CPU vs GPU vs TPU: Pros, Cons, Difference

CPU vs GPU vs TPU, which is better for you?

Artificial intelligence and machine learning technologies have enhanced the advancement of intelligent applications. Processors and accelerators, including CPUs, GPUs, and TPUs, are constantly being developed by semiconductor companies to support increasingly complex applications. The performance of CPUs alone won’t be sufficient to handle demanding workloads efficiently as Moore’s law slows down.

As AI applications become more sophisticated, how can companies increase the entire system’s performance to support their excessive demands? Deep learning models may be run using GPUs and TPUs to supplement CPUs. Thus, it is crucial to understand the technology behind CPUs, GPUs, and TPUs to keep up with constantly evolving technology.

How do CPUs, GPUs, and TPUs differ if you want information on these topics? Does computer architecture depend on them? This blog’s report may help you better understand CPUs, GPUs, and TPUs.


CPUs, GPUs, and TPUs are all general-purpose processors that handle logic, calculations, and input/output of the computer. In contrast, a GPU performs high-end tasks and enhances the graphical interface. TPUs (Tensor Processing Units) are powerful custom-built processors designed to run projects based on the TensorFlow framework and TensorFlow software.

Several coresThousands of CoresMatrix-based workload
Low latencyHigh data throughputHigh latency
Serial processingMassive parallel computingHigh data throughput
Limited simultaneous operationsLimited multitaskingSuited for large batch sizes
Large memory capacityLow memoryComplex neural network models
  • CPU: Central Processing Unit. Control every aspect of a computer.
  • GPU: Graphical Processing Unit. Make the computer more graphical-friendly.
  • TPU: Tensor Processing Unit. TensorFlow projects can be accelerated with custom-built ASICs.

What is a CPU?

A CPU is an acronym for Central Processing Unit. In addition to being known as a processor, it is also known as a microprocessor.

Digital computing systems are incomplete without this critical piece of hardware.

The flow of electricity through integrated circuits in a CPU is controlled by millions of micron-sized transistors and tiny switches.

Computer motherboards contain the CPU.

It is the main circuit board of a computer that consists of all the electronic components. All hardware components are connected through it.

CPUs control all digital systems, often called their brain and heart. A computer executes programs and performs every action it can.

What does a CPU do?

CPUs process logical and mathematical instructions and execute instructions given to them.

One instruction can be carried out simultaneously, but millions of instructions can be executed per second.

An input device (such as your monitor display screen, keyboard, mouse, or microphone) or a software program (such as an operating system or web browser) first receives input.

There are four tasks that the CPU is responsible for:

  1. For each input data obtained, the processor fetches instructions from memory to know how to handle it and what to do with it. It forwards the request to the RAM after finding the address of the corresponding instruction. A constant exchange of information takes place between the CPU and RAM. Reading from memory is also referred to as reading.
  2. Input instructions are encoded or translated into machine language (binary), which the CPU can understand.
  3. The process of executing and implementing instructions.
  4. For future retrieval, the execution result is stored back in memory. As well as writing to memory, this is called reading from memory.

The final step is to generate some output, such as printing something.

Fetch-execute cycles occur millions of times per second and are called fetch-execute cycles.

What is a GPU?

Computer graphics processors render images on a computer’s screen utilizing a programmable processor called a GPU (Graphics Processing Unit). In gaming, the GPU is a stand-alone card plugged into the PCI Express (PCIe) bus that provides the fastest graphics processing. It is also possible to find GPU circuitry on motherboard chipsets or CPU chips themselves (see diagram below).

Parallel operations are performed by a GPU. A GPU is essential for smooth video and animation decoding and rendering, even though it is used for 2D data as well as zooming and panning. Higher resolution and smoother motion are possible with a more sophisticated GPU. When GPUs are integrated into CPU chipsets or chipsets, the main memory is shared with the CPU.

Ray Tracing Engine

The GPU may also include hardware designed to accelerate ray tracing, which produces bright and shadowed areas by simulating a light source falling on an object. Ray tracing is one of the most significant factors in determining the realism of video games and has become mandatory for serious gamers.

Not Just Graphics Processing

In scientific and AI applications that require repetitive computations, GPUs are widely used because they perform parallel operations on multiple sets of data. In addition to CPUs, supercomputers can have thousands of GPUs. Bitcoin and other digital currencies can also be mined with GPUs (see crypto mining).

What Does a GPU Do?

In both personal and business computing, graphics processing units have become one of the most important technologies. Graphics and video rendering are among the many applications for GPUs, which are designed for parallel processing. While GPUs are most often associated with gaming, they are also becoming increasingly popular for creative production and artificial intelligence (AI).

3D graphics were originally rendered with GPUs. Eventually, they gained more flexibility and programmability, which improved their capabilities. Using advanced lighting and shadowing techniques, graphics programmers were able to create more realistic scenes and more interesting visual effects. GPUs are also being used to speed up high-performance computing (HPC), deep learning, and many other workloads.

What is a TPU?

Tensor Processing Units (TPUs) are Google’s custom-developed application-specific integrated circuits (ASICs) to accelerate machine learning workloads. TPUs are designed from the ground up with the benefit of Google’s deep experience and leadership in machine learning.

Using TensorFlow, Cloud TPU enables you to run your machine learning workloads on Google’s TPU accelerator hardware. Cloud TPU is designed for maximum performance and flexibility to help researchers, developers, and businesses to build TensorFlow compute clusters that can leverage CPUs, GPUs, and TPUs. High-level TensorFlow APIs help you to get models running on the Cloud TPU hardware.

Cloud TPU programming model

TPUs in the cloud are very fast when computing dense vectors and matrices. A Cloud TPU interconnect, and an on-chip high bandwidth memory (HBM) is much slower than the PCIe bus, which transfers data between Cloud TPU and host memory at a slower rate than the computation speed. TPUs are idle most of the time during partial compilations, as data is waiting to arrive over the PCIe bus as execution passes from host to device. To alleviate this problem, all training should be performed on the TPU of Cloud TPU.

Following are some salient features of the TPU programming model:

  • Model parameters are stored in high-speed memory on the chip.
  • Several training steps are executed in a loop to amortize the cost of launching computations on Cloud TPU.
  • A Cloud TPU program retrieves batches of training data from an “infeed” queue during each training step by streaming the data to the Cloud TPU.
  • To feed the Cloud TPU hardware with data, the TensorFlow server (located on the host machine) preprocesses it.
  • Cloud TPU cores execute similar programs stored in their HBMs synchronously. Each neural network step ends with a reduction operation across all cores.

What Does A TPU Do?

In order to accelerate machine learning workloads, Google develops custom ASICs called Tensor Processing Units (TPUs). Based on Google’s deep machine learning expertise, TPUs are designed from the ground up.

People Also Ask (FAQs)

Is TPU Better than GPU?

When working on complex problems, GPUs can create thousands or millions of separate tasks, but TPUs were designed specifically for neural network loads, so they can run faster and require fewer resources than GPUs.

Is TPU the Same as CPU?

Deep learning and machine learning applications are powered by TPU, Google’s ASIC. For its TensorFlow software, Google developed matrix processor TPU instead of other general processors like CPU and GPU.

How Much TPU is Faster Than CPU?

Commercial AI applications that use neural network inference run 15 to 30 times faster on TPUs than on GPUs and CPUs.

Why Is TPU Expensive?

There is, however, an imbalance in the supply of AA due to both growing global nylon demand and rising living costs in countries such as China, causing the price of TPU material to rise.

How Do I Stop TPU Wrapping?

Use a heated build plate

Heated build plates are most effective in preventing warping. A glass transition temperature ensures that the material stays flat and connected to the build surface by keeping it just below the temperature at which it solidifies (the glass transition temperature).

Final Words

Finally, operations that used to take hours on CPUs, GPUs, and TPUs can now be done in a matter of minutes.

CPU vs GPU vs TPU: How do they differ? I hope you found this article useful in understanding the differences between CPU, GPU, and TPU.

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lisa shroff
About Lisa Shroff

I am a Tech Enthusiast who is Obsessed with Graphics Cards or GPU. I have been building Gaming PC since last 6 years and got super experience in this field.

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