Save Big: Up To 10% Off On Multiple GPU Servers!

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Boost Your Computing with GPU Dedicated Servers

GPU-dedicated servers are typically added in various cloud services or data centres, and anyone can easily access them from any place.

  • Scientific Simulations
  • Gaming
  • Handle multiple calculations
  • Uses Minimum Power
  • 3-D Rendering
  • Video Editing
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Dedicated GPU Server Hosting for High-Performance

GPU4HOST’s dedicated GPU server hosting unites exceptional GPU power with powerful infrastructure, engineered for heavy workloads such as artificial intelligence, complex simulations, and 3D rendering. Get flawless scalability, quick processing, and the trust you want to extend the limits of your tasks.

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Customization

The GPU4HOST offers a complete sequence of hardware configurations, allowing you to choose the needed GPU.

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Scalability

GPU4HOST generally offers scalable choices, allowing you to simply delete or add assets as per your needs.

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Performance Enhancement

GPU servers let you take full benefit of the high-performance of all dedicated servers, thus decreasing the processing time.

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Cost-effective

GPU servers are very cost-effective, mainly if you only want to utilize computing assets for a very limited period.

Benefits of a GPU-Dedicated Server

While comparing the GPU server with the out-dated CPU server, dedicated GPU servers can easily process large amounts of data and all difficult tasks related to computing in a very short period of time as they have strong parallel computing abilities. So, GPU-dedicated servers are frequently utilised to train all models of deep learning, perform scientific computing, and quicken computing-concentrated applications. GPU servers can benefit individuals or organisations by saving their precious time as well as assets and concern of buying and upholding classy hardware equipment.

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GPU4HOST's Affordable Pricing Plans

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Advanced GPU - RTX 3060 Ti

$ 309.00/month

$50.99
  • Dual 12-Core E5-2697v2
  • 128GB RAM
  • 2TB SSD
  • 1Gbps Port Speed
  • GPU: GeForce RTX 3060 Ti
  • Microarchitecture: Ampere
  • Max GPUs: 2
  • CUDA Cores: 4864
  • Tensor Cores: 152
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 16.2 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Enterprise GPU - RTX A6000

$ 455.00/month

$50.99
  • Dual 18-Core E5-2697v4
  • 256GB RAM
  • 2TB SSD
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • GPU: Nvidia Quadro RTX A6000
  • Microarchitecture: Ampere
  • Max GPUs: 1
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Multi-GPU - 4xA100

$ 2,569.00/month

$50.99
  • Dual 22-Core E5-2699v4
  • 512 GB RAM
  • 4TB NVMe
  • 1Gbps Port Speed
  • GPU: 4 x Nvidia A100
  • Microarchitecture: Ampere
  • Max GPUs: 4
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2e
  • FP32 Performance: 19.5 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Quad-Core Xeon X3440

$ 120.00/month

$50.99
  • 16GB RAM
  • 960GB SSD
  • 1Gbps Port Speed
  • GPU: Nvidia GeForce GT710
  • Microarchitecture: Kepler
  • Max GPUs: 1
  • CUDA Cores: 192
  • GPU Memory: 1GB DDR3
  • FP32 Performance: 0.336 TFLOPS
  • OS: Linux/Windows
  • Fully Managed
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Quad-Core Xeon E3-1230

$ 144.00/month

$50.99
  • 16GB RAM
  • 960GB SSD
  • 1Gbps Port Speed
  • GPU: Nvidia GeForce GT730
  • Microarchitecture: Kepler
  • Max GPUs: 1
  • CUDA Cores: 384
  • GPU Memory: 2GB DDR3
  • FP32 Performance: 0.692 TFLOPS
  • OS: Windows / Linux
  • Fully Managed
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Quad-Core Xeon E3-1270v3

$ 125.00/month

$50.99
  • 16GB RAM
  • 960GB SSD
  • 1Gbps Port Speed
  • GPU: Nvidia Quadro K620
  • Microarchitecture: Maxwell
  • Max GPUs: 1
  • CUDA Cores: 384
  • GPU Memory: 2GB DDR3
  • FP32 Performance: 0.863 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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P600

$ 129.00/month

$50.99
  • Quad-Core Xeon E5-2643
  • 32GB RAM
  • 960GB SSD
  • 1Gbps Port Speed
  • GPU: Nvidia Quadro P600
  • Microarchitecture: Pascal
  • Max GPUs: 1
  • CUDA Cores: 384
  • GPU Memory: 2GB GDDR5
  • FP32 Performance: 1.2 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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P1000

$ 144.00/month

$50.99
  • Eight-Core Xeon E5-2690
  • 32GB RAM
  • 960GB SSD
  • 1Gbps Port Speed
  • GPU: Nvidia Quadro P1000
  • Microarchitecture: Pascal
  • Max GPUs: 1
  • CUDA Cores: 640
  • GPU Memory: 4GB GDDR5
  • FP32 Performance: 1.894 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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GT 1650

$ 189.00/month

$50.99
  • Eight-Core Xeon E5-2667v3
  • 64GB RAM
  • 960GB SSD
  • 1Gbps Port Speed
  • GPU: Nvidia GeForce GTX 1650
  • Microarchitecture: Turing
  • Max GPUs: 1
  • CUDA Cores: 896
  • GPU Memory: 4GB GDDR5
  • FP32 Performance: 3.0 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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RTX 2060

$ 269.00/month

$50.99
  • Dual 10-Core E5-2660v2
  • 128GB RAM
  • 960GB SSD
  • 1Gbps Port Speed
  • GPU: Nvidia GeForce RTX 2060
  • Microarchitecture: Ampere
  • Max GPUs: 2
  • CUDA Cores: 1920
  • Tensor Cores: 240
  • GPU Memory: 6GB GDDR6
  • FP32 Performance: 6.5 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Advanced GPU - RTX 3060 Ti

$ 309.00/month

$50.99
  • Dual 12-Core E5-2697v2
  • 128GB RAM
  • 2TB SSD
  • 1Gbps Port Speed
  • GPU: GeForce RTX 3060 Ti
  • Microarchitecture: Ampere
  • Max GPUs: 2
  • CUDA Cores: 4864
  • Tensor Cores: 152
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 16.2 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Multi-GPU - 3xRTX 3060 Ti

$ 569.00/month

$50.99
  • Dual 18-Core E5-2697v4
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • GPU: 3 x GeForce RTX 3060 Ti
  • Microarchitecture: Ampere
  • Max GPUs: 3
  • CUDA Cores: 4864
  • Tensor Cores: 152
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 16.2 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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A4000

$ 349.00/month

$50.99
  • Dual 12-Core E5-2697v2
  • 128GB RAM
  • 2TB SSD
  • 1Gbps Port Speed
  • GPU: Nvidia Quadro RTX A4000
  • Microarchitecture: Ampere
  • Max GPUs: 2
  • CUDA Cores: 6144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • FP32 Performance: 19.2 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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A5000

$ 419.00/month

$50.99
  • Advanced GPU - A5000
  • 128GB RAM
  • 2TB SSD
  • 1Gbps Port Speed
  • GPU: Nvidia Quadro RTX A5000
  • Microarchitecture: Ampere
  • Max GPUs: 2
  • CUDA Cores: 8192
  • Tensor Cores: 256
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Enterprise GPU - RTX A6000

$ 455.00/month

$50.99
  • Dual 18-Core E5-2697v4
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • GPU: Nvidia Quadro RTX A6000
  • Microarchitecture: Ampere
  • Max GPUs: 1
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Multi-GPU - 3xRTX A6000

$ 1,269.00/month

$50.99
  • Dual 18-Core E5-2697v4
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • GPU: 3 x Quadro RTX A6000
  • Microarchitecture: Ampere
  • Max GPUs: 3
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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RTX 4090

$ 455.00/month

$50.99
  • Enterprise GPU - RTX 4090
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • GPU: GeForce RTX 4090
  • Microarchitecture: Ada Lovelace
  • Max GPUs: 1
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24 GB GDDR6X
  • FP32 Performance: 82.6 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Multi-GPU - 2xRTX 4090

$ 969.00/month

$50.99
  • Dual 18-Core E5-2697v4
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • GPU: GeForce RTX 4090
  • Microarchitecture: Ada Lovelace
  • Max GPUs: 2
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24 GB GDDR6X
  • FP32 Performance: 82.6 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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K80

$ 199.00/month

$50.99
  • Eight-Core Xeon E5-2690
  • 64GB RAM
  • 960GB SSD
  • 1Gbps Port Speed
  • GPU: Nvidia Tesla K80
  • Microarchitecture: Turing
  • Max GPUs: 2
  • CUDA Cores: 4992
  • GPU Memory: 24GB GDDR5
  • FP32 Performance: 8.73 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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A40

$ 619.00/month

$50.99
  • Dual 18-Core E5-2697v4
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • GPU: Nvidia A40
  • Microarchitecture: Ampere
  • Max GPUs: 1
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 37.48 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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V100

$ 669.00/month

$50.99
  • Multi-GPU - 3xV100
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • GPU: 3 x Nvidia V100
  • Microarchitecture: Volta
  • Max GPUs: 3
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Enterprise GPU - A100

$ 869.00/month

$50.99
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • OS: Windows / Linux
  • GPU: Nvidia A100
  • Microarchitecture: Ampere
  • Max GPUs: 1
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2e
  • FP32 Performance: 19.5 TFLOPS
  • Fully managed
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Multi-GPU - 4xA100

$ 2,569.00/month

$50.99
  • Dual 22-Core E5-2699v4
  • 512 GB RAM
  • 4TB NVMe
  • 1Gbps Port Speed
  • GPU: 4 x Nvidia A100
  • Microarchitecture: Ampere
  • Max GPUs: 4
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2e
  • FP32 Performance: 19.5 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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  • Instant Support
  • Quick Deploy
  • Robust Security

GPU Dedicated Server for Deep Learning

In the case of deep learning, GPU-dedicated servers are the appropriate options to attain high performance. The algorithm of deep learning requires a high amount of computing power, particularly when training all neural networks on large data sets. Dedicated GPU servers can offer the crucial skills to train all neural networks rapidly and resourcefully.

At the time of choosing a dedicated GPU server, especially for deep learning, several components need to be kept in mind. The most necessary component is the GPU itself. The NVIDIA GPU is a very popular option in the case of deep learning. Tesla’s GPU is now one of the most robust GPUs. These GPUs offer good internal storage bandwidth, less latency, and benefit from innovative operations like tensor cores.

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Dedicated Server with GPU for Scientific Computing

Using a dedicated GPU server is the ideal option for all types of scientific computing. Many applications related to scientific computing need a variety of computing assets, and GPU servers can offer the important proficiencies to perform all these complex calculations in less time.

When choosing GPU servers for complex scientific computing, the type of GPU is very crucial. The NVIDIA GPU is frequently utilized in all types of scientific computing as it provides the best performance and helps with progressive functions like tensor kernel and CUDA. NVIDIA Tesla is also a very good option for complex scientific computing, as it offers high internal storage bandwidth and a lot more functions.

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Dedicated GPU Server for Virtualization

The dedicated GPU server can be utilized for the process of virtualization, allowing numerous virtual machines to share a single physical GPU device to attain more proficient usage of GPU computing resources. Moreover, the performance and model of the GPU also required to be chosen as per the requirements of virtualization applications. Furthermore, server configuration virtualization applications need a huge amount of storage capacity and memory to support the requirements of all virtualization applications. The network is another important component. Consequently, high connectivity in the network is needed to run synchronized processes among all virtual machines.

Please understand that GPU virtualization needs superior hardware and software help and may require good technical skills. When utilizing a GPU server for the concept of virtualization, always be cautious about the software for virtualization and GPU drivers.

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Dedicated Server with GPU for Gaming

GPU-dedicated servers can be utilized for various game servers, particularly for games that require high-performance graphics processing. Casually saying that the game server requires a robust CPU, good memory, and a less expectancy network connection to guarantee easy game playing and quick data transmission among both the client and the server. Nevertheless, the requirement for a GPU completely depends on the particular game and its graphics needs.

For various games, a GPU is not a very necessary component, mainly for games that utilize normal graphics or are developed to work on low-end hardware. In all these situations, a dedicated GPU server with a robust CPU, quick memory, and a fine-quality network connection may be enough. For some games, mainly those with maximum graphics needs, GPUs may be a useful component for dedicated servers.

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GPU Dedicated Server for Data Analysis and Mining

The dedicated GPU server can be the appropriate option for the process of data mining as well as analysis tasks, particularly for big data sets and computation-concentrated processes. When choosing the right GPU server for data analysis and mining, numerous vital components need to be kept in mind.

Firstly, the GPU model is very important for all types of data analysis and mining tasks. The NVIDIA GPU is very famous in the arena of data analysis as well as mining because of its fine performance, CUDA help, and widespread software system. Furthermore, the NVIDIA series of Tesla and some other best GPUs offer extra memory, kernel, and progressive operations, which can considerably enhance the performance of tasks related to data analysis and mining.

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Dedicated Server with GPU for Video Editing

Dedicated GPU server for various projects related to video editing to easily manage all complex tasks with comfort. The correct GPU quickens interpretation times and also processes high-resolution video quality rapidly. This whole setup lets you work more productively and handle requesting video edits without interruptions.

GPU dedicated servers are frequently utilized for tasks related to video rendering due to their high processing ability and the skill to process maximum quantity of data. At the time of choosing a GPU dedicated server for the purpose of video rendering, some crucial factors must be kept in mind.

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How does GPU Dedicated
Server Work

GPU-dedicated servers let customers buy any type of physical server with the resources of a dedicated GPU to easily meet their computing requirements. It works something like this:

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Choose a Correct Server Plan

You always need to choose the correct server plan that easily meets all your requirements in terms of GPU resources.

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Access your server

You can simply install and operate your software on the GPU server and utilize its all-dedicated assets to speed up your applications.

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Configure Your Server

After getting access to the server, you can now modify the configuration of the server as per your precise needs.

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Manage your Server

We are accountable for conserving the physical infrastructure, you will be accountable for handling the software.

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GPU4HOST migrates your information for free without any additional charge while maintaining proper data privacy.

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Frequently Asked Questions

A GPU-dedicated server is a dedicated hosting outcome that has a physical server fortified with a dedicated GPU. Different from outmoded servers that depend exclusively on the CPU, a dedicated GPU server uses the parallel processing power of the GPU to manage various tasks that request important computational assets. This whole setup is best for applications like high-resolution rendering of graphics, AI/ML, and compound data analysis. By influencing the capability of the GPU to process several processes instantaneously, this kind of server considerably increases performance and productivity for all tasks that include complex graphical computations or actual processing of data.

A server does not essentially require a GPU, but having one can offer a lot of advantages for some particular use situations. For activities that contain complex graphical computations, like 3D rendering, AI/ML, or video editing, a dedicated GPU can easily improve performance and productivity. The ability of the GPU to manage tasks related to parallel processing makes it extremely operative for quickening these requesting procedures, which would then depend only on the central processing unit (CPU) and probably experience low performance. But, for numerous normal server applications such as file storage or web hosting, a dedicated GPU is not needed and would not provide any special benefit.

A GPU server provides some considerable advantages, mainly for tasks that need strong computational supremacy. The main benefit is its ability to quicken processing by influencing the parallel processing architecture of the GPU, which lets it manage numerous calculations instantaneously. This makes GPU servers very helpful for various applications like AI models, high-resolution video rendering, and deep learning, where quick information processing is vital. Moreover, GPU servers can knowingly decrease the time needed for scientific simulations, improving complete efficiency and competence.

A dedicated GPU is also recognised as a discrete GPU; it is basically a distinct GPU that is installed as a separate section in any computer system, a lot different from the CPU. Different from included GPUs, which always share memory of the system and also the power of processing with the CPU, any dedicated GPU has its own personal dedicated memory, which is known as VRAM, and processing capabilities. This policy lets it manage all challenging graphical works and compound computations with better productivity as well as speed. Dedicated GPUs are mainly useful for various applications that need high-performance, like video editing, gaming, 3D rendering, and AI/ML.

A dedicated GPU offers numerous important advantages by providing dedicated processing power for various tasks that require thorough computation. Integrated graphics completely share the memory of the system with the CPU, whereas a dedicated GPU has its own dedicated memory, known as VRAM, and also good processing abilities, letting it manage difficult graphical activities and tasks related to parallel processing more powerfully. This led to significantly quicker performance for some big applications, like 3D graphics rendering, and a lot more.

A dedicated GPU is a very important part for every single person and specialists who always need high graphics and the best computation proficiencies beyond what all the included GPUs can provide. For instance, gamers usually need dedicated GPUs to manage the best resolution graphics and frame rates as requested by advanced video games, guaranteeing smooth and fascinating gameplay. Also, experts who are involved in the processes of video editing, animation, and others get a lot of help from dedicated GPUs for their facility to process compound visual data rapidly and powerfully.

Yes, a dedicated GPU can easily drain the battery more rapidly as compared to included graphics. Dedicated GPUs are developed for high-quality computational tasks and frequently use more battery due to their progressive processing capabilities and distinct memory. This boosted power usage can result in a quicker battery reduction, mainly during intensive tasks like video rendering or gaming. Although the performance enhancement offered by a dedicated GPU is useful for challenging applications, operators should be aware of the adjustment in battery life.

There are two main types of GPUs: dedicated (or discrete) and integrated GPUs. Integrated GPUs are developed on the same chip as the CPU and also share the memory system with the CPU, making them extra powerful and worthwhile but not very powerful for specialized graphical tasks. They are appropriate for daily computing activities and basic graphics tasks. In comparison, dedicated GPUs are different modules with their own personal memory (VRAM) and power of processing, developed mainly for managing complex graphical and computational capabilities. Dedicated GPUs are usually discovered in powerful systems and especially in gaming laptops, where their improved abilities validate their higher power usage and price.

SQL Server utilizes a dedicated GPU to power up data analytics and ML-based projects by offloading challenging computations, allowing quicker processing of complex datasets and issues with the help of parallel processing.