What Is TPU?

⚙️ What Is a TPU (Tensor Processing Unit)?

A Tensor Processing Unit (TPU) is a custom-designed AI accelerator developed by Google to speed up machine-learning workloads—especially deep-learning operations built on large tensor and matrix computations. Unlike CPUs or GPUs, TPUs are specialised ASICs engineered for high-throughput, high-efficiency neural-network training and inference at scale.

⚙️ Why Google Built the TPU

Optimised for Deep Learning

Neural networks require massive parallel math operations, mainly matrix multiply-accumulate tasks. CPUs struggle with these workloads, while GPUs, although powerful, are general-purpose accelerators.
TPUs were created to:

  • Deliver extremely high performance per watt

  • Maximise matrix-multiplication throughput

  • Support large-scale AI models cost-effectively

  • Meet rising internal demand across Google Search, Translate, YouTube, Maps, and AI models

AI-First Design

From the beginning, the TPU architecture focused on:

  • Hardware-software co-design with TensorFlow

  • Reduced precision formats (e.g. bfloat16, int8) for energy-efficient compute

  • Scalable fabrics for multi-chip clustering

⚙️ TPU Architecture Explained

TPU Architecture

Systolic Matrix Engines

At the core of each TPU chip is a massive matrix multiplication unit arranged in a systolic array, enabling thousands of simultaneous multiply-accumulate operations.

High-Bandwidth Memory

Modern TPUs integrate HBM to feed data at extremely high bandwidth, preventing memory bottlenecks common in GPU-based systems.

Interconnect & Scalability

Individual TPUs scale into TPU Pods, interconnected with low-latency, high-bandwidth networks for multi-exaflop modular AI clusters.
This architecture enables extremely large model training and faster inference at hyperscale.

⚙️ TPU Generations and Key Specs

Generation

Focus

Memory & Compute

Notes

TPU v1

Inference

8-bit compute

First internal deployment

TPU v2

Training & Inference

bfloat16, HBM

Cloud TPU launched

TPU v3

Large-scale training

Liquid cooling, HBM

Pod up to ~1K chips

TPU v4

Efficient exascale pods

32GB HBM, advanced mesh

Data-center scale

TPU v6 “Trillium”

High-density AI compute

Multiple HBM stacks

~5× perf vs prior

TPU v7 “Ironwood”

Inference-first architecture

FP8 optimisation

Built for LLM serving

⚙️ TPU vs GPU vs CPU

TPU vs GPU vs CPU

Feature

TPU

GPU

CPU

Purpose

AI-specific tensor compute

Graphics + ML acceleration

General compute

Best For

Neural networks, LLMs

HPC, ML, graphics

OS, logic, apps

Parallelism

Extremely high

High

Low

Efficiency

Highest for AI workloads

High

General purpose

Deployment

Cloud & clusters

Cloud & on-prem

Everywhere

In short:

CPUs are universal. GPUs are versatile. TPUs are laser-focused on AI at scale.

⚙️ Where TPUs Are Used

Large-Scale Model Training

Ideal for transformer models, recommendation systems, and large-language-model training pipelines.

Cloud Inference

TPUs power global AI workloads such as search ranking, language translation, speech recognition, and generative AI services.

Edge TPU

A lightweight TPU variant runs ML inference locally in edge/embedded devices for low-latency AI and power-efficient IoT intelligence.

⚙️ Best Practices for TPU Deployment

  • Use supported data types (bfloat16 / int8) for maximum efficiency

  • Optimise data pipelines for distributed compute

  • Choose TPU Pods for LLM-scale workloads

  • Consider thermal and network design for cluster scalability

  • Leverage hybrid cloud + edge strategies for balanced compute density

⚙️ TPUs and the Future of AI Infrastructure

AI models are more compute-intensive than ever, shifting focus from pure training to real-time inference at scale.
TPUs will continue advancing in:

  • Interconnect density

  • Energy-efficient architectures

  • Hybrid precision (e.g., FP8)

  • Integration with software frameworks (TensorFlow, JAX, PyTorch via XLA)

As AI workloads accelerate, specialised compute and ultra-high-speed connectivity become essential components of modern data-centre and network design.

⚙️ How This Relates to LINK-PP

AI acceleration at hyperscale depends on advanced networking and robust connectivity infrastructure. LINK-PP components support the data-center environment that powers TPU deployments, including:

⚙️ Conclusion

TPUs represent a major leap in specialised AI computing—purpose-built for tensor workloads and large-scale neural-network operations. As generative AI and deep-learning adoption accelerate globally, TPUs play a crucial role in powering training clusters and inference infrastructure.

For industries building or supporting modern data-centre environments, understanding TPU technology provides valuable insight into the demands of high-performance AI systems—and opportunities in next-generation networking hardware and components.