Dedicated GPU Servers Features
Custom GPU Servers
We offer custom GPU servers for deep learning, rendering, neural networks, and other verticals like machine learning. Apart from the usual hardware customization like RAM, Storage and CPU we can offer a variety of enterprise-grade graphics cards from Nvidia Quadro to the top-notch A6000 models. While we have certain configurations in stock, we understand that each case is unique, so we can customize each server for a particular task. We use only quality enterprise-grade hardware from Dell, Supermicro, Samsung, Nvidia, AMD, and Cisco, to ensure that your business tasks and goals are executed on time and with excellent quality.
What is dedicated GPU server?
A dedicated GPU (graphic processing unit) server is a server with an installed graphics card to cope with specific tasks set by the developers and business. GPUs have become increasingly popular when developers unlocked a new way of utilizing their processing power to calculate model training in deep learning more efficiently than CPU.
Our GPU server hosting service offers a powerful set of GPU-powered dedicated servers. With GPUs installed, the amount of raw processing power of our dedicated servers is much more efficient than what can be achieved with CPU processors alone.
With our selection of GPU Bare Metal Dedicated Servers, you will have the infrastructure necessary to deploy high-performance computing with significantly greater processing benefit compared to CPU-driven dedicated servers. This is due to the thousands of efficient cores designed to process information faster. These are bare metal servers powered by your choice of NVIDIA GeForce Quadro, TESLA, or GRID GPU cards.
When to choose a Dedicated GPU Server
GPU servers become more often and often in demand thanks to a variety of use cases. Here we will take a glance at how you can utilize a dedicated GPU server:
Use cases that utilize high GPU power and parallel processing include heavy rendering tasks, image manipulation, video streaming and inference, and the ability to provide secure, scalable anywhere/anytime access to applications, to name a few. These types of cloud Deployments using a GPU, combined with reliable networking and fast NVMe / SSD storage, can provide cost-effective, high-performance, and agile solutions for enterprises looking to leverage the latest technologies to achieve their business goals. Not to mention that GPU renders faster than CPU.
CPUs are great when processing numbers, but not at speed. GPU server on the other side can process accurate floating-point arithmetic and complex mathematical and scientific programming in parallel. GPU has thrice as many cores as CPU has, thus it means that it can process big data in big chunks simultaneously and x3 times faster. Whenever you need to extract something from a huge amount of data your dedicated GPU server is an option here.
Putting all the workload on the CPU can become too much stressful for the system and it can start throttling. Instead, it is better to allocate resource-intensive tasks to GPU to free up enough resources for other tasks as well as maintain the speedy performance of the server. Certainly, there are some tasks that can be done only by the CPU, but if there is an option to transfer some of the tasks to GPU why not do it?
Machine Learning & Deep Learning
Probably one of the biggest use cases nowadays is machine learning and deep learning including artificial intelligence. Training your models with a CPU is a very slow and expensive process in terms of electricity and traffic you spend. Why not do it faster with GPU? The answer is still the same - parallel processing. It just makes everything faster. The CPU cannot allow it to do it because it processes every request in the queue.
Video Encoding and Streaming
Another great use case is video encoding and streaming. Imagine that you are a media marketing agency. We’ll bet that you shoot a ton of reels with different promo videos for different companies. Not always it should be 4k quality with uncompressed bitrate, right? Here is where encoding is required. Plus you are making a montage in After Effects, or Premier. After you compile the video it should take some time before it can be compiled in one video. Processing it all with a CPU is a dauntless task. When you are streaming live events GPU servers are also required hardware. Converting and handling video are tasks that are marked as resource-heavy and GPUs can often ease the load on the server at the same time improving the output.
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