What is TPU (Tensor Processing Unit)?

What Is a TPU?

A TPU, or Tensor Processing Unit, is an application-specific integrated circuit (ASIC) developed by Google to handle the mathematical operations at the heart of machine learning. Unlike GPUs, which were originally designed for graphics rendering and later adapted for AI, TPUs were purpose-built from the ground up to accelerate tensor computations, the core mathematical structures used in deep learning.

Google first introduced TPUs internally in 2015 to power its own services, including Google Search, Google Translate, and Google Photos. Since 2018, TPUs have been available to businesses through Google Cloud Platform, giving organisations access to the same hardware that powers Google's own AI products.

How TPUs Work

At a technical level, TPUs are optimised for matrix multiplication and other linear algebra operations that dominate machine learning workloads. While GPUs handle these tasks by distributing them across thousands of small cores, TPUs use a different architecture called a systolic array, which passes data through a grid of processing units in a highly efficient pipeline.

Key architectural advantages of TPUs include:

• High memory bandwidth: TPUs are designed with large amounts of high-bandwidth memory (HBM), allowing them to process massive datasets without bottlenecks
• Bfloat16 precision: TPUs use a custom number format called bfloat16 that maintains training accuracy while requiring less memory and computation than standard 32-bit floating point
• Interconnect fabric: TPU pods, clusters of TPUs linked together, use custom high-speed interconnects that allow thousands of chips to work together on a single training job

The latest generation, TPU v5p, delivers significantly more compute power per dollar compared to earlier versions, and Google continues to invest heavily in each new generation.

TPUs vs GPUs

The most common question business leaders ask is how TPUs compare to GPUs. The answer depends on your use case:

Where TPUs Excel

• Large-scale model training: TPUs are exceptionally efficient at training large language models and other transformer-based architectures
• Google Cloud workloads: If your AI infrastructure is built on Google Cloud, TPUs integrate seamlessly with TensorFlow, JAX, and other Google-supported frameworks
• Cost efficiency at scale: For sustained, large-scale training jobs, TPUs often deliver better price-performance than equivalent GPU configurations

Where GPUs May Be Preferable

• Framework flexibility: GPUs have broader support across all major frameworks including PyTorch, which has a larger community ecosystem
• Multi-cloud strategies: GPUs are available from every major cloud provider, while TPUs are exclusive to Google Cloud
• Inference diversity: For running many different small models, GPUs offer more flexibility

For most businesses, the choice between TPUs and GPUs comes down to your cloud provider, your preferred frameworks, and the scale of your workloads.

Why TPUs Matter for Businesses in Southeast Asia

Google Cloud has a significant presence in Southeast Asia, with data centres and cloud regions in Singapore and Jakarta. This means businesses across ASEAN can access TPU infrastructure with low latency and in compliance with regional data residency requirements.

For organisations already using Google Cloud services, TPUs offer a compelling option for AI workloads:

• Cost savings: For large training jobs, TPUs can reduce compute costs by 30-50% compared to equivalent GPU instances
• Speed: TPU pods can dramatically accelerate training times, turning week-long jobs into hours
• Managed infrastructure: Google handles all hardware maintenance, allowing your team to focus on model development rather than infrastructure management

Several Southeast Asian companies in fintech, e-commerce, and logistics have adopted TPUs to train recommendation systems, fraud detection models, and natural language processing systems at scale.

Getting Started with TPUs

For businesses considering TPUs, a practical approach includes:

1. Evaluate your framework: TPUs work best with TensorFlow and JAX. If your team primarily uses PyTorch, check the latest compatibility support, as Google has been expanding PyTorch/XLA integration
2. Start with Cloud TPU VMs: Google Cloud offers on-demand TPU instances that allow you to experiment without long-term commitment
3. Use preemptible TPUs for cost savings: For training workloads that can tolerate interruptions, preemptible TPU instances offer significant discounts
4. Leverage TPU Research Cloud: Startups and research teams may qualify for free TPU access through Google's TRC programme
5. Benchmark against GPUs: Run the same workload on both TPUs and GPUs to determine which delivers better performance and cost efficiency for your specific use case

TPUs represent an important option in the AI infrastructure landscape. While they are not the right choice for every workload, for organisations operating within the Google Cloud ecosystem and running large-scale AI training, they can deliver meaningful advantages in speed and cost.