Pick your GPU Dedicated Servers Location
We provides high-performance GPU dedicated servers for customers who need powerful computing capabilities. These servers are perfect for running complex simulations, machine learning tasks, or data-heavy workloads. Our GPU dedicated servers feature the latest NVIDIA GPUs, which are designed for efficient parallel processing and data acceleration. You can select from various configurations, including single and dual GPU options, and customize your server settings to install the software and applications you need.
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Key Features of GPU Dedicated Servers
Performance Network
High-capacity network with multiple providers. All speeds are guaranteed and not oversubscribed.
DDoS Protection
Dedicated servers come with monitoring and protection against DDoS attacks.
100% Uptime Guarantee
We guarantee 100% service uptime. Our data centers use built-in redundancy for power, cooling, physical security, and network connectivity to ensure reliability.
Custom Configurations
We can provide nearly any configuration you need. If you’re looking for something specific, contact our sales department.
Didn’t find the right GPU dedicated server?
Let us know what GPU configuration or solution you need, and we’ll create a custom solution just for you. Custom solutions are our specialty and what we enjoy the most!
Advantages of GPU Hosting and Renting Dedicated GPU Servers
GPU hosting offers major benefits for those needing high-performance computing resources. Renting GPU servers allows you to save money, access powerful computing, and easily scale up or down as needed, all while minimizing maintenance and management..
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Cost Savings
GPU hosting can save you a lot of money compared to purchasing a GPU computer. With GPU hosting, you avoid the high costs of buying hardware and handling maintenance and upgrades. Instead, you rent high-performance GPU servers on a pay-per-use basis, which can be more cost-effective for many situations.
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Scalability and Flexibility
GPU hosting lets you easily adjust your computing resources to fit changing needs. You can quickly add or remove GPU instances to handle spikes in demand or adapt to shifting workloads. This flexibility is especially useful for businesses and organizations that need to stay adaptable.
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Instant Availability
GPU hosting gives you immediate access to high-performance computing resources for complex tasks and parallel processing. Renting GPU servers means you can start using the needed resources right away, without waiting for equipment to be purchased and set up.
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Reduced Maintenance and Management
With GPU hosting, you don’t have to handle hardware and software maintenance yourself. The hosting provider manages the infrastructure, including security updates, backups, and hardware repairs. This saves you time and resources, letting you focus on your core business activities.
Get Your Dedicated servers Now!
Click below to view our complete list of continent options. We aim to make your experience with COLO BIRD as easy and convenient as possible.
High-Performance Unmetered Dedicated Servers
Businesses can take advantage of powerful, dedicated servers and fast network connections without the concern of bandwidth limits or extra charges.
Flexible Payment Options That Fit Your Needs
1 Gbps
Take advantage of Unmetered bandwidth and powerful computing performance for your most demanding projects with COLO BIRD's 1 Gbps unmetered dedicated server.
View10 Gbps
Take advantage of Unmetered bandwidth and powerful computing performance for your most demanding projects with COLO BIRD's 10 Gbps unmetered dedicated server.
ViewFrequently Asked Questions About GPU Dedicated Servers
Leveraging the power of Graphics Processing Units (GPUs), GPU dedicated servers are designed for the most demanding computational tasks. Whether you're involved in AI, machine learning, or high-performance computing, understanding GPU servers is crucial for optimizing your operations. Here, we address common questions to guide your decisions.
What is a GPU Dedicated Server?
A GPU dedicated server is a custom dedicated server specifically configured with one or more GPU accelerators. Unlike traditional CPU-only servers, these machines are built to provide high-performance parallel processing capabilities, making them exceptionally efficient for complex computations. We offer a range of powerful Nvidia GPU accelerators, including A100, A40, A16, A10, A2, Tesla P4, Quadro RTX 4000, and K4200, allowing you to choose the ideal hardware for your specific workload.
What are the primary applications and use cases for GPU
Dedicated Servers?
GPU servers are indispensable for applications requiring massive parallel computation and data acceleration. Key use cases include:
- AI & Machine Learning (ML): Accelerating model training, real-time inference, natural language processing, recommendation systems, and predictive analytics.
- Virtual Desktop Infrastructure (VDI): Enhancing remote desktop experiences by providing fast graphics rendering for demanding applications.
- Computer Research and Simulation: Crucial for complex simulations in physics, chemistry, engineering, and scientific modeling.
- Video Transcoding and Rendering: Ideal for media workflows, content creation, and live streaming due to fast encoding and decoding capabilities.
- Data Analytics: Rapidly processing and analyzing large datasets to uncover real-time insights and optimize business processes.
- Gaming: Hosting high-performance game servers that require intensive graphics processing.
How can I deploy a GPU Dedicated Server?
Deploying a GPU dedicated server is straightforward. You select your preferred GPU accelerator(s) when configuring a Custom Dedicated Server that supports these accelerators. Deployment typically takes between 24 to 72 hours, depending on your chosen server region, ensuring your powerful hardware is ready to go as quickly as possible.
What Operating Systems are supported on these servers?
We offer broad operating system support for our Custom Dedicated Servers, including:
- Linux Distributions: Ubuntu (22.04, 24.04), Debian (11, 12), AlmaLinux (8, 9, 10), CentOS (7.9, 8, 9), and Rocky Linux (8, 9).
- Windows Server: 2017, 2019, 2022.
- Virtualization Hypervisors: VMware ESXi (7, 8).
Additionally, you can self-install any other custom Operating System via IP KVM.
Why is a GPU used in a server, and how does it differ from
a
CPU?
A GPU significantly increases a server's computational power, especially for parallel tasks. While CPUs excel at sequential processing and general-purpose tasks, GPUs possess thousands of cores designed to perform numerous calculations simultaneously. This parallel processing capability makes GPU servers vastly more efficient than CPU-only servers for tasks like AI model training, graphics rendering, and machine learning, which involve large datasets and complex, simultaneous calculations.
What are some popular GPUs commonly used in hosting
environments?
In hosting environments, the most popular and powerful GPUs are typically from Nvidia's Tensor Core series due to their exceptional performance for high-performance computing and deep learning. These include:
- Nvidia A100
- Nvidia A40
- Nvidia A10
These GPUs provide excellent parallel processing capabilities and efficiency, making them highly suitable for various dedicated server setups.
How do I choose the right GPU dedicated server for my
needs?
Selecting the right GPU server involves several considerations to match your workload:
- Application Requirements: Identify the specific applications (e.g., AI training, video rendering, VDI) and their exact GPU performance needs.
- GPU Performance & Model: Determine the required GPU model and quantity (e.g., Nvidia A100 for heavy AI training, A10 for general-purpose acceleration).
- Memory (RAM & VRAM): Ensure sufficient system RAM for the CPU and, critically, ample Video RAM (VRAM) for the GPUs, as GPUs use their own dedicated memory for processing.
- Storage: Opt for high-speed NVMe SSDs for quick data access, crucial for data-intensive workloads.
- Networking: Look for high-throughput network interfaces (e.g., 10 Gbps or higher) if you're moving large datasets.
- Scalability: Consider future growth and if the provider offers options to upgrade GPU power or other resources.
- Cost-Effectiveness: Balance performance needs with your budget, considering both initial setup and long-term operational costs.
- Software Compatibility: Verify compatibility with your preferred software frameworks and libraries (e.g., TensorFlow, PyTorch, CUDA).
- Technical Support: Assess the availability and quality of technical support for deployment and ongoing assistance.
Does a GPU server use system RAM?
No, a GPU server uses its own dedicated Video RAM (VRAM) for its processing tasks. It does not directly use the server's system RAM, allowing for independent and highly efficient data handling for graphical and parallel computations.
Does every server need a GPU server?
Not every server requires a GPU. For general web hosting, databases, or basic applications, a CPU-only server is sufficient. However, for computationally intensive tasks such as AI, machine learning, complex data analytics, professional graphics rendering, or video processing, a dedicated GPU is absolutely necessary to achieve optimal performance and efficiency.
Can I increase GPU memory?
Generally, you cannot physically add more memory to an existing GPU. The VRAM capacity is fixed by the GPU's design. If you require more GPU memory, the solution is to upgrade to a GPU model that inherently offers a larger VRAM capacity.