TPUs vs GPUs: How Google's Tensor Processing Units Actually Work

<p>If you've worked with machine learning on Google Cloud, you've hit the choice: GPU instance or TPU? Most teams default to GPU because that's what they already know. But as

<p><strong>TL;DR:</strong> Google's TPU 8 is not one chip. It is two: an 8t for training and an 8i for inference. The 121 ExaFlops number is the headline. The split is the story.

<p>If you've shopped for a GPU for ML work, you've seen both numbers on the spec sheet — CUDA cores and Tensor cores — usually with zero explanation of what either actually does

<p>A guide to Cloud TPU generations, what changed between them, and how to choose the right one for your workload</p> <p><a class="article-body-image-wrapper"

<!-- SC_OFF --><div class="md"><p><strong>Cost &amp; Performance Efficiency</strong></p> <ul> <li><strong>Training Cost-Performance (8t):</strong> +170% to +180% gain

<p>Most people still think AI is about better answers. That phase is already behind us. What is emerging now is something fundamentally different: AI that reasons. Systems that do

<ul> <li> <strong>Book:</strong> <a href="https://www.amazon.com/dp/B0GYLHMLMT" rel="noopener noreferrer">LLM Observability Pocket Guide</a> </li> <li> <strong>Also by