First, there was a CPU, then GPU, and now TPU. As the tech industry is growing, and finding new ways to use computers, the need for faster hardware is increasing.
But what is the difference between CPU, GPU and TPU?
In this post, we will compare CPU vs GPU vs TPU briefly.
CPU vs GPU vs TPU
The difference between CPU, GPU and TPU is that the CPU handles all the logics, calculations, and input/output of the computer, it is a general-purpose processor. In comparison, GPU is an additional processor to enhance the graphical interface and run high-end tasks. TPUs are powerful custom-built processors to run the project made on a specific framework, i.e. TensorFlow.
- CPU: Central Processing Unit. Manage all the functions of a computer.
- GPU: Graphical Processing Unit. Enhance the graphical performance of the computer.
- TPU: Tensor Processing Unit. Custom build ASIC to accelerate TensorFlow projects.
What is the CPU?
CPU stands for Central Processing Unit and considered as the brain of the computer.
It is the primary hardware of the computer that executes the instruction for computer programs. All the basic arithmetic, logic, controlling, and the CPU handles input/output functions of the program.
CPU runs the operating system, continually receiving inputs and providing output to the users.
A CPU contains at least one processor. The processor is an actual chip inside the CPU to perform all the calculations. For a long time, CPUs had only one processor, but now dual-core CPUs (CPU with two processors) are common.
There are also four-processor CPUs that are quad-core CPUs. Some high-end companies also build CPUs with eight processors.
Popular Manufacturers: Intel, AMD, Qualcomm, NVIDIA, IBM, Samsung, Hewlett-Packard, VIA, etc
What is the GPU?
While CPU is known as the brain of the computer, and the logical thinking section of the computer, GPU helps in displaying what is going on in the brain by rendering the graphical user interface visually.
GPU stands for Graphical Processing Unit, and it is integrated into each CPU in some form. But some tasks and applications require extensive visualization that available inbuilt GPU can’t handle. Tasks such as computer-aided design, machine learning, video games, live streamings, video editing, and data scientist.
Simple tasks of rendering basic graphics can be done with the GPU built into the CPU. For other high-end jobs, GPU is made.
Moreover, if you want to do extensive graphical tasks, but do not want to invest in physical GPU, you can get GPU servers. GPU servers are servers with GPU that you can remotely use to harness the raw processing power to complex calculations.
Popular GPU Manufacturers: NVIDIA, AMD, Broadcom Limited,
GPU is typically expensive. Best option is to get GPU server on rent, and use the GPU power without buying the GPU server. Get your GPU server now.
What is TPU?
Tensor Processing Unit (TPU) is an application-specific integrated circuit, to accelerate the AI calculations and algorithm. Google develops it specifically for neural network machine learning for the TensorFlow software. Google owns TensorFlow software.
Google started using TPU in 2015; then, they made it public in 2018. You can have TPU as a cloud or smaller version of the chip.
TPUs are custom build processing units to work for a specific app framework. That is TensorFlow. An open-source machine learning platform, with state of the art tools, libraries, and community, so the user can quickly build and deploy ML apps.
Cloud TPU allows you to run your machine learning projects on TPU using TF. Designed for powerful performance, and flexibility, Google’s TPU helps researchers and developers to run models with high-level TensorFlow APIs.
The models who used to take weeks to train on GPU or any other hardware can put out in hours with TPU.
TPU is only used for TensorFlow projects by researchers and developers.
Manufacturer: Only Google makes them.
CPU vs GPU vs TPU
|Several core||Thousands of Cores||Matrix based workload|
|Low latency||High data throughput||High latency|
|Serial processing||Massive parallel computing||High data throughput|
|Limited simultaneous operations||Limited multitasking||Suited for large batch sizes|
|Large memory capacity||Low memory||Complex neural network models|
Which is better TPU or GPU?
GPUs and TPUs both have their own advantages and disadvantages. A single GPU can process thousands of tasks at once, but GPUs are typically less efficient in the way they work with neural networks than a TPU. TPUs are more specialized for machine learning calculations and require more traffic to learn at first, but after that, they are more impactful with less power consumption.
Is TPU faster than CPU?
TPUs are 3x faster than CPUs and 3x slower than GPUs for performing a small number of predictions.
How much faster is TPU vs GPU?
The TPU is 15 to 30 times faster than current GPUs.
Difference between CPU, GPU and TPU
I hope this article helped you to understand the difference between the CPU, GPU and TPU.
If you are looking for something faster to run your project, then GPU servers are the best option for you. You can scale up the power of the server quickly.
You can contact us for GPU servers.
7 thoughts on “CPU vs GPU vs TPU: Understanding the Difference Between Them”
“TPUs are 3x faster than CPUs and 3x slower than GPUs […] The TPU is 15 to 30 times faster than current GPUs.”
This doesn’t make sense….
For small calculations, TPU is slow — because it takes times to learn. For large calculations, TPU takes less time, as it is efficient in long run.
Though I know the basic difference between CPU and GPU, But I didn’t know how to differentiate TUP now it’s all clear to me, Thank you so much.
Everyone show use your basic explanatory way of teaching so as to get full understanding of whatever it is we are searching answers to..
Thank YOU !!!
well done !!!!!
Thank you so much.
Everything was explained very efficiently manner. Basic key points cleared 100 %.
Exactly what I wanted to know! Many thanks.
I never understood the clear cut difference between the two untill I saw this article. It was very basic and self explanatory. Thanks for writing such a detailed article.