Rent a dedicated server with GPU. Our bare metal servers are equipped with powerful AMD and Nvidia GPU accelerators designed for resource-intensive applications like AI & ML, data processing, and gaming. Deploy a pre-configured server in minutes, or fully customize your GPU server for full control.
Our pre-built dedicated servers are prepared and provisioned automatically from the ground up
Control your servers with a few clicks, or automate server management via RESTful API
Manage your expenses with hourly billing, or save money by ordering fixed-term services
Perfectly suited for high-end workloads, mission-critical applications and more.
Add your selected GPU upon your server deployment via Client Portal
Full server hardware belongs to a single user with zero virtualization
Automate your server management through REST API and DevOps integrations
Use Crypto, Credit Card, PayPal, or any other payment option
Our network is protected against the most common DDoS attacks by default
Get from 30 to 100TB of free monthly egress traffic included with your server
You may add private subnets, floating or DDoS protected IPs on demand
Add free backup storage and extend it to 2TB later when you scale up
Configure your GPU server to meet your specific requirements after choosing a GPU hosting plan from Cherry Servers. This includes selecting the desired GPU model, memory, storage capacity, and operating system. You can also choose any pre-installed software or additional features you need. Once the configuration is complete, you’ll receive login credentials via email to access your GPU server remotely.
Cherry Servers allows you to pay based on the usage time of your GPU instance. You can choose from flexible billing options. We have hourly, monthly, and yearly payment plans, which help you test the infrastructure without a long-term commitment. The cost is determined by the GPU resources you select and the chosen payment cycle. Another perk is the ability to make payments using various methods, such as crypto coins, credit/debit cards, bank transfers, and digital wallets.
Once your GPU server is ready, you can access it through remote desktop connections, SSH, or other command-line interfaces. After connecting, you can install the necessary software, upload your data, and run computations or tasks on the server's GPU.
You are responsible for managing the software, applications, and data on your GPU instance. This includes security, backups, and maintaining the software environment. Cherry Servers provides customer support and resources to assist you in managing your instance, but any customizations or configurations remain under your control. Some of these resources are 100GB of backup storage to securely store your data on a network-attached system, as well as elastic block storage for flexible and secure data handling.
GPU servers are essential for training deep learning models and implementing AI algorithms because they help manage large datasets alongside complex computations. Tasks such as training neural networks, performing image recognition, natural language processing, and running autonomous systems are all dramatically sped up by GPUs. This makes them helpful in modern AI research and applications.
Machine learning frameworks like TensorFlow and PyTorch use GPUs' parallel processing power to efficiently handle the training and testing of models on large datasets. GPU servers enable faster model development and optimization, making them ideal for applications ranging from predictive analytics to recommendation systems and personalized marketing.
High-quality real-time graphics rendering is key to providing immersive experiences in gaming and virtual reality. GPU servers power the intensive graphical computations required and ensure smooth frame rates, realistic environments, and low latency. This makes them vital for game development, VR simulations, and streaming services, where consistent performance is critical.
GPU servers are widely used in high-performance computing for scientific research, weather modeling, and molecular simulations. Their ability to execute many calculations in parallel makes them ideal for computationally heavy tasks that require vast processing power. This allows for breakthroughs in physics, chemistry, and climate research.
Companies can avoid major capital investments and maintenance because they rent GPU servers rather than buying costly hardware. Organizations can pay for GPU as needed on a project basis through flexible options such as pay-as-you-go while avoiding dealing with physical infrastructure management overheads.
GPU servers are useful for different industries, including healthcare, finance, automotive, and entertainment. Such servers are used in medical imaging, financial modeling, and even for graphics rendering in video games. Therefore, companies looking to innovate and use advanced technologies in their operations can find GPU servers a worthy addition.
Modern machine learning and AI frameworks are optimized for GPU architecture. This allows for the implementation of advanced algorithms that would be computationally prohibitive for CPUs alone. Consequently, researchers and developers can experiment with more complex models and methods, achieving better accuracy and performance in tasks like natural language processing and image analysis.
GPU Dedicated Server is any Custom Dedicated Server that supports GPU accelerators. We offer Nvidia A100, A40, A16, A10, A2, Tesla P4, Quadro K2200 and K4200 GPU accelerators for you to choose from. These servers are ideal for applications such as machine learning, AI, gaming, and scientific simulations. They provide high-performance and parallel processing capabilities that increase the overall computational efficiency.
You may add your selected GPU when deploying a Custom Dedicated Server that supports GPU accelerators. It will take up to 24 hours to get your GPU Dedicated Server deployed. It will take from 24 to 72 hours to get your GPU Dedicated Server deployed, depending on your preferred region.
We support the following operating systems for Instant Dedicated Servers: Ubuntu (20.04, 22.04, 24.04), CentOS (7), CloudLinux (7), Debian (11, 12), AlmaLinux (8, 9), OpenSUSE (Leap 15), Windows Server (2019, 2022), VMware ESXi (7, 8), and any other custom Operating System via IP KVM self-install.
Nvidia A100, Nvidia A40, and Nvidia A10 Tensor Core GPUs are commonly used in hosting environments. These GPUs are designed for high-performance computing, deep learning, and data processing tasks, providing excellent parallel processing capabilities and efficiency. This makes them suitable for various applications in cloud and dedicated server setups.
The use of a GPU in a server greatly increases the computation power, primarily for parallel tasks. These include AI, graphics rendering, and machine learning. Large datasets and complex calculations yield optimal performance output when handled by GPUs and are thus more efficient compared to a CPU-only server. They are critical in scenarios that involve a lot of data operations.
When choosing a GPU server, consider the type of applications you plan to run, required GPU performance, memory, and storage needs. Evaluate different hosting providers and compare pricing, support options, and scalability. Additionally, make sure it is compatible with your software frameworks and assess the availability of technical support to aid deployment.
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