TPU vs. GPU in artificial intelligence: What’s the difference?
Compare TPU and GPU processors and their operational profiles for various AI use cases. Explore each processor's unique advantages and find answers to frequently asked questions.
By: James M. Tobin, Edited by: Gabriela Pérez Jordán, Reviewed by: Jeff Le
Published: October 31, 2025
Tensor Processing Units (TPUs) and Graphics Processing Units (GPUs) are processor technologies commonly used in artificial intelligence (AI) and machine learning (ML). TPUs are specialized processors designed by Google specifically for ML applications, while GPUs have a more generalized and versatile profile.
The TPU vs. GPU debate involves multiple usage-based factors, including performance, accessibility, cost, and flexibility. Gaining insights into the respective profiles of TPU and GPU technologies can help you choose the right hardware for your use case while optimizing system performance and keeping cost in mind.
Explore the defining features of each technology below, and connect with educational programs that can deepen your knowledge of AI and ML hardware.
What is a TPU?
TPUs were developed by Google engineers over a decade ago as a solution for the complex and demanding computational needs of ML systems. They belong to a class of computer hardware technologies known as application-specific integrated circuits (ASICs).
A TPU's defining feature is contained in the technology's name: Tensor Processing Units were purpose-built for tensor operations, which are technical computations vital to the efficient functioning of ML computing algorithms.
TPUs excel at tensor operations because of their unique architecture, which directly supports system speed while providing ready scalability. As such, they are widely used in the large language model (LLM) training of AI and ML technologies. LLM training is what powers generative AI's ability to produce outputs that closely replicate human speech.
While TPUs are highly efficient, they are also quite expensive to access and deploy. High cost is one of the technology's most limiting downsides.
What is a GPU?
A GPU is a different kind of electronic circuit originally created for image- and graphics-based applications. While GPUs first emerged in the 1980s, 21st-century computer scientists have found new applications for the tech in AI and ML. GPUs function by segmenting complex tasks into smaller parts and performing parallel computations on those individualized parts simultaneously.
As artificial intelligence and machine learning have developed, GPUs have proven helpful in deploying their parallel processing capabilities to efficiently manage enormous quantities of data. They are used to both train and run AI models. They especially excel at applications involving natural language processing and image recognition.
GPUs offer versatility advantages, making them widely used in computing applications involving deep learning. They are also much cheaper than TPUs; however, they use more energy, which can diminish or even negate their cost advantages.
Key differences between TPUs and GPUs
| TPUs | GPUs |
|---|---|
| Improves the speed and efficiency of AI and ML workloads | Performs complex calculations at high speed |
| Capable of reaching higher raw throughput speeds in tensor operations | Versatile and well-suited to mixed computing workloads |
| Closely integrated with Google's TensorFlow ML framework Compatible with JAX | Stronger support across a variety of deep-learning frameworks, including Caffe, Keras, MXNet, PyTorch, TensorFlow |
| Most readily available on Google Cloud Platform Vendor lock-in can be an issue | Greater interoperability and access from multiple vendors |
| Higher initial cost but lower energy requirements | Lower initial cost but higher energy requirements |
How to choose the right processor for your AI project
The TPU vs. GPU question is complex, and you need to carefully evaluate the specific circumstances of your use case to make the correct choice. That said, there are situations in which one technology or the other may be a better option.
A TPU may be a better choice if:
- You are using Google's TensorFlow open-source ML framework.
- You want to reduce power input needs and energy consumption.
- High throughput and maximum speed are important performance objectives.
A GPU may be a better choice if:
- You need more framework support than TensorFlow can provide.
- Your project requires computational flexibility.
- You value customization and precision control.
- You want to optimize deployment versatility.
- You are working in high-performance gaming or cryptocurrency mining.
Learn more about AI hardware and tools
AI and ML both use advanced hardware, and knowledge of their underlying technologies is important if you're pursuing a career in artificial intelligence. You can prepare for the future by adding an AI certificate to your educational profile or by pursuing an advanced degree in AI technology.
Business leaders may not need deep technical knowledge, but they can benefit from a high-level understanding of how AI and ML hardware are used at their organization. AI and ML executive education programs are relatively affordable, quick learning pathways designed for managers.