NVIDIA Tesla dedicated servers equipped with professional NVIDIA GPU cards are ideal option for AI, Deep Learning and HPC - configurable with up to 4x GPU's for massively paralell computing.
These GPU servers are tailored for heavy loads and they can radically increase performance in areas of data science, ML (machine learning), video rendering, visualization, and all manner of scientific computing.
GPU passthrough allows to directly present an internal PCI GPU to virtual KVM machines. The GPU card is dedicated to the VM and cannot be used by other clients. GPU performance in virtual machines matches GPU performance in dedicated servers. Since we use large multi-card nodes for vGPU, virtual machines come at a cheaper price.
Looking for a non-standard OS? We have a wide selection of ISO images in our control panel. Or you can use your own image and install the OS via IPMI. All our servers are unmanaged. Administration services can be provided for a fee. Please contact us for any questions.
The selected collocation region is applied for all components below.
Do you need a GPU server based on NVIDIA TESLA V100 / T4 / P100? Get a quote!
We provide a free trial period for companies only.Specify your corporate address in the application form!
GPU servers with RTX A4000 and RTX A5000 cards are already available to order. NVIDIA RTX A4000 / A5000 graphics cards are the closest relative of the RTX 3080 / RTX 3090, but have double the memory.
Order a serverA special offer for our instant dedicated servers built with 2x Intel Xeon 2670 CPUs housed in our TIER III class data center in the Netherlands
Order a serverHOSTKEY is delivering selected premium dedicated servers with top-speed 10G connectivity in the Netherlands. 10G interconnect provides low latency and top speed to your services.
Find out moreReady-to-use servers with a discount. We will deliver the server within a day of the receipt of the payment.
Order nowYou will be first to receive useful tips and special offers
10.05.2022
News
We are offering up to 20% discount on AMD RYZEN 9 high performance servers and GPU servers with the latest NVIDIA graphics cards.
09.05.2022
Blog
A step-by-step guide on how to switch to RockyLinux or AlmaLinux - popular free distributions that are binary compatible with RedHat Enterprise Linux (RHEL).
08.05.2022
Blog
Learn how to use a GPU for the deep learning process in machine learning.
With GPU accelerated server platforms, high-performance computing become a key to empowering progress in science and engineering. Equipped with Nvidia Tesla GPU, servers are capable of greatly boosting performance thanks to parallel computing power of graphical processing units.
In just a few minutes a GPU dedicated server that can host up to eight NVIDIA Tesla GPU accelerators can do jobs that take days or even weeks when a regular CPU server is used. What is more, if you compare GPU server cost with that of CPU, it is clear that for a great variety of tasks it will be reasonable to rent a GPU server rather than a conventional CPU-only one.
Hundreds of software applications have been already GPU-accelerated, and their number is growing rapidly.
No wonder that Nvidia Tesla certified servers are extensively employed in lots of spheres where heavy data streams are to be processed - artificial intelligence, training sophisticated deep learning networks, visualizing big data, virtual reality, augmented reality, to name a few.
Also, processing results of scientific research often requires parallel computing. Among the sciences that could greatly benefit from use of GPU servers are quantum chemistry and physics, geoscience and bioinformatics, climate and weather modeling, computational fluid dynamics, structural mechanics, and many more.
What is more, thanks to GPU-Accelerated Server Platforms from Nvidia, now it is possible to pick an ideal server for any workload, Nvidia offers classes of servers optimal for various applications: training, supercomputing, and inference (HGX-T, SCX, and HGX-I respectively). providing both scientists and businesspeople with highly efficient but affordable solutions.
Tesla cards are also widely applied in heterogeneous computing systems, which make the most of both CPUs and GPUs to achieve higher energy efficiency and lower cost.