Learn the trade-offs in flexibility, cost, and specialization between TPUs, GPUs, and CPUs for your organization's AI and machine learning projects.
Tensor processing units (TPUs) are specially designed AI accelerators. They are a type of application-specific integrated circuit (ASIC), or chips designed for specific tasks. For TPUs, that task is running and optimizing AI and machine learning (ML) workflows, including training and inference.
Unlike CPUs that serve as the backbone of traditional computing, or GPUs used for more advanced computing and gaming, TPUs are specifically built to handle the complex calculations demanded by AI, more specifically large language models (LLMs) and generative AI.
TPUs are ideal for a wide range of use cases, including code and content generation (text, audio, video, 3D models), recommendation engines, computer vision, natural language processing (NLP), generative AI, and agentic AI.
TPUs were originally developed by Google to speed up and improve the performance of their AI applications, including Google Search, Translate, and Photos. Google specifically designed the chips to accelerate operations in TensorFlow, the open-source ML framework it built to support neural network algorithms. TensorFlow has the ability to offload operations from CPUs and GPUs.
Google made TPUs available to the wider market in 2018, primarily via TPU-powered cloud server instances on Google Cloud. The specialized chips have since become a critical component in AI infrastructure.
How TPUs work
AI platforms and their underlying ML models require intensive mathematical processing.
Fundamentally, TPUs are optimized for a type of mathematical operation known as tensor computation.
Tensors are multi-dimensional arrays, or matrixes, that store and process data. Think of them as fundamental data structures or “gears” in machine learning, deep learning, and scientific computing that drive neural network computations and data analysis.
TPUs employ giant groups of multiply-and-accumulate arithmetic logic units (ALUs) that form specialized processing blocks known as tensor cores or matrix multiply units (MXUs). This infrastructure is able to perform addition, multiplication, linear algebra, and convolution, a critical computation in ML that allows systems to extract features from data.
In simple terms, TPUs take in data, break it down into multiple tasks (vectors), simultaneously perform required math on each vector, then return outputs to models.
Tensor operations are foundational to deep learning algorithms because they can process vast datasets simultaneously — including those that incorporate complex data such as images, audio, and video — via parallelism, rapid matrix math, and high memory bandwidth.
TPUs are designed to directly target performance bottlenecks, allowing them to make predictions much more quickly than GPUs or CPUs. TPUs also support reduced precision arithmetic (such as 16-bit floating point operations), that allows for more computations per second without sacrificing accuracy (in the case of most AI workloads). They can perform matrix processing much faster using far less power, and their architecture reduces the need for unnecessary computations.
Advantages of TPUs
TPUs are essential to advancing AI because they can support model training and deployment much more quickly and at far greater scale than traditional architectures like GPUs and CPUs. Their key advantages:
- Purpose-built architecture: TPUs are specifically designed to support matrix and tensor operations, driving more efficient training and inference. What could take days or weeks with GPUs or CPUs can be dramatically sped up.
- Massive parallelism: Enormous arrays of multiply-and-accumulate arithmetic logic units (ALUs) allow for fast, concurrent computations. This can support large batch sizes and complex architectures.
- Scalability: TPUs can essentially be joined together in pods — in clusters of hundreds or even thousands — for exascale compute. Enterprises can train massive models to support voice recognition, language translation, recommender systems, and image and other content generators.
- Excellent throughput and performance: At least when it comes to neural network tasks, TPUs regularly outperform GPUs in both speed and energy efficiency. Some benchmarks report anywhere from 2.5X to 4X greater performance and throughput as well as significant reductions in total training time.
- Energy efficiency: Purpose-built circuitry and optimized memory hierarchies allow TPUs to deliver high performance at lower power consumption than traditional architectures. This is critical when it comes to data center cost and sustainability.
- Cloud integration: TPUs are available on Google Cloud and are tightly integrated with TensorFlow and other frameworks like JAX and PyTorch. Cloud TPUs are versatile and designed to scale for training, fine-tuning, and inference. This managed approach allows dev teams to scale as needed without the need for significant upfront infrastructure investments.
What are TPUs used for?
TPUs allow for large-scale model training and high-volume inference, supporting many real-world services. A few examples:
Natural language processing (NLP): AI chatbots, translation, sentiment analysis, speech recognition.
Computer vision: Facial recognition, robotics, medical imaging, internet of things (IoT) applications.
Recommendation systems: Personalized content for web services, e-commerce, or media recommendations.
Media and content generation: Text, video, audio, 3D, even personalized podcasts.
Data analytics: Raw data processing can uncover important insights and identify patterns to improve efficiencies, identify opportunities, and support a variety of business objectives.
Edge computing: Data is processed at or near its source, such as in IoT, requiring near-or-real-time insights and throughput.
Reinforcement learning: Models determine sequences of actions to iteratively maximize rewards for their behavior. This is useful in virtual environments (recommender systems) and physical settings (robotics, autonomous driving).
TPUs vs GPUs vs CPUs
Tensor processing units (TPUs), graphics processing units (GPUs), and central processing units (CPUs) all play their own important roles in today’s computing environments.
CPUs are general-purpose and essentially serve as the backbone of today’s computing environments. Think of them as a “brain” that manages computer systems. They have been around for decades, and, at the simplest level, they are what allow computers to function. They support all software, are customizable and universally available, and scale via cores. However, they offer limited parallelism capabilities.
TPUs and GPUs both offer substantial advantages over traditional CPUs when it comes to deep learning and more complex computational tasks. However, they are optimized for different use cases and bring distinct trade-offs.
Here’s a more detailed breakdown of their differences.
Architecture and design
- TPus are custom application-specific integrated circuits (ASICs) developed by Google to support massive parallel matrix tensor operations. These ops serve as the fundamental building block for neural networks, supporting highly accelerated performance on specific AI tasks. TPUs scale through the use of increased pods (cloud clusters).
- GPUs were originally designed to render graphics for video games. They contain thousands of small graphics cores optimized for parallel computation that have been found to be well-suited for a variety of workloads beyond graphics, such as machine learning, data analytics, and advanced scientific computing. Scaling involves stringing together multiple GPUs.
Performance
- TPUs typically outperform GPUs when it comes to pure tensor-heavy workloads and large batch sizes; this is thanks to their parallelism abilities and excellent memory bandwidth. TPUs can train deep neural networks, particularly at hyperscale, more quickly and with less energy.
- GPUs are more versatile, offering strong performance across a variety of deep learning frameworks (such as TensorFlow, PyTorch, and CUDA). They can excel at both training and inference, particularly in varied network architectures where batch sizes are more moderate.
Flexibility, ecosystem, deployment options
- TPUs are specialized for AI and deep learning, and are tightly integrated with the Google Cloud ecosystem, offering high performance with TensorFlow, JAX and PyTorch. Typically, they are limited and purpose-built, and therefore less flexible for workloads that don’t require matrix-heavy neural networks.
- GPUs are known for their flexibility; they support a broad range of software ecosystems and can perform many computational tasks. GPUs are available in many configurations from numerous vendors and can be deployed in data centers, the cloud, or on hardware and edge devices.
Cost and energy efficiency
- TPUs are designed for maximum throughput per watt and cost savings at scale. Their architecture can significantly lower total training and inference costs for large neural networks.
- GPUs are cost-effective across a range of workloads, but they can use more power and require more cooling when scaling is required.
The bottom line: TPUs are ideal for large-scale, tensor-heavy deep learning, as they support high efficiency and performance. GPUs are highly flexible, accessible, and offer wide software support for simpler AI and ML tasks. CPUs are still the best choice for general-purpose computing and legacy compatibility.
It’s important to remember that, while TPUs are powerful when it comes to a variety of more complex ML and AI tasks, they are not always necessary or even useful when it comes to the traditional, everyday computing that powers enterprise (they can simply be too much when it comes to simpler tasks).
TPU challenges and limitations
Like any technology, TPUs do have their challenges.
Specialization: Some may consider TPUs too specialized and not ideal for workloads outside of matrix-heavy neural networks or projects where custom hardware is necessary.
Limited availability: TPUs are almost exclusively accessed via Google Cloud, which can restrict deployment flexibility for organizations with unique infrastructure needs.
Framework lock-in: TPUs work best with TensorFlow, although they are beginning to support other frameworks like JAX and PyTorch. GPU ecosystems, by contrast, are much broader.
Expertise required: Optimizing code for TPUs may necessitate additional developer expertise as well as workflow adjustments.
Accessing and using TPUs
As noted earlier, TPUs were initially developed internally by Google. As of yet, they are not available for direct purchase, as physical hardware or on-premises deployment. However, smaller edge TPUs are available for local device apps.
Instead, Google Cloud offers managed TPU instances for its Trillium, TPU v5p, and TPU v5e architectures. Organizations can rent TPU-equipped servers for model training, fine-tuning, and inference. Integration with frameworks like TensorFlow can help streamline workflow migration for devs and data scientists.
Google also recently announced that Ironwood, its seventh generation TPU, will be generally available this month. According to Google, Ironwood is purpose-built for demanding workloads: from large-scale model training and complex reinforcement learning (RL) to high-volume, low-latency AI inference and model serving.
Google says it offers a 10X peak performance improvement over TPU v5p and more than 4X better performance per chip for both training and inference workloads compared to TPU v6e (Trillium).
Cloud TPUs are deployed via console, API, or managed services such as Google’s Vertex AI. This fully managed AI development platform supports orchestration with Kubernetes and other cloud-native tooling.
According to Google documentation, pricing is pay-as-you-go, with discounts for longer commitments and higher volumes.
TPUs the phase of AI at scale
TPUs represent the next phase of large-scale computing and have essentially redefined the possibilities of enterprise-scale AI. Their specialized architecture can quickly and efficiently compute some of the most challenging AI workloads.
While GPUs and CPUs still play important roles in the computing ecosystem — and will continue to do so — TPUs hold the promise of unlocking new, ever more advanced opportunities with AI.
