GPU Hosting, Dedicated Server with GPU, Dedicated GPU Server Rent

GPU Mart offers top-tier GPU Hosting for your high-performance computing projects! Our advanced infrastructure delivers unmatched power and speed for complex tasks like AI/deep learning, rendering, streaming, and gaming. Choose from 20+ top GPU models, explore various GPU solutions, and access a wide range of GPU applications. Elevate your projects with GPU Mart's premium hosting services today!

10K+

GPU Servers Delivered

1.5K+

Active Graphics Cards

5Year

GPU Hosting Expertise

24/7

Full Human Customer Service

Affordable GPU Servers with 20+ Nvidia GPU Models for Numerous Business Scenarios

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Multi-GPU Server
Summer Sale

Express GPU VPS - GT730

16.8/mo
Save 42% (Was $29.00)
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  • 8GB RAM
  • 6 CPU Cores
  • 120GB SSD
  • 100Mbps Unmetered Bandwidth
  • Once per 4 Weeks Backup
  • OS: Linux / Windows 10
  • Dedicated GPU: GeForce GT730
  • CUDA Cores: 384
  • GPU Memory: 2GB DDR3
  • FP32 Performance: 0.692 TFLOPS

Express GPU VPS - K620

21.00/mo
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  • 12GB RAM
  • 9 CPU Cores
  • 160GB SSD
  • 100Mbps Unmetered Bandwidth
  • Once per 4 Weeks Backup
  • OS: Linux / Windows 10
  • Dedicated GPU: Quadro K620
  • CUDA Cores: 384
  • GPU Memory: 2GB DDR3
  • FP32 Performance: 0.863 TFLOPS

Basic GPU VPS - P600

29.00/mo
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  • 16GB RAM
  • 12 CPU Cores
  • 200GB SSD
  • 200Mbps Unmetered Bandwidth
  • Once per 4 Weeks Backup
  • OS: Linux / Windows 10
  • Dedicated GPU: Quadro P600
  • CUDA Cores: 384
  • GPU Memory: 2GB GDDR5
  • FP32 Performance: 1.2 TFLOPS

Lite GPU - GT710

45.00/mo
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  • 16GB RAM
  • Quad-Core Xeon X3440
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia GeForce GT710
  • Microarchitecture: Kepler
  • Max GPUs: 1
  • CUDA Cores: 192
  • GPU Memory: 1GB DDR3
  • FP32 Performance: 0.336 TFLOPS

Lite GPU - GT730

49.00/mo
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  • 16GB RAM
  • Quad-Core Xeon E3-1230
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia GeForce GT730
  • Microarchitecture: Kepler
  • Max GPUs: 1
  • CUDA Cores: 384
  • GPU Memory: 2GB DDR3
  • FP32 Performance: 0.692 TFLOPS
  • 2.5 Times More Powerful than Express GPU VPS - GT730

Lite GPU - K620

49.00/mo
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  • 16GB RAM
  • Quad-Core Xeon E3-1270v3
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Quadro K620
  • Microarchitecture: Maxwell
  • Max GPUs: 1
  • CUDA Cores: 384
  • GPU Memory: 2GB DDR3
  • FP32 Performance: 0.863 TFLOPS

Express GPU - P600

52.00/mo
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  • 32GB RAM
  • Quad-Core Xeon E5-2643
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Quadro P600
  • Microarchitecture: Pascal
  • Max GPUs: 1
  • CUDA Cores: 384
  • GPU Memory: 2GB GDDR5
  • FP32 Performance: 1.2 TFLOPS

Express GPU - P620

59.00/mo
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  • 32GB RAM
  • Eight-Core Xeon E5-2670
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Quadro P620
  • Microarchitecture: Pascal
  • Max GPUs: 1
  • CUDA Cores: 512
  • GPU Memory: 2GB GDDR5
  • FP32 Performance: 1.5 TFLOPS

Express GPU - P1000

64.00/mo
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  • 32GB RAM
  • Eight-Core Xeon E5-2690
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Quadro P1000
  • Microarchitecture: Pascal
  • Max GPUs: 1
  • CUDA Cores: 640
  • GPU Memory: 4GB GDDR5
  • FP32 Performance: 1.894 TFLOPS

Basic GPU - GTX 1650

99.00/mo
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  • 64GB RAM
  • Eight-Core Xeon E5-2667v3
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia GeForce GTX 1650
  • Microarchitecture: Turing
  • Max GPUs: 1
  • CUDA Cores: 896
  • GPU Memory: 4GB GDDR5
  • FP32 Performance: 3.0 TFLOPS

Basic GPU - T1000

99.00/mo
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  • 64GB RAM
  • Eight-Core Xeon E5-2690
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Quadro T1000
  • Microarchitecture: Turing
  • Max GPUs: 1
  • CUDA Cores: 896
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 2.5 TFLOPS

Basic GPU - K80

109.00/mo
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  • 64GB RAM
  • Eight-Core Xeon E5-2690
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Tesla K80
  • Microarchitecture: Turing
  • Max GPUs: 2
  • CUDA Cores: 4992
  • GPU Memory: 24GB GDDR5
  • FP32 Performance: 8.73 TFLOPS
  • Suitable for Basic AI, Data Modeling, High Performance Computing, etc. Supports CUDA versions 11.4 and lower.

Benefits of Using GPU Hosting and Dedicated GPU Server Rental

GPU hosting can provide significant benefits for organizations and individuals that need access to high-performance computing resources. By renting access to GPU servers, you can save costs, access powerful computing resources, and scale up or down as needed, all while reducing the need for maintenance and management.
Cost Savings

Cost Savings

GPU hosting can provide significant cost savings compared to buying GPU computer. With GPU hosting, you don't need to invest in expensive hardware or pay for the associated maintenance and upgrades. Instead, you can rent access to high-performance GPU servers on a pay-per-use basis, which can be much more cost-effective for many use cases.
Instant Availability

Instant Availability

GPU hosting provides access to high-performance computing resources that can handle complex computations and parallel processing tasks. Renting GPU servers allows immediate access to the required computing resources without the need to wait for equipment procurement and deployment.
Scalability and Flexibility

Scalability and Flexibility

With GPU hosting, you can easily scale your computing resources up or down to meet changing needs. You can quickly add or remove GPU instances as needed, allowing you to handle spikes in demand or adjust to changing workloads. This provides a high degree of flexibility and agility, which can be especially valuable for businesses and organizations that need to adapt to changing circumstances.
Reduced Maintenance and Management

Reduced Maintenance and Management

With GPU hosting, you don't need to worry about maintaining and managing hardware and software on your own. The hosting provider takes care of the infrastructure and maintenance, including security updates, backups, and hardware repairs. This frees up your time and resources, allowing you to focus on your core business activities.

Affordable GPU Hosting Provider with Stunning Quality: GPU Mart

GPU Mart is committed to providing professional GPU server hosting. At present, we have provided a variety of GPU dedicated hosting solutions for deep learning, AI, Android emulators, games, and video rendering. Chat with our experts at any time.
Bare Metal GPU Servers
Bare Metal GPU Servers
Experience superior performance for demanding applications with GPU dedicated server. With no CPU/RAM/GPU sharing, your server effortlessly manages heavy workloads.
GPU Hosting Experts
GPU Hosting Experts
With 5 years of experience in GPU server hosting, GPU Mart provides expertly configured GPU dedicated servers tailored to various industry needs. Our team of GPU specialists is available 24/7 to offer technical support, ensuring smooth operation of your GPU servers.
Cheap GPU Hosting
Cheap GPU Hosting
GPU Mart is dedicated to providing cost-effective GPU hosting services, making GPU computing accessible to everyone worldwide. Our prices are among the most competitive in the market, catering to both individual IT enthusiasts and professionals.
Premium Hardware
Premium Hardware
Our GPU dedicated servers and VPSs are equipped with high-quality NVIDIA graphics cards, efficient Intel CPUs, pure SSD storage, and renowned memory brands such as Samsung and Hynix.
Dedicated GPU Cards
Dedicated GPU Cards
When you purchase a GPU server from GPU Mart, whether it's a GPU dedicated server or GPU VPS, you benefit from dedicated GPU resources. This means you have exclusive access to the entire GPU card's computing power, including all GPU memory, cores, and other resources.
USA Quality Since 2005
USA Quality Since 2005
Operating multiple data centers across the USA since 2005, GPU Mart ensures top-tier infrastructure with advanced power and network equipment. Our data centers are meticulously maintained, featuring state-of-the-art cooling systems to guarantee optimal GPU server performance and 99.9% uptime year-round.

Common Use Cases of GPU Dedicated Server Hosting

These are just six examples of many use cases hosted by GPU Mart. The versatility and powerful functions of GPU server hosting make it a valuable resource for a wide range of applications, especially those that need a lot of parallel processing capabilities.
Machine Learning and AI

Machine Learning and AI

GPU servers are commonly used to train and run machine learning models and deep learning algorithms. GPUs can handle the massively parallel computations involved in these applications, reducing training time and improving accuracy. GPU servers can be used in a variety of machine learning and artificial intelligence applications, such as image and speech recognition, natural language processing, and recommender systems.
Scientific Simulations

GPU Hosting for Scientific Simulations

Many scientific simulations, such as those used in weather forecasting, fluid dynamics, and materials science, require significant computing power. GPUs can speed up these simulations by processing the large amounts of data involved in parallel. GPU servers are also commonly used for simulation-based optimization and machine learning-based data analysis in scientific research.
Video Rendering and Transcoding

Video Rendering and Transcoding

Video rendering and transcoding involve processing large amounts of data to create high-quality video content. GPUs dedicated servers can speed up this process by parallelizing the video encoding and decoding process. This makes GPU servers ideal for video production, streaming, and editing applications.
Virtual Reality

Gaming and Virtual Reality

High-performance dedicated GPU servers are used to support online gaming and virtual reality applications that require massive processing power and high frame rates. This enables players to experience a more realistic and immersive gaming experience. Cloud gaming providers typically use GPU servers to deliver gaming services accessible from any device.
OBS Streaming

GPU Hosting for OBS Streaming

GPU dedicated Servers can provide significant advantages in streaming, such as improving performance, accelerating rendering, enabling customization, and allowing you to multitask. Especially when you use software such as OBS (Open Broadcast Software), the powerful GPU also allows you to perform multiple tasks at the same time when streaming.
Emulators

GPU Hosting for Android Emulators

GPU Dedicated Servers can be a great option for running Android emulators, especially if you're running multiple instances at once or need high-performance and customization options. By providing fast processing, improved performance, and the ability to customize your settings, a GPU Dedicated Server can help you to create a stable and efficient environment for your Android emulator.

How GPU Hosting Works?

GPU hosting provides a flexible and scalable way to access high-performance computing resources without purchasing and maintaining expensive hardware. By renting the access rights of the remote GPU server, you can perform complex calculations, run simulations, and accelerate machine learning, AI algorithms, and other applications.
step1

Select plan, Configure Instance

After selecting a plan, we will configure your server to meet your needs. This may involve selecting the amount of memory, storage, and processing capacity you need, as well as the operating system and software you want to use. A remote connection account will be sent to you by email.
step2

GPU Instance Trial or Pay

GPU Mart will charge you the usage fee of the GPU instance according to the time you use the GPU instance. You can usually choose to pay by month or year. The cost of GPU hosting is related to the resources and payment cycle you choose.
step3

Access Instance

After getting the GPU server, you can access it through a remote desktop connection, a command line interface, and other methods. Then, you can install and run the software, upload data, and perform calculations on the remote GPU server.
step4

Manage Instances

You will be responsible for managing the software and data on the GPU instance, as well as any security or maintenance tasks that may be required. GPU Mart will do its best to provide support and resources to help you manage your instance, but you usually need to be responsible for any customization or configuration you make.

Case Studies: Leveraging GPU Mart for High-Performance Computing Solutions

AI Researcher
Game Developer
Live Streaming Company
stories

Background

Ellery is an AI researcher working on training deep learning models for image classification tasks. She requires high-performance computing resources with GPU capabilities to train and optimize her AI models.

Challenges

Need for powerful servers with multiple GPUs to accelerate the training process and handle large datasets.

Requirement for reliable and responsive technical support to troubleshoot issues and optimize model training algorithms.

Desire for cost-effective hosting solutions to minimize expenses while maximizing computing power.

Solution

Ellery chose GPU Mart for its dedicated GPU servers optimized for AI model training. With GPU Mart's state-of-the-art hardware and expert technical support, she could efficiently train and optimize her deep learning models for image classification tasks. The cost-effective pricing of GPU Mart's hosting solutions allowed her to maximize her computing budget without compromising performance.

Outcome

Ellery's AI research projects progressed rapidly with GPU Mart's high-performance servers. She achieved significant improvements in model accuracy and training efficiency, thanks to GPU Mart's reliable infrastructure and responsive technical support. With GPU Mart's scalable hosting solutions, Ellery could easily adjust server resources to meet changing research requirements and accommodate growing datasets.

FAQs of GPU Dedicated Server Hosting

Find answers to the most frequently asked questions about GPU dedicated server hosting.

What is hosting with GPU?

GPU hosting is a hosting for servers packed with graphics cards, designed to harness this raw processing power. Using an offloading process, the CPU can hand specific tasks to the GPUs, increasing performance.

What is GPU Dedicated Server?

A GPU dedicated server is a physical server dedicated to a user or organization and equipped with one or more GPUs (graphics processing units). These servers are usually used for high-performance computing tasks that require a lot of parallel processing capabilities, such as scientific simulation, machine learning, and video rendering.
Compared with CPU-based servers, GPU servers are usually much faster for tasks that can be processed in parallel across multiple cores. This is because GPUs have much more cores than CPUs, which makes them very suitable for decomposing into many small computing tasks that can be executed at the same time. GPU dedicated servers can be purchased or leased from various hosting providers. They differ in GPU type, GPU quantity, available memory and storage. These servers can be managed remotely, allowing users to access their computing resources from anywhere through Internet connections.

What is dedicated server with GPU Rental?

A dedicated server with GPU rental is a service offered by hosting providers that allows users to rent a dedicated physical server equipped with one or more GPUs (graphics processing units) for a specified period of time. This service can be particularly useful for tasks that require high-performance computing resources, such as machine learningientific simulations, and video rendering.
Unlike virtualized GPU instances, dedicated servers with GPU rental offer users full access to the physical hardware, providing more control and customization options. Users can install their own software and configure the server to meet their specific requirements. Additionally, dedicated servers with GPU rental can offer more consistent performance and higher throughput compared to shared resources. When renting a dedicated server with GPU, users can typically choose from a range of specifications, including the type and number of GPUs, the amount of memory and storage, and the processing power of the CPU. The cost of the service will depend on the specifications selected and the duration of the rental.

How do I choose the right GPU instance for my needs?

When choosing a GPU instance, you should consider factors such as the type and complexity of the applications you will be running, the amount of memory and storage you need, and the level of support and customization you require.

Can I use a GPU dedicated server for streaming or running Android emulators?

Yes, a GPU dedicated server can be used for streaming and running Android emulators. By providing improved performance, faster processing, multi-tasking, and customization options, a GPU dedicated server can help you to create a stable and efficient environment for these applications.

What are some common GPUs used in hosting environments?

Some common GPUs used in hosting environments include Nvidia GeForce, Quadro, Tesla, and RTX Server. The specific GPU you choose will depend on your needs and the applications you will be running.

Why put a GPU in a server?

Putting a GPU in a server can significantly increase its computing power, making it capable of handling more complex and demanding tasks. GPUs, or graphics processing units, are highly specialized processors that are designed to handle parallel computing tasks, such as those required for machine learning, scientific simulations, and gaming.
Compared to traditional central processing units (CPUs), GPUs are more efficient at handling compute-intensive workloads because they can perform multiple calculations simultaneously. This is due to the fact that GPUs have many more cores than CPUs, which allows them to process data much faster and in parallel.
By adding a GPU to a server, you can accelerate compute-intensive applications and reduce processing times, leading to improved performance, reduced latency, and increased efficiency. This is particularly important for applications that require real-time processing, such as video rendering, transcoding, and streaming, or for tasks that involve large datasets, such as machine learning and data analytics.