Difference between CPU and GPU

CPU and GPU both are important components of computing systems but they are assigned different tasks. CPU has a few powerful cores that are designed for general-purpose, Sequential Processing. GPU has many small cores that are designed especially for tasks like graphics rendering and parallel computation.

What is a CPU?

A Central processing unit (CPU) is commonly known as the brain of the computer. It is a conventional or general processor used for a wide range of operations encompassing the system instructions to the programs. CPUs are designed for high-performance serial processing which implies they are well-suited for performing large amounts of sequential tasks. They also excel at multi-threading and parallel processing for many types of workloads, depending on the number of cores and threads

Advantages of a CPU

• Versatility: CPUs are capable of performing various tasks, they may include operating systems and application software. 
• Single-Thread Performance: Intel CPUs are most efficient when it comes to single-threaded operations, which include typing or web surfing. 
• Multi-Tasking: CPUs can manage to run several programs at once because of the thread allocation to several tasks.

Disadvantages of a CPU

• Limited Parallelism: Although current and future CPUs disclose multiple cores (up to 64 or more), their structure cannot efficiently utilize largest-scale parallelism, not to mention the fact that they are designed for computational parallelism such as 3D graphic rendering or deep neural networks.
• Energy Consumption: While high-performance CPUs consume significant power, many are designed with power-efficient modes and technologies (e.g., Intel's SpeedStep and AMD's Cool'n'Quiet).

What is a GPU? 

The Graphics Processing Unit (GPU) is designed for parallel processing and it uses dedicated memory known as VRAM (Video RAM). They are designed to tackle thousands of operations at once for tasks like rendering images, 3D rendering, processing video, and running machine learning models. It has it's own memory separate from the system’s RAM which allows them to handle complex, high-throughput tasks like rendering and AI processing efficiently.

Advantages of a GPU

• Parallel Processing: One main distinguishing feature of GPUs is versatility in multitasking. As such its strategies are best used in large ‘data crunching’ applications that involve repetitive computations and others such as deep learning, scientific simulations, and video rendering.
• High Throughput: These devices have so many cores, GPUs offer excellent compute density for applications such as matrix arithmetic and picture interpretation. 
• Graphics Rendering: GPUs are very unique for their purpose which is rendering quality images and graphics, a profitable factor in gaming, video editing, and other graphic simulations.

Disadvantages of a GPU

• Not Versatile: Contrary to CPUs, GPUs are not versatile and cannot be used for tasks which require processing, such as operating systems or everyday applications. 
• Power Consumption: GPUs are power-intensive, particularly high-performance models, they use power to process data and generate heat hence they need to cool down immediately.
• Cost: high-end GPUs for professional tasks (e.g., NVIDIA A100, RTX 4090) are expensive, many mid-range GPUs are affordable and effective for light computational and gaming tasks.

Difference Between CPU and GPU

Conclusion

CPUs and GPUs are closely related products for modern computing complexes which can successfully work together. CPUs are general-purpose and can perform most of the computations, but they are not as efficient in parallel processing as GPUs which are used in gaming, rendering, artificial intelligence, etc. What distinguishes the choice between a CPU and a GPU is the type of work that is to be performed and the amount of performance expected.