How Google Uses TPUs to Power AI

How Google Uses TPUs to Power AI

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When people talk about AI computing, NVIDIA GPUs usually get most of the attention.

But one of the world’s largest AI infrastructure platforms has been building a different type of processor for more than a decade.

Google’s Tensor Processing Units, or TPUs, are custom-designed AI accelerators built specifically for machine-learning workloads. Unlike general-purpose CPUs, TPUs are designed around the mathematical operations that neural networks perform repeatedly, particularly large matrix and tensor operations. (Google Cloud TPU documentation)

Today, TPUs are used across Google’s AI infrastructure, including the systems behind Gemini and other Google products. Google also makes TPU infrastructure available through Google Cloud for organizations that want to train and deploy their own AI models. (Google Cloud TPU documentation)

The important part is that Google does not treat the TPU as just a chip.

It is part of a much larger AI computing system that includes:

  • TPU accelerators
  • high-bandwidth memory
  • high-speed networking
  • machine-learning frameworks
  • compilers
  • data centers
  • storage
  • Google’s AI models
  • Google Cloud infrastructure

Understanding that system explains why TPUs are so important to Google’s AI strategy.

What Is a Google TPU?

What Is a Google TPU?

A TPU is a Tensor Processing Unit.

It is an application-specific integrated circuit, or ASIC, designed by Google to accelerate machine-learning workloads.

Traditional CPUs are designed to perform a huge variety of tasks.

GPUs are more specialized for parallel computation, which makes them particularly useful for AI. Our guide on how AI chips work explains the differences between GPUs, TPUs and other AI accelerators in more detail.

TPUs go another step further.

They are designed specifically around the types of mathematical operations that dominate neural-network workloads.

Google describes TPUs as custom accelerators optimized for AI workloads ranging from large language models and code generation to intelligent agents. Google also describes them as the infrastructure behind Gemini and other major Google products. (Google Cloud TPU documentation)

A simplified comparison looks like this:

CPU
 │
 ├── General-purpose computing
 ├── Operating systems
 ├── Applications
 └── Many different workloads


GPU
 │
 ├── Highly parallel computing
 ├── Graphics
 ├── AI
 └── Scientific computing


TPU
 │
 ├── Machine learning
 ├── Matrix operations
 ├── Neural networks
 └── Large-scale AI workloads

This specialization is the fundamental idea behind Google’s TPU strategy.

Why AI Needs Specialized Hardware

Modern AI models perform enormous numbers of mathematical operations.

Consider a neural network.

At a simplified level, it repeatedly performs operations such as:

Input data
    ↓
Matrix multiplication
    ↓
Activation
    ↓
Matrix multiplication
    ↓
Activation
    ↓
Output

Large language models perform these operations at enormous scale.

A model may contain billions or even hundreds of billions of parameters.

During training, the system repeatedly processes huge amounts of data while adjusting those parameters.

During inference, the model performs more calculations every time it generates tokens.

This creates an enormous demand for computational throughput.

That is where specialized AI accelerators become useful.

How a TPU Processes AI Calculations

How a TPU Processes AI Calculations

One of the most important components inside a TPU is its Matrix Multiply Unit, or MXU.

The MXU is designed to perform large numbers of multiply-and-accumulate operations efficiently.

Google’s TPU architecture uses a structure called a systolic array.

Instead of repeatedly moving data back and forth between computation units and memory, data can flow through interconnected arithmetic units while calculations are performed.

Conceptually:

Input
  ↓
┌───┬───┬───┬───┐
│ × │ × │ × │ × │
└───┴───┴───┴───┘
  ↓
┌───┬───┬───┬───┐
│ + │ + │ + │ + │
└───┴───┴───┴───┘
  ↓
Result

Google’s current TPU documentation describes the MXU and systolic-array architecture as fundamental parts of how TPUs accelerate matrix operations. (Google Cloud TPU documentation)

This architecture is particularly well suited to neural-network mathematics.

TPUs Use High-Bandwidth Memory

Computational power alone is not enough to run large AI models.

The accelerator also needs fast access to model parameters and intermediate data.

This is why memory has become one of the most important parts of AI hardware.

Google’s TPU systems use High Bandwidth Memory (HBM) alongside their compute hardware.

A simplified view is:

             TPU
              │
       ┌──────┴──────┐
       │             │
   Compute          HBM
       │             │
       └──────┬──────┘
              │
        AI model data

During computation, model parameters and other data need to move efficiently between memory and the processing units.

The eighth-generation TPU architecture illustrates how important this has become. TPU 8t provides 216 GB of HBM per chip, while TPU 8i provides 288 GB, according to Google’s technical documentation. (Google Cloud TPU 8 technical deep dive)

This becomes especially important as AI models grow and workloads increasingly involve long contexts and large amounts of intermediate data.

Google’s TPU Evolution

Google has not built just one TPU.

The company has developed multiple generations as AI workloads have changed.

The progression has broadly moved toward:

Early TPUs
    ↓
Faster matrix computation
    ↓
Larger memory
    ↓
Higher bandwidth
    ↓
Better interconnects
    ↓
Larger TPU clusters
    ↓
Training + inference optimization
    ↓
Agentic AI workloads

Google’s TPU family has progressed through multiple generations, including Trillium and Ironwood, before the eighth-generation TPU systems.

Google’s current TPU documentation identifies Ironwood as the seventh generation, while TPU 8t and TPU 8i represent the eighth generation. (Google Cloud TPU documentation)

The important trend is that Google is increasingly designing different TPU systems around different AI workloads.

TPU 8t and TPU 8i: Different Chips for Different Jobs

One of the most interesting changes in Google’s latest TPU strategy is specialization.

Google’s eighth-generation TPU systems include:

  • TPU 8t for large-scale training
  • TPU 8i for inference, post-training and reinforcement-learning workloads

Google describes TPU 8t as its training-focused system and TPU 8i as a system optimized for inference and reasoning workloads. (Google Cloud TPU technical deep dive)

The reason is simple.

Training and inference have different requirements.

Training:

Huge datasets
     ↓
Massive parallel computation
     ↓
Large TPU clusters
     ↓
Repeated optimization
     ↓
Model parameters updated

Inference:

User request
     ↓
Model
     ↓
Generate tokens
     ↓
Return response

Inference happens continuously after a model has been trained.

When millions of users interact with an AI system, inference can become an enormous infrastructure challenge.

That means optimizing the hardware for inference can be just as important as optimizing it for training.

TPU 8t Is Designed for Large-Scale Training

Training a frontier AI model requires enormous computational resources.

TPU 8t is designed specifically for this type of workload.

Google says TPU 8t can scale to 9,600 chips in a single superpod, with the system designed for massive-scale pre-training and embedding-heavy workloads. (Google Cloud TPU documentation)

The basic architecture looks like:

               AI Model
                   │
        ┌──────────┴──────────┐
        │                     │
      TPU                     TPU
        │                     │
      TPU                     TPU
        │                     │
        └──────────┬──────────┘
                   │
            High-speed network
                   │
        ┌──────────┴──────────┐
        │                     │
      TPU                     TPU
        │                     │
      TPU                     TPU

The objective is not simply to make one TPU faster.

The objective is to make thousands of TPUs work together efficiently.

That is a much harder engineering problem.

TPU Clusters Are More Important Than Individual Chips

TPU Clusters Are More Important Than Individual Chips

A modern AI accelerator cannot be viewed in isolation.

Training large models requires many accelerators to communicate with each other.

If the chips are extremely fast but the network between them is slow, the overall system can waste computing capacity waiting for data.

This is why Google’s TPU infrastructure includes specialized interconnects and networking.

Google’s eighth-generation TPU architecture uses different network approaches for its training and inference systems. TPU 8t uses a 3D torus topology, while TPU 8i uses a serving-oriented topology called Boardfly. (Google Cloud TPU technical deep dive)

A simplified system looks like:

TPU ─── TPU ─── TPU ─── TPU
 │       │       │       │
 ├───────┼───────┼───────┤
 │       │       │       │
TPU ─── TPU ─── TPU ─── TPU
 │       │       │       │
 └───────┴───────┴───────┘
             │
        TPU cluster

This is one of the major differences between building an AI chip and building an AI supercomputer.

Google Uses TPUs for Gemini

One of the biggest reasons TPUs matter is Google’s own AI models.

Google says TPUs are the engine behind Gemini and other major Google products. (Google Cloud TPU documentation)

That means Google’s AI hardware and AI models have developed together.

The relationship can be represented as:

Google AI research
        ↓
AI models
        ↓
Software / compilers
        ↓
TPU architecture
        ↓
TPU clusters
        ↓
Google data centers
        ↓
AI products

This gives Google control over several layers of the AI stack.

Instead of relying entirely on an external accelerator provider, Google can design hardware around the workloads its own AI teams expect to run.

TPUs Are Part of Google’s Full AI Stack

The biggest advantage of Google’s TPU strategy is not simply the processor itself.

It is the integration between hardware and software.

Google’s TPU environment supports frameworks including JAX and PyTorch, while XLA helps translate and optimize machine-learning computations for TPU hardware. Google’s latest TPU documentation also highlights native PyTorch support and vLLM support for inference. (Google Cloud TPU documentation)

The stack can be simplified as:

AI application
       ↓
AI model
       ↓
JAX / PyTorch
       ↓
XLA compiler
       ↓
TPU runtime
       ↓
TPU hardware
       ↓
Networking + memory
       ↓
Google data center

This is important because AI performance depends on the entire stack.

A powerful processor with inefficient software will not automatically deliver good performance.

Why Google Does Not Use Only TPUs

It would be incorrect to assume that Google uses TPUs instead of GPUs everywhere.

Google operates a heterogeneous AI infrastructure environment.

Google’s 2026 AI infrastructure announcements explicitly include both its custom TPUs and NVIDIA GPU platforms as part of Google Cloud’s AI accelerator portfolio. (Google Cloud AI infrastructure)

There are several reasons for this.

GPUs have:

  • enormous software ecosystems
  • broad framework support
  • large developer communities
  • extensive third-party optimization
  • flexibility across many workloads

TPUs have:

  • deep integration with Google’s infrastructure
  • specialized AI hardware
  • tight software-hardware integration
  • large-scale cluster capabilities

So the practical situation is closer to:

Google AI infrastructure

        ┌───────────────┐
        │   AI Workload │
        └───────┬───────┘
                │
       ┌────────┴────────┐
       │                 │
      TPU               GPU
       │                 │
 Google-designed     NVIDIA
 infrastructure     ecosystem
       │                 │
       └────────┬────────┘
                │
             AI systems

Google can therefore choose the most appropriate hardware for a particular workload.

For a broader explanation of why GPUs remain central to AI computing, see Why NVIDIA GPUs Are Important for AI.

TPUs and AI Inference

Training gets much of the attention, but inference is becoming increasingly important.

Every time an AI model generates a response, inference hardware performs the required calculations.

At massive scale, the number of inference requests can be enormous.

For an AI service:

Millions of users
       ↓
Millions of requests
       ↓
Large-scale inference
       ↓
Huge compute demand

This is why Google’s newer TPU designs are increasingly focused on inference efficiency.

Google says TPU 8i is optimized for post-training and inference and includes significantly more on-chip SRAM to help keep larger KV caches close to the compute units during long-context decoding. (Google Cloud TPU technical deep dive)

For a deeper explanation of what happens when a trained AI model generates an answer, see AI Inference: How AI Models Generate Responses.

That matters for modern AI systems because long-context models and reasoning systems can require significantly more memory and computation.

TPUs and AI Agents

AI agents introduce another type of workload.

A traditional chatbot might perform one inference request:

Prompt
  ↓
Model
  ↓
Answer

An agent may perform many steps:

User goal
   ↓
Reason
   ↓
Search
   ↓
Read information
   ↓
Reason again
   ↓
Use a tool
   ↓
Inspect result
   ↓
Reason again
   ↓
Take another action
   ↓
Complete task

That can require many inference operations for a single user request.

Google’s eighth-generation TPU architecture is explicitly designed around these newer workloads. Google says TPU 8t and TPU 8i were developed for the demands of agentic AI, with TPU 8i focused particularly on low-latency inference and reasoning workloads. (Google Cloud TPU technical deep dive)

For more background, see AI Agents: How They Work and Why They Matter.

This is an important shift.

The future AI infrastructure challenge may not simply be training bigger models.

It may be running billions of model interactions efficiently.

Why Google Designs Its Own AI Chips

There are several strategic reasons for Google’s TPU investment.

1. Performance

Google can design hardware specifically around the mathematical operations its AI models need.

2. Efficiency

Specialized hardware can potentially reduce unnecessary computation and data movement for targeted workloads.

3. Infrastructure Control

Google can optimize the accelerator, networking, memory and software stack together.

4. Supply Diversity

Building its own accelerators reduces Google’s dependence on a single external hardware architecture.

5. AI Product Integration

Google can design infrastructure around the requirements of Gemini, Search, Cloud and other products.

This is part of a larger industry trend.

Microsoft, Amazon, Meta and other major technology companies are also developing custom AI silicon.

For a broader look at this trend, see Why AI Companies Are Building Their Own Chips.

The competition is therefore no longer only about AI models.

It is also about who can build the most efficient infrastructure underneath those models.

TPU vs GPU: The Important Difference

The TPU-versus-GPU debate is often presented too simply.

It is not really:

TPU = better
GPU = worse

The two technologies have different design goals.

FeatureTPUGPU
Primary designAI / tensor workloadsParallel computing
FlexibilityMore specializedMore general
Matrix operationsHighly optimizedHighly optimized
AI trainingExcellentExcellent
AI inferenceExcellentExcellent
Software ecosystemGoogle-focused ecosystemExtremely broad ecosystem
Custom integrationVery high within GoogleBroad vendor ecosystem
Large AI clustersDesigned for large-scale TPU systemsDesigned for large-scale GPU systems

The best choice depends on the workload, software stack, cost, availability and infrastructure requirements.

The Real Advantage: Google Controls More of the Stack

The most important part of Google’s TPU strategy is not that Google has created another AI accelerator.

It is that Google controls multiple layers of the system.

                Google AI Stack

             AI Applications
                    ↓
              Gemini Models
                    ↓
          AI Frameworks / JAX
                    ↓
              XLA Compiler
                    ↓
                 TPUs
                    ↓
          Memory + Networking
                    ↓
             AI Data Centers
                    ↓
              Google Cloud

This level of integration gives Google an opportunity to optimize the complete system rather than optimizing a processor in isolation.

That becomes increasingly valuable as AI workloads become larger and more complex.

TPUs Are Also a Google Cloud Product

Google does not keep TPU technology exclusively inside its own infrastructure.

Google Cloud makes TPUs available to developers and organizations through its cloud infrastructure and associated AI services. (Google Cloud TPU documentation)

This creates another business opportunity.

A company can build an AI application using Google’s infrastructure without designing its own accelerator hardware.

The model can run on Google’s TPU infrastructure while the customer interacts with it through Google Cloud services.

In simplified form:

Developer
    ↓
Google Cloud
    ↓
AI workload
    ↓
TPU cluster
    ↓
Model
    ↓
Application

Google therefore benefits both from using TPUs internally and from making TPU infrastructure available to cloud customers.

This also connects TPUs directly to the broader evolution of cloud infrastructure and AI data centers. See our article on How AI Data Centers Are Different From Traditional Data Centers.

Why TPUs Matter to Google’s AI Strategy

Google’s TPU investment is ultimately about more than replacing GPUs.

It is about building an AI computing platform that Google can optimize from the silicon level upward.

The strategy looks something like:

Custom silicon
      ↓
Specialized hardware
      ↓
High-speed networking
      ↓
AI software stack
      ↓
Large-scale clusters
      ↓
Foundation models
      ↓
AI products
      ↓
Google Cloud

That creates a powerful feedback loop.

Google’s AI researchers develop new models.

Those models create new infrastructure requirements.

Google can then modify its hardware and software to address those requirements.

The improved infrastructure allows Google to build and operate even larger AI systems.

What Comes Next for Google TPUs?

AI workloads are changing quickly.

Models are becoming larger.

Context windows are becoming longer.

Reasoning systems are using more computation.

AI agents are performing more steps.

Multimodal systems are processing more types of data.

All of these trends create new infrastructure requirements.

Google’s eighth-generation TPU systems show where the company is heading: rather than treating all AI workloads identically, Google is increasingly designing different accelerator systems around different stages of the AI lifecycle. (Google Cloud TPU technical deep dive)

The future may therefore look less like one universal AI processor and more like a collection of specialized systems:

                    AI Infrastructure

                         AI Model
                            │
          ┌─────────────────┼─────────────────┐
          │                 │                 │
       Training          Inference         Agents
          │                 │                 │
        TPU 8t            TPU 8i         Specialized
          │                 │             workloads
          └─────────────────┼─────────────────┘
                            │
                     AI Data Center

That is a major change in how AI infrastructure is being designed.

Final Thoughts

Google’s Tensor Processing Units are one of the clearest examples of how AI is changing computer hardware.

Instead of relying entirely on general-purpose processors, Google designed an accelerator specifically around the mathematical structure of neural networks.

But the real story is bigger than the TPU chip itself.

Google combines:

  • custom AI accelerators
  • high-bandwidth memory
  • specialized networking
  • large TPU clusters
  • JAX and PyTorch
  • the XLA compiler
  • Google data centers
  • Gemini and other AI models
  • Google Cloud

Together, these components form an integrated AI computing platform.

That is why TPUs have become strategically important to Google.

The competition in AI is no longer only about who has the best model.

It is also about who can train, serve and scale those models most efficiently.

And Google’s TPU strategy gives the company control over one of the most important layers underneath the AI revolution: the hardware that makes modern AI possible.

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