The full form of GPU is Graphics Processing Unit. It is a specialized electronic circuit designed to manipulate and render images, videos, and animations rapidly. However, due to their parallel processing capabilities, GPUs are now widely used for general-purpose computing tasks.

A GPU cloud server is a powerful computational server hosted in the cloud equipped with Graphics Processing Units (GPUs). It provides users access to high-performance GPU resources for machine learning, scientific computing, and graphics-intensive applications.

Yes, cloud servers can be equipped with GPUs. Many cloud service providers offer GPU instances as part of their infrastructure, allowing users to leverage the computational power of GPUs for specific workloads that require high-performance parallel processing.

The SLA guarantees 99.99% monthly availability on GPU. For more information, you can contact our subject matter experts.

Cards with GPUs are served via the physical server's PCI bus. PCI Passthrough is a hypervisor feature that allows you to dedicate hardware to a virtual machine by giving direct access to the PCI bus.

Yes, after a reboot, GPU instances can be upgraded to a higher model. However, they cannot be downgraded to a lower model.

Yes, our anti-DDoS protection is included with all CloudOYE solutions at no extra cost.

If you have monthly billing set up, you cannot switch to hourly billing over the month. Before you launch an instance, please take care to select the billing method that is best suited to your project.

Yes, CloudOYE allows you to upgrade/downgrade easily as and when required.

We make use of 16 vCore processors on our GPU-based servers. 16 vCore processors offer enterprise-level computing with high performance, scalability, and advanced security.

Indeed. Our GPU-2x-A100 and GPU-2x-T4 plans accompany two NVIDIA A100 and T4 cards separately. GPU-2x.V100 arrangement accompanies two NVIDIA V100 cards, each with 32GB installed with memory.

GPU cloud servers are known for giving the best cost execution proportion in the business. Similar holds for GPU Instances — beginning as low as ₹40/hour for a devoted NVIDIA T4-based cloud GPU Instance.

We offer 64 GB RAM, GPU 8 core, 480 GB NVMe SSD storage, unlimited data transfer, 24x7 email support, and 16 vCore processors and NVIDIA T4 GPU.

GPU platforms refer to the hardware and software frameworks that enable the utilization of Graphics Processing Units for high-performance computing. These platforms provide developers with the necessary tools, libraries, and APIs to harness the parallel processing power of GPUs for a wide range of applications.

A GPU (Graphics Processing Unit) is a specialized electronic circuit that performs rapid calculations for rendering images, videos, and animations. It works by dividing complex computational tasks into smaller parallel processes that can be executed simultaneously across multiple cores within the GPU. This similar processing capability enables GPUs to perform highly efficient and fast calculations.

GPUs can be physically located in different places depending on the context. In the case of cloud computing, GPUs are typically located within data centers operated by cloud service providers. GPUs are integrated into the respective hardware architecture for personal computers, laptops, or mobile devices.

GPUs are used for various reasons, including their ability to accelerate computationally intensive tasks. GPUs excel in parallel processing, making them highly efficient for machine learning, deep learning, scientific simulations, video editing, and gaming applications. Their immense computational power enables faster and more efficient execution of complex calculations than traditional Central Processing Units (CPUs).

The best GPU for mobile devices depends on performance requirements, power efficiency, and compatibility with the device's hardware and software. Some popular GPUs for mobile devices include the Qualcomm Adreno series, ARM Mali series, and Apple's custom-designed GPUs used in their iPhones and iPads.

The advantages of GPUs include their ability to handle massively parallel computations, which significantly speeds up tasks that can be divided into smaller parts. GPUs are highly efficient in processing large amounts of data simultaneously, making them ideal for data analysis, machine learning, and image processing tasks. Additionally, GPUs can offload computation from the CPU, allowing for better overall system performance.

GPUs are used for AI due to their exceptional parallel processing capabilities. AI applications, intense learning, involve performing numerous matrix calculations simultaneously. GPUs can handle these calculations efficiently, leading to faster training and inference times. The parallel architecture of GPUs enables them to process large amounts of data in parallel, making them well-suited for training and running AI models.

GPUs are powerful due to their architecture and design optimized for parallel processing. Unlike CPUs, which focus on executing tasks sequentially, GPUs are designed to handle thousands of computations simultaneously. This similar processing power allows GPUs to perform complex calculations much faster, making them ideal for computationally intensive tasks like machine learning, scientific simulations, and graphics rendering.

VMware supports GPU virtualization through technologies like NVIDIA GRID and AMD MxGPU. These technologies enable the virtualization of GPUs, allowing multiple virtual machines to share the computational power of a physical GPU. GPU virtualization allows efficient utilization of GPU resources in virtualized environments, catering to workloads that require GPU acceleration.

Once you have deployed a GPU dedicated server from CloudOYE, the very instant you will be delivered high-performance and quality-based service. We have put in place experts also to help you determine the appropriate configuration that can boost your speed, efficiency, etc. Here are some reasons why you should select CloudOye GPU server hosting -

• We provide warranty-based service and replace it with a good, updated part.
• World-class configuration with instant pricing.
• Exceptional customer service with dedicated support to each of our customers.
• Ensures the best quality and reliability in the market.

GPU has gained popularity in the age of supercomputing but follows an older idea of parallel computing. When the CPU runs across a series of tasks and involves a lot of interactivity, GPU, on the other hand, breaks complex problems into millions of separate tasks and works on all of them altogether. CPU has low latency, whereas GPU has high latency throughout the process. CPU can handle very few operations at once as compared to GPU, which can handle thousands of them.

Yes, CloudOYE allows you to expand easily as and when required. We also allow you to migrate to another configuration if not specified with the present one.