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TPU vs GPU vs CPU Performance and Differences discussed


As technology advances, the hardware used in a computer system is also upgraded in order to meet the demands of the public. Earlier, there was a CPU (Central Processing Unit) in the computer systems. Later on, the introduction of GPU (Graphics Processing Unit) has taken image rendering and image processing to the next level. Today in the age of Artificial Intelligence, we have TPU (Tensor Processing Unit). All these three are the processors that are developed to carry out specific tasks on a computer. In this article, we will talk about the difference between CPU, GPU, and TPU.


TPU vs GPU vs CPU Performance and Differences discussed

CPU or Central Processing Unit carries out all the arithmetic and logical operations. On the other hand, the work of a GPU is to render and process images or graphics. TPU is a special type of processor developed by Google. It is used to handle neural network processing using the TensorFlow. CPU can do multiple tasks, including image rendering. But the higher level of image rendering requires a dedicated processor, GPU. That’s why high-end games always require a dedicated graphics card.

What is a CPU?

CPU stands for Central Processing Unit. It is the brain of a computer because it handles all the tasks that a user performs on his/her computer. All the arithmetic and logical calculations required to complete a task are performed by the CPU. The aim of the CPU is to take input from the devices connected to a computer like a keyboard, mouse, etc., or from a programming software and display the required output.

Components of a CPU

A CPU consists of the following three components:

  1. CU (Control Unit)
  2. ALU (Arithmetic and Logical Unit)
  3. Registers

Components of a CPU

Control Unit in CPU

A Control Unit (CU) is one of the components of a CPU that fetches the instructions from the main memory and decodes them into commands. These commands are then sent to the ALU, whose work is to execute these instructions, and finally, the result is stored in the main memory.

ALU (Arithmetic and Logical Unit) in CPU

ALU, as the name implies, is that component of a CPU whose work is to carry out arithmetic and logical calculations or operations. Further, an ALU can be split into two parts namely, AU (Arithmetic Unit) and LU (Logical Unit). The work of these two units is to perform arithmetic and logical operations respectively.

All the calculations required by a CPU are carried out by the ALU. ALU receives commands from the Control Unit. After receiving these commands, it processes them by doing calculations and then it stores the final result in the main memory. The following three operations are carried out by ALU:

  1. Logical operations: These operations include AND, OR, NOT, NAND, NOR, etc.
  2. Bit-shifting operations: Bit-shifting operation is the displacement of the bits to the right or left by a certain number of places.
  3. Arithmetic operations: Addition, subtraction, multiplication, and division are the arithmetic operations.

Registers in CPU

A CPU consists of several registers. These registers include both general purpose and special purpose registers. The general-purpose register is used to store data temporarily. On the other hand, the special purpose registers are used to store the results of arithmetic and logical operations carried by the ALU.

What are CPU Cores?

CPU cores are pathways consisting of billions of microscopic transistors. A CPU uses cores to process data. In simple words, a CPU core is a basic computation unit of a CPU. The number of cores is directly proportional to the computational power of a CPU. The CPU cores define whether the CPU can handle multiple tasks or not. You might have heard the following two types of CPUs:

  • Single-core CPU
  • Multi-core CPU

A single-core CPU can handle only one task at a time, whereas a multi-core CPU can handle multiple tasks at a time. If you have a multi-core CPU installed on your system, you can do more than one task at a time, like you can browse the internet, create a document or spreadsheet in Microsoft Office programs, do image editing, etc., at the same time. How many CPU cores you need depends on the type of work you perform on your computer.

What is a GPU?

GPU stands for Graphics Processing Unit. A GPU is used in a variety of applications, including image and video rendering. In the field of gaming, graphics cards have a crucial role. A GPU is the main component of a graphics card. Graphics cards are of two types, namely, integrated graphics cards and dedicated graphics cards. The integrated graphics card is the one that is integrated into the computer’s motherboard. The integrated GPUs cannot handle high-level tasks, like high-end gaming. That’s why if you are a high-end gamer, you need to install a dedicated graphics card on your computer. Apart from that, the image and video editing tasks performed by heavy software also require a dedicated graphics card.

Read: What is GPU Computing used for?

What is the difference between a GPU and a Graphics Card?

Though the terms GPU and Graphics Card are used interchangeably, both of these terms are not the same. Let’s see what is the difference between both of these terms?

A GPU is a component of a graphics card, whereas a graphics card is a piece of hardware that is equipped with different components, including GPU, memory, heat sink, fan, etc. GPU is the heart of a graphics card because all the calculations required to process and render images are handled by GPU. Unlike a CPU, GPU has hundreds to thousands of cores. These small cores in a GPU are responsible for performing simple to complex calculations.

Read: Difference between DDR3 vs DDR4 vs DDR5 Graphics Cards.

What is a TPU?

TPU stands for Tensor Processing Unit. It is a processor developed by Google to handle neural network processing using the TensorFlow. TensorFlow is a free and open-source software library for artificial intelligence and machine learning.

The core of a TPU developed by Google is made of two units, namely, MXU (Matrix Multiply Unit) and VPU (Vector Processing Unit). The Matrix Multiply Unit performs matrix calculations and operates in a mixed 16 – 32 bit floating point format, whereas the Vector Processing Unit performs float32 and int32 computations.

Google has developed Cloud TPU to offer maximum flexibility and performance to researchers, developers, and businesses. The main aim to develop TPUs is to minimize the time required to train large and complex neural network models. The Cloud TPU accelerates the performance of linear algebra computation, which is used in machine learning applications. Due to this, TPUs are able to minimize the time to accuracy when it comes to training large and complex neural network models. If you train neural network models on hardware integrated with TPU, it will take hours, whereas, if the same task when done on the other hardware can take weeks.

Read: Do more CPU cores mean better performance?

TPU vs GPU vs CPU: Comparison based on different factors

Let’s compare these three processors on different factors.


  • CPU: The number of cores in a CPU includes one (single-core processor), 4 (quad-core processor), 8 (octa-core processor), etc. The CPU cores are directly proportional to its performance and also make it multitasking.
  • GPU: Unlike a CPU, a GPU has several hundred to several thousand cores. The calculations in a GPU are carried out in these cores. Hence, the GPU performance also depends on the number of cores it has.
  • TPU: According to Google, a single Cloud TPU chip has 2 cores. Each of these cores uses MXUs to accelerate the programs by dense matrix calculations.


  • CPU: A CPU has three main parts, namely, CU, ALU, and Registers. Talking about the registers, there are 5 different types of registers in a CPU. These registers are:
    • Accumulator
    • Instruction Register
    • Memory Address Register
    • Memory Data Register
    • Program Counter
  • GPU: As explained above, there are several hundred to several thousand cores in a GPU. All the calculations required to perform image processing and image rendering are done in these cores. Architecturally, the internal memory of a GPU has a wide interface with a point-to-point connection.
  • TPU: TPUs are the Machine Learning accelerators designed by Google. Machine Learning accelerators have the potential to boost Machine Learning tasks. The cores of TPU comprise of MXU and VPU that are capable of carrying out the matrix and floating-point calculations respectively.


  • CPU: The power consumed by a CPU depends on the number of cores it has. An octa-core processor consumes power approximately from 95 to 140 watts, whereas a 16-core processor consumes approximately 165 watts of power.
  • GPU: A GPU can consume up to 350 watts of power.
  • TPU: In a TPU, the process of reading and writing is performed on buffer and memory due to which power optimization can be achieved.

Read: What is System on a Chip (SoC)?

Is TPU or GPU better?

Both TPU and GPU are the processing units. The former one is the Tensor Processing unit and the latter one is the Graphics Processing Unit. The work of both of these processors is different. Being a part of a graphics processor, the work of the GPU is to do calculations required to render images. TPU is designed to handle neural network processing using the TensorFlow.

Which one of these two is better depends on the type of applications you are using them for. Cloud TPUs are optimized for specific workloads. In some situations, the use of GPU or CPU is better to run machine learning workloads. Let’s see when you can use a TPU and a GPU.

The use of GPU is better than TPU for medium to large models with larger effective batch sizes, the models with TensorFlow are not available on Cloud TPU, etc.

The use of TPU is better than GPU for the models that require matrix calculations, models that take from weeks to months to get trained, the models with larger effective batch sizes, etc.

Is TPU faster than CPU?

TPU is the Tensor Processing Unit. Google developed it to handle neural network processing using TensorFlow. The aim of designing TPU is to minimize the time required to train neural network models. According to Google, the training of neural network models on a TPU integrated hardware takes hours, whereas the same can take from weeks to months when done on other hardware. Hence, TPU is faster than CPU.


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