Nvidea GPU Dedicated Server

Nvidea GPU Dedicated Server Plans

SSD GPU Server with Nvidea Card

  • Choose Your Requirement
  • Server Type
  • DataCenter
  • RAM
  • CPU Core
  • Storage (SSD/HDD)
  • Flag
    Datacenter - USA
  • VMLG101
  • Intel Xeon X3440
  • CPU Quad-Core
  • RAM 16 GB
  • Storage 1080 GB SSD
  • Nvidia GeForce GT 710 GPU
  • GPU RAM 1 GB
  • 120 Mbit/s port
  • 100Mbps Unmetered Bandwidth
  • Other Details  
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  •  Linux
    (USD 65/Month)
  • Flag
    Datacenter - USA
  • VMLG102
  • Intel Xeon E5-2643
  • CPU Quad-Core
  • RAM 32 GB
  • Storage 1080 GB SSD
  • GPU Nvidia Quadro P620
  • GPU RAM 2 GB
  • 120 Mbit/s port
  • 100Mbps Unmetered Bandwidth
  • Other Details  
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  •  Linux
    (USD 85/Month)
  • Flag
    Datacenter - USA
  • VMLG103
  • Intel Xeon E5-2670
  • CPU Eight-Core
  • RAM 64 GB
  • Storage 1080 GB SSD
  • Nvidia Tesla K40 GPU
  • GPU RAM 12 GB
  • 120 Mbit/s port
  • 100Mbps Unmetered Bandwidth
  • Other Details  
  •  Try Now
  •  Linux
    (USD 145/Month)
  • Flag
    Datacenter - USA
  • VMLG104
  • Intel Xeon E5- 2689
  • CPU Eight Core
  • RAM 128 GB
  • Storage 1080 GB SSD
  • Nvidia Tesla K80 GPU
  • GPU RAM 24 GB
  • 120 Mbit/s port
  • 100Mbps Unmetered Bandwidth
  • Other Details  
  •  Try Now
  •  Linux
    (USD 215/Month)

Why GPU Server?


Analysing GPUs


Some years ago, 3D design and gaming were the ruling functions of GPUs. Content creators use them for quality services. However, these days, GPUs are not limited to just content or gaming.


The programming on GPUs is according to your needs. They are used in many fields. As the name suggests, the graphics processing unit is an excellent tool when it comes to designing and gaming. It also supplies the highest level of services for users who are in the creative field, but now they have more applications in the fields of computing, deep learning, AI, etc. in terms of flexibility, user-friendly, and high functioning.


GPUs are better in many ways. The reason for which more users are attracted to GPUs these days is that they have a lot more potential. Developers realised the potential in GPU and utilised it. They are extremely popular now between developers and users. They also work in computing. Graphic designers and content creators still use it for its effects and because it uses advanced technology and efficiency.


Is it essential to have a GPU?


Most of the systems available on the market have CPUs in them. The question is, why would you need a GPU?


If you work in the content field then you need right tools. Good graphics softwareor work as a content creator need high-quality editing software. For this purpose, GPU is a must-have. GPU increases the graphic possibilities of your system.CPU has supplied all the high-performing services you need as well. Get a new system with So, whenever you are looking to get a new system should compare both the GPU and the CPU to make sure you are choosing what is best for you.


Graphic cards and how are they different from the GPU?

We can understand this by the example of the CPU. The CPU is just a part of your system on the motherboard. Similarly, a graphics card is like a motherboard, and the GPU is in it. The motherboard has many components, and the CPU is just a part of them. Graphics cards are related to motherboards too.


The GPU is just a part of the graphic card, which also holds many components. GPUs is divided into two categories: integrated GPUs and discrete GPUs. The integrated one is installed in the CPU instead of an individual card, while discrete GPUs come installed on an individual board of circuits. To further understand these two types of GPUs,let's analyse them.


Integrated GPU

Many systems on the market are integrated GPU. They are integrating system for several reasons. One of the reasons is cost. An integrated GPU is installed on the motherboard along with the CPU, which makes it cost-effective. since you do not have to pay for a separate board. Another reason for which it is more popular and preferred is that it makes the system consume less power and makes it less heavy. For users who are interested in graphic design and content creation, a GPU is necessary.


Discrete graphics processing unit

Systems can work well even with integrated GPUs, but some users want more functions and applications. For this purpose, you should opt for a discrete graphics processing unit. However, it would be more expensive. A separate board will also consume more energy but have high functionality.


What are the uses?

GPU dedicated hosting servers were mainly designed for creative purposes. People who want more options for superior quality graphics and to be able to edit their content in a more efficient way. However, now they are programmed to do more than just that. They are getting more used in computing and deep learning now. Developers recognised the potential and programmed it accordingly.


  • Gaming: In the past decade, the sales of systems that support high-quality video games have increased. The gaming industry is booming day by day now. interactive gaming, high-quality graphics, 3D, etc. Because the CPU has limitations, users enjoy GPUs for enjoying a fast, high-quality experience. The CPU may not support VR gaming, good resolution, and advanced technologies, but the demands on them are remarkably high. Companies are focusing increasingly on gaming systems and introducing more gaming platforms that support high-tech games.

  • Creative aspects: users who are inclined towards functions like editing or content and graphic design require special tools. Usually, systems are slow and cannot support high-quality content. GPU supplies all the services to professionals who need all these functions and tools on a day-to-day basis. GPU servers are secure, safe and powerful. You need a powerful resource that can handle your data efficiently.

  • AI: AI technology is at its peak right now. Almost all companies are working on it. The GPU has excellent computing benefits and can handle a lot of loads. AI needs a high-functioning system such as profile/face recognition and a lot more. So, for all the required advanced technologies, the GPU is recommended.


Why is the GPU used for deep learning?

GPUs are used for multiple and simultaneous computations with less resources. While using GPUs, you come across many varieties, even though NVIDIA is all over the market.


While we are designing a deep learning process, our decision should include GPUs and thus these factors:


  • It can give proper bandwidth to adjust large datasets.

  • It has more scaling range than CPUs, which provides datasets to run faster than ever.

  • Effectiveness in long-running singular tasks is better in GPUs than in CPUs, and it is yet another function of the GPU.


Why Us?

  • Our team has professionals who have training and experience in programming and who work on all the leading technologies.

  • They are all highly experienced professionals who have worked in this field for a long time.

  • We at VPSServer.Net make sure that you have the best service and information. Our professionals supply 24/7 customer service so you get all the information you need.

  • We provide customizable services and information about what is the best choice for you within your budget. A well-communicated service with good advice and correct knowledge can provide you with the best assistance. Even after you choose any service and face issues, our assistance will help you resolve all the errors you might face.