decision making.

Remember for all these companies with chip ambitions, they have to design, manufacture, deploy, network and support them with sophisticated software/fleet management.

No doubt, some of Nvidia's market share will erode as the market is compute constrained but that doesn't mean Nvidia lose business, the industry is just getting started and Nvidia holds key cards in all hardware areas GPU/CPU/Networking/Robotics/Driving and the software stack to operate in all these domains.