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Turn any lecture into flashcards, quizzes, videos and shorts in one click 👉 https://lecturescribe.io/y/en Can Google beat Nvidia in the AI chip race? TPU vs GPU explained: how the hardware works, why CUDA matters, and what determines the cost of training and running AI. Google built its first Tensor Processing Unit to solve a problem inside its own data centers. Today, TPUs compete for AI workloads far beyond Google and for the next customer order. This animated explainer follows the story from Google's first TPU to Ironwood, Gemini and the growing demand for AI compute. We break down: • How Google TPUs differ from Nvidia GPUs • AI training vs inference, and why moving data matters • Why CUDA makes Nvidia's software ecosystem hard to leave • Where PyTorch, JAX and XLA fit into the picture • Why Anthropic uses multiple types of AI chips • How price, performance and software shape the final decision The question goes beyond which chip is faster. Which system helps a team get useful work done at the best cost—and how easy is it to use? What would make you switch: lower cost, better performance, or easier software? Share your take in the comments. 0:00 Anthropic buys from both 0:26 2013: Google's voice search problem 0:55 Why GPUs work for AI 1:36 How a TPU differs from a GPU 2:12 The ants and the pond 2:27 The first TPU and AlphaGo 3:03 TPUs open up to other companies 3:26 CUDA: Nvidia's roots 4:20 Inference, the job that never stops 5:21 November 2025: Nvidia responds 6:01 What it really costs 6:40 Why Anthropic uses both 7:08 What has changed 7:43 What would it mean for Google to win? #TPU #GPU #AIChips #Jensen #LLM #AI

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