What is GPU Used for?8 Application Scenarios of Graphics Processing Units

What is GPU Used for?8 Application Scenarios of Graphics Processing Units

In the era of artificial intelligence, with the increasing requirements for large computing power and low power consumption of chips, the market penetration rate of GPUs is increasing. According to statistics, the GPU market size in 2020 is 25.41 billion US dollars, and it is expected to reach 185.31 billion US dollars by 2027, with a compound annual growth rate of 32.82% from 2021 to 2027.
the market penetration rate of GPUs is increasing

GPU global market size forecast (unit: USD 100 million)

At present, smartphones occupy a dominant position in the global GPU market share, but with the continuous growth of GPU demand in areas such as autonomous driving, medical equipment, gaming equipment, and cryptocurrency, the future GPU application market will be divided into multiple fields, and who will grow What about the fastest app market?

Let’s take a look at the eight areas where GPU is used, let’s get started!

GPU Game Server

If you are a game enthusiast, you will probably know that when buying a gaming device, you will pay more attention to its GPU performance. The so-called “CPU determines the lower limit, GPU determines the upper limit”. The GPU generally determines the game resolution and how high the special effects can be opened, which plays a key role in the user’s game experience.

Game graphics have always been a traditional application area of GPUs, which can provide game developers with real-time rendering of movie-quality images to enhance the game user experience. How do achieve this? One word: fast!

The basic structure of GPU parallel computing can perform massive data calculations; GPU access speed is fast; GPU has higher floating-point computing capability, and the processing speed of graphics and media is fast.

RTX's light chasing brings realistic light and shadow effects to water mirrors.

RTX’s light chasing brings realistic light and shadow effects to water mirrors.

It is also with the advantage of real-time rendering of game image quality that GPUs occupy a large share of the gaming PC and display market. With the strong growth of global game PC and monitor shipments, GPU demand for future game rendering scenarios will be strong.

According to IDC data, shipments of gaming PCs and monitors in 2020 increased by 26.8% year-on-year to 55 million units. Similarly, JPR data shows that in 2020, global GPU shipments will be 394 million, a year-on-year increase of 17.9%.

The annual report of global GPU giant Nvidia also shows that gaming is its largest market, accounting for 46.5%.

The annual report of global GPU giant Nvidia also shows that gaming is its largest market, accounting for 46.5%.

Nvidia’s revenue share in each market in 2020, data source: Nvidia annual report

Consumer Electronics

At present, the smartphone market occupies a dominant position in the global GPU market share. In addition, mobile consumer electronics such as smart speakers, smart bracelets/watches, and VR/AR glasses are all potential markets for GPUs.

GPU chips used in embedded and mobile terminals such as mobile phones are generally small in size, low in power consumption, and do not need to be particularly powerful in performance, but also support many functions, not limited to image creation, image processing, computational photography, gesture recognition, etc. Bring a brand new mobile device visual experience to consumers.

Mobile GPUs mainly use integrated GPUs, which often share a Die with the CPU and share system memory. As smartphone applications become more abundant, the advantages of GPU are more obvious. For example, taking pictures, compositing navigation maps, UI icons, image frames, and post-processing of photos all require GPUs.

However, the penetration rate of GPU in mobile phones and PCs has peaked. According to the data of the Chinese Academy of Social Sciences, the penetration rate of PCs per 100 people in major countries in the world from 2011 to 2018 showed a downward trend. Smartphones have a certain substitution for PCs. The rise of cloud computing, intelligent driving, and AI has created new demands for high computing power, which will bring about the rapid growth of the high-performance GPU market.

Main application end Main functions performance requirements Major provider
Personal terminal Independent graphics card Video editing, Gaming High Nvidia Geforce series, AMD Radeon series
Integrated graphics card Light office Low Intel HD series, AMD APU series, Imagination PowerVR series, Qualcomm Snapdragon Adreno series, Apple
Server Deep learning, AI reasoning, scientific computing, image processing, video coding and decoding High Nvidia Tesla series, AMD Instinct
Intelligent driving AI reasoning High Nvidia Orin series

GPU classification and major manufacturers

AI servers are usually equipped with GPU, FPGA, ASIC, and other acceleration chips. The combination of CPU and acceleration chips can meet the needs of high-throughput interconnection and provide powerful computing power for artificial intelligence application scenarios such as natural language processing, computer vision, and voice interaction. , which supports the AI algorithm training and reasoning process.

Currently, the most widely used AI chip in cloud scenarios is NVIDIA’s GPU. The main reasons are powerful parallel computing capability (compared to CPU), versatility, and a mature development environment.

In 2020, the global AI server market size is 12.2 billion US dollars. It is expected that the global AI intelligent server market will reach 28.8 billion US dollars by 2025, and the 5-year CAGR will reach 18.8%.

Market size and growth rate of the global AI server industry

Market size and growth rate of the global AI server industry in 2020-2025 (unit: US$100 million)


GPU has both technical cost advantages and has become the mainstream in the field of autonomous driving.

On the one hand, after relying on sensors such as radar to collect rational information during driving, the processor needs to analyze several gigabytes of data in real-time per second and can generate more than 1G of data per second. Therefore, autonomous driving requires a high amount of calculation on the processor.

On the other hand, after processing and analyzing real-time data, it is necessary to plan the driving path and vehicle speed with the time precision of milliseconds to ensure the safety of the driving process, and the computing speed of the processor is also high.

The GPU adopts the streaming parallel computing mode, which can independently perform parallel computing for each data row and is good at large-scale concurrent computing, which is exactly what is needed for autonomous driving.

At present, the automotive electronic control system is a distributed ECU architecture. Different infotainment, body, vehicle motion, and powertrain systems, and their subdivided functions are independently controlled by different independent ECU units. The number of ECUs on some high-end models exceeds 100. individual. In the future, the automotive electronic control system will further develop in the direction of centralization, decoupling of software and hardware, and platformization. The car will use a unified supercomputing platform to process, fuse, and make decisions on sensor data to achieve high-level autonomous driving functions.

Lv1-2 Lv3 Lv4-5
Sensors Millimeter wave 1-3 4-6 6-10
camera 1 2-4 6-8
Lidar N/A 0-1 1-3
Intelligent driving <1 TOPs 10<50 TOPs >50 TOPs

Source: Horizon’s official website

On April 13, 2021, Nvidia released the latest generation of super-computing chip Atlan, with a single-chip computing power of 1,000TOPS, which can meet L5 needs, and is expected to provide samples in 2023.

In addition to autonomous driving, GPUs are widely used in automotive design and engineering applications. Automotive design departments are under increasing pressure to rapidly implement automotive innovations and respond to the changing needs of the marketplace. Remote workers, external suppliers, and partners need faster and better access to data. GPUs make it easier for automakers to build and collaborate with global teams and scale their computing resources as needed.

Edge Computing

In edge computing scenarios, AI chips are mainly responsible for inference tasks, and the inference results are obtained by substituting data collected by sensors (microphone arrays, cameras, etc.) on the terminal device into the trained model for inference.

As the most mature general-purpose AI chip, GPU will benefit from a wide range of edge computing scenarios. Including the Internet of Things, autonomous driving, and other application scenarios.

Because edge-side scenarios are diverse and different, the considerations for computing hardware are also different, and performance requirements such as computing power and energy consumption are also large and small. Therefore, computing chips applied to the edge side need to be specifically designed for special scenarios to achieve optimal solutions.

Smart Security

The development of security cameras has experienced the development from analog to digital, digital high-definition to digital intelligence. The latest smart cameras can realize structured image data analysis in addition to simple recording and storage functions.

Security cameras can generate 20GB of data a day. If all data is sent back to the cloud data center, it will take up a lot of network bandwidth and data center resources. By adding AI chips to the camera terminal and network edge side, localized real-time processing of camera data is realized. After structured processing and key information extraction, only data with key information is sent back to the rear, which will greatly reduce network transmission. Bandwidth pressure.

AI chips in front-end camera equipment

The current mainstream solutions are divided into integrating AI chips in front-end camera equipment and adopting intelligent server-level products on the edge side. Front-end chips need to balance area, power consumption, cost, reliability, and other issues in design, and it is best to adopt low-power, low-cost solutions (such as DSP, ASIC); there are fewer edge-side restrictions, and more efficient solutions can be adopted. Server-level products (eg GPU, ASIC) for large-scale data processing tasks.

GPU can accelerate the processing of rapidly expanding data and video data, and has a good application prospect in smart security video processing.


The popularity of Bitcoin and other cryptocurrencies has driven the demand for mining card GPUs. The global PC GPU shipments exceeded 100 million units for three consecutive quarters in 2020Q4-2021Q2, and reached 123 million units in 2021Q2, a year-on-year increase of 42%.

The size of the mining machine’s computing power determines the speed of mining. The greater the computing power, the faster the mining. In addition to the mainstream ASIC miners, the most used for cryptocurrency mining is probably GPU miners.

The hardware characteristics of GPU are: the number of cores is very large, the structure of a single core is relatively simple, and it is suitable for a large number of repetitive general operations. For example, when we play games and 3D design, it is a large number of repetitive general operations. The number of cores (called stream processors) is very large, usually in the thousands. For example, the RX570 of the A card has as many as 2048 stream processors.

Mining happens to be a large number of repetitive general operations, which is in line with the performance characteristics of GPUs. GPUs are very suitable for this kind of brainless algorithm. The more stream processors there are, the more dominant they are.

Although many countries have tightened regulations on cryptocurrencies and there are financial risks, it does not hinder the vigorous development of the cryptocurrency industry. As of September 2021, the combined market capitalization of all crypto assets has exceeded $2 trillion, a ninefold increase since the beginning of 2020. In the future, as encrypted assets become more and more mainstream, the demand for GPU mining machines is bound to increase.

Medical Imaging Equipment

In recent years, with the rapid development of deep learning and GPU-accelerated computing, artificial intelligence has become a driving force to meet the growing demand for medical imaging. Several medical market research reports have pointed out that the market size of artificial intelligence in the medical imaging field is expected to grow rapidly at a compound annual growth rate of 30% from 2021 to 2026.

Medical Imaging Equipment

Medical imaging involves a complex series of signal and image reconstruction processes that convert raw data detected by X-ray or ultrasound sensors into 2D cross-sectional or 3D stereoscopic images. This image processing is time-consuming, requires a large amount of data, and requires accurate and stable image rendering quality.

With powerful parallel computing capabilities, GPU can complete image rendering in real-time, and combined with the training and inference of deep learning neural network to provide matrix operation acceleration, which can help to remove artifacts, adjust contrast, enhance sharpness, and obtain clearer images. medical image.

Conclusion for GPU Usages

The above are the mainstream application scenarios of GPU, and there are some special application areas of GPU. Including military, aviation, Xinchuang, etc., but the market demand is small. For example, according to statistics, the size of my country’s military GPU market in 2018 was only about 11.76 million yuan. Taken together, the main driver of future GPU growth is the increase in GPU penetration in servers and autonomous driving.

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