What kind of tasks can be performed by a GPU server?

What is a server with a graphics card

We’re lucky living in the digitalized age. Each of the seconds, the total amount of information in the world increases beyond belief. Informational systems, personal computers, and gadgets kind of smartphones and tablets process so massive data flow that even a high-end CPU can hardly deal.

High-performance computing provided by graphic adapters gave a new direction in IT.  Video cards have become a tool for mining cryptocurrencies, video/audio rendering, streaming, processing large amounts of information, — so graphics processors are much more efficient in cases where the CPU power is insufficient. Now, many infrastructure providers offer customers to rent servers with a video card. In this article, we will explain what their advantages are. 

What is a graphic adapter and how it applies to compute?

Earlier, when the computer world was young, all compute operations were performed by the CPU, a Central Processor Unit. It was responsible for computing, and for playing sound, and for processing requests from a video card and displaying graphics. People who played the old computer games thirty years ago probably remember that the video picture was far from good quality and the performance of a task simultaneously with the running game session slowed down the game up to its total freezing. We could mute the sounds in the game, that allowed unloading the processor a little bit, so the game stopped hanging. But in general, the problem was not solved.

With the computer equipment evolved, the graphics cards hardware had divided into integrated and discrete ones. Graphic tasks became more complicated; games and applications required more power for video cards. So video adapters got the processor — GPU, Graphic Processor Unit, which is responsible for the several similar computing that is processed in several simultaneous streams. Other names of GPU are video processor and graphics accelerator, which actually describe the functionality of the GPU.

At the application layer, we get complex graphic objects — both static (photos, drawings, diagrams), and dynamic (games, including 3D, animation, video) — that are displayed on the screen in high resolution.

CPU Vs. GPU: The comparison

The main difference between the calculations performed by the CPU and the calculations on the GPU is the principle of stream processing operations, which is directly related to the functional features of the CPU and GPU. Let's firstly talk about processor cores, or ALU, arithmetic logic units.

CPU working principleThe core of even the most powerful CPU performs operations step by step, in strict sequence — one after another. On the left-side scheme, this sequence is shown by green arrows. The embedding of urgent tasks with a high priority into the processing stream (also, interruptions — shown by orange arrows on the scheme) is possible. Still, their execution also is provided in the sequential order. The implementation of each subsequent step begins after the completion of the previous one and is based on the results obtained previously. Thus, an error made at one of the steps interrupts the operation of the entire program, and the process is crashed.

Now, up-to-the-date multi-core processor boards host several cores, and each of them processes instructions sequentially within a single thread. Thus, multitasking is implemented in the chip — various tasks are performed simultaneously in different threads. But each task in the thread is still processed sequentially.

GPU working principleThe architecture of the GPU is another. A graphic processor contains many cores combined in blocks. The GPU cores' modus operandi is fundamentally different from the CPU, due to being based on the parallelism of operations. In other words, the graphics processor performs many tasks simultaneously in several parallel threads. On the left-side scheme, it's indicated by green arrows. In this case, a random error in one of the calculation flows does not lead to a critical failure in the program, since it affects only one of the vast number of threads. Thus, high-performance computing on the GPU is achieved, up to eight times higher compared to the CPU. Due to it, GPUs are also called graphics accelerators.

Another essential difference between CPUs and GPUs is memory, concerning, memory access and interaction with it. The GPU does not need large RAM, and the operations of writing data to the video card and reading the result are different operations that consume time and resources. However, in recent years, developments in this area have been actively conducted, allowing to accelerate the interaction of the graphics processor with video RAM.

Differences in CPU and GPU architecture.
From https://habr.com/

GPU computing: The implementation areas

Initially, GPUs were created to process sophisticated graphics. However, the high performance of multi-threaded computing of relatively simple operations, achieved as a result of the GPU pipeline architecture, made it possible to use video cards for general computing as well. The GPGPU (General-purpose computing on graphics processing units) technology uses many parallel calculations of the same type of operations with non-graphic data.

So, the scope of the GPU is extensive, including:

  • Graphic rendering in various applications of video content processing;
  • Computer games and simulators;
  • GPU computing is widespread in science research — e.g., in molecular chemistry, biochemistry, flow fluid dynamics, mathematics, probabilistic calculus, etc.;
  • Statistics and predictive models;
  • Artificial Intelligence, Machine Learning, Deep Learning;
  • Cryptography and cryptanalysis;
  • Design and 3D-modeling (visualization);
  • Big Data analysis and processing and many other areas.

Definitely, the blockchain technology and cryptomining often use GPU computing.

How the cryptocurrency mining uses video cards and what the mining farm looks like, you can learn from our article Pirates of the Internet Age: Part I. What is cryptomining?

So, in the case of solving resource-intensive tasks, the power of a video card is not used for processing and displaying sophisticated graphics on the screen, but to obtain a result using mathematical algorithms similar to rendering.


A dedicated server with a GPU is an excellent solution for projects requiring high-performance computing resources.

Everyone knows how a dedicated server differs from a personal computer. In simple words, a personal computer is a software and hardware individual computing device. In its turn, a server much more complicated equipment that serves a network of several personal and mobile devices. The main task of the server is the distribution of computing resources among all devices within the network. In other words, "computer" is relating to "server" as "apple" to "apple tree" or "planet" to "star, around which the planet revolves."

Well, it is clear how the graphic adapter works on the computer — it’s used for playing games, watching a movie, streaming a video, processing a 3D image, and so on. But what does a video card in the server? After all, the server does not have a monitor, and you cannot play and watch videos on the server.

However, the answer is simple — the video card in the server is used for calculations. That's why we recommend using a dedicated GPU server if your projects need in resource-intensive computing, e.g., Big Data and Artificial Intelligence projects, 3D-modeling, cryptanalysis, and cryptography, including blockchain, so on.

Therefore, often a GPU dedicated servers are rented by developers of video games and resource-intensive applications, research projects with large-scale computing, e-media with video streaming, and so on.


A virtual dedicated server equipped by a graphic card is a promising solution in cases where high-performance computing combined with low cost is essential.

A virtual server (virtual private server, VPS, or virtual dedicated server, VDS) is the result of the virtualization. It's created by a hypervisor running on a dedicated physical server. Several virtual servers that are based on hardware, are isolated from each other; however, they all use the power resources of the physical server. Thus, the RAM and the CPU capacities of the hardware equipment are distributed among its virtual "clones."

The evolution of virtualization technology now allows using the hypervisor to create a virtual graphics adapter, vGPU. NVidia develops the most famous vGPUs.

How is vGPU created? Similar to how VPS is deployed on the server — via virtualization and specialized software. Based on the physical GPU installed on the physical server, this software deploys several virtual GPUs that can be shared among several virtual machines (VDS).


A cloud server with GPU is a virtualization-based cloud service equipped by a graphics accelerator.

Somebody tells that cloud is the same as a virtual private server. Is it true? To learn the truth, we recommend you read our article Is VPS the same as a cloud or not? (spoiler: actually, not!)

Cloud technology can’t miss the opportunity provided by graphics adapters. And the use of virtualization GPUs installed on powerful, failover cloud clusters allows you to utilize their advantages fully.  

So, cloud solutions using vGPU are often used to create a corporate virtual work environment using VDI (Virtual Desktop Infrastructure) technology. These solutions are very popular among companies for which business continuity and high-level information security are crucial.

Large game-dev companies can afford to use Cloud GPU platforms for their cloud gaming projects. Games deployed on such equipment are a real work of art, and it is not surprising that they have so many fans. Cloud video streaming services have also great popularity. Cloud instances equipped with GPUs are very demanded among developers of web applications based on artificial intelligence, as well as in studies of various scientific fields.

Sure, prices for such projects are far from low-cost. But it does worth it.

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Using graphics cards for high-performance computing is a new era in the history of IT. The benefits that various areas of business and science can and do receive from this technology are undeniable. The current market of infrastructure solutions with video cards is rich in offers with a variety of configurations.

If you want to raise your project to a fundamentally different level of productivity and security, we recommend you rent a GPU dedicated server located in a datacenter in Germany.

Contact our experts — they will help you to choose the best infrastructure solution with a configuration that meets all your requirements.

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