Hardware & Compute

TPUs

Google's custom AI chips, and why they give Google a unique strategic advantage

What it is

TPUs (Tensor Processing Units) are custom ASICs designed by Google specifically for the matrix mathematics required by neural networks. Unlike GPUs, which are general-purpose parallel processors adapted for AI, TPUs are purpose-built for lower-precision tensor operations.

Google's Gemini series is trained and served entirely on TPUs, making Google the only major AI lab not dependent on NVIDIA's supply chain. Google is now licensing TPU access through Google Cloud, with Anthropic entering a significant partnership for TPU compute.

Why it matters

Google's TPU independence is a significant strategic moat. While OpenAI and Anthropic pay NVIDIA's premium, Google controls its own compute cost structure. Understanding TPUs explains why Anthropic's Google partnership is so significant, why hardware independence matters strategically, and why chip geopolitics affects AI lab strategy.

Related concepts

GPUs

Resources

How Google's Tensor Processing Unit (TPU) Works

blog.bytebytego.com· Excellent deep-dive with systolic array diagrams, TPU v1 through v5+ evolution

15 min

An In-Depth Look at Google's First Tensor Processing Unit

cloud.google.com· Official Google technical post with floor plan diagrams and matrix multiply unit explanations

12 min

Introduction to Cloud TPUs: History and Hardware

youtube.com· Official Google TPU video series, covers origins, why Google designed an ASIC for AI, architecture compared to CPUs/GPUs

15 min

Ask a Techspert: What's the difference between a CPU, GPU and TPU?

blog.google· Highly accessible Q&A format, plain language, introduces Trillium (6th-gen TPU), very beginner-friendly

6 min