Touching the Elephant – TPUs

https://considerthebulldog.com/tte-tpu/

#HackerNews #Touching #the #Elephant #TPUs #TPU #Technology #AI #Hardware #Machine #Learning

"If you go back a year or two, you might make the case that Nvidia had three moats relative to TPUs: superior performance, significantly more flexibility due to GPUs being more general purpose than TPUs, and CUDA and the associated developer ecosystem surrounding it. OpenAI, meanwhile, had the best model, extensive usage of their API, and the massive number of consumers using ChatGPT.

The question, then, is what happens if the first differentiator for each company goes away? That, in a nutshell, is the question that has been raised over the last two weeks: does Nvidia preserve its advantages if TPUs are as good as GPUs, and is OpenAI viable in the long run if they don’t have the unquestioned best model?

Nvidia’s flexibility advantage is a real thing; it’s not an accident that the fungibility of GPUs across workloads was focused on as a justification for increased capital expenditures by both Microsoft and Meta. TPUs are more specialized at the hardware level, and more difficult to program for at the software level; to that end, to the extent that customers care about flexibility, then Nvidia remains the obvious choice.

CUDA, meanwhile, has long been a critical source of Nvidia lock-in, both because of the low level access it gives developers, and also because there is a developer network effect: you’re just more likely to be able to hire low level engineers if your stack is on Nvidia. The challenge for Nvidia, however, is that the “big company” effect could play out with CUDA in the opposite way to the flexibility argument. While big companies like the hyperscalers have the diversity of workloads to benefit from the flexibility of GPUs, they also have the wherewithal to build an alternative software stack. That they did not do so for a long time is a function of it simply not being worth the time and troube..."

https://stratechery.com/2025/google-nvidia-and-openai/

#AI #GenerativeAI #Nvidia #Google #ChatGPT #OpenAI #LLMs #Chatbots #CUDA #GPUs #TPUs

"In the blistering race for AI supremacy, Nvidia has long reigned as the undisputed king. Its GPUs powered the explosive growth of machine learning, turning abstract neural networks into reality and fueling an empire valued at trillions. But as the AI landscape evolves, cracks are appearing in Nvidia's armor. The shift from model training (Nvidia's stronghold) to inference, the real-time application of those models, is reshaping the market. And at the forefront of this revolution stands Google's Tensor Processing Units (TPUs), delivering unmatched efficiency and cost savings that could spell the end of Nvidia's monopoly.

By 2030, inference will consume 75% of AI compute, creating a $255 billion market growing at 19.2% annually. Yet most companies still optimize for training costs. This isn't just hype; it's economics. Training is a one-time sprint, but inference is an endless marathon. As companies like OpenAI grapple with skyrocketing inference bills (projected at $2.3 billion for 2024 alone, dwarfing the $150 million cost to train GPT-4), Google's TPUs emerge as the cost-effective powerhouse. In this in-depth analysis, we'll explore how TPUs are winning the inference war, backed by real-world migrations from industry leaders, and why this pivot signals Nvidia's impending decline."

https://www.ainewshub.org/post/ai-inference-costs-tpu-vs-gpu-2025

#AI #AIInference #GenerativeAI #Nvidia #Google #GPUs #TPUs #LLMs

TPUs vs. GPUs and why Google is positioned to win AI race in the long term

https://www.uncoveralpha.com/p/the-chip-made-for-the-ai-inference

#HackerNews #TPUs #GPUs #Google #AI #race #technology