Get outstanding training and inference times with a GPU equipped dedicated server, perfect for parallel task processing and complex deep machine learning frameworks.
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Deploy your most resource intensive applications on a GPU dedicated server, designed specifically for the training of deep learning models.
Get access to the latest GPUs from NVIDIA, AMD and Intel, with the best available data processing speeds, optimal for machine learning projects.
When you combine a high-performance GPU with the raw processing power of a dedicated server you get the best possible efficiency out of a single machine. Replace up to 30 serves with a single GPU server.
*Results may vary based on server configuration.
Your deep machine learning project requires on the fly technical assistance. A team of GPU server experts are ready to assist around the clock, via phone or live chat.
NVIDIA TESLA T4
Choose NVIDIA’s Tesla T4 for multi-precision computing power, through its Tensor Core technology.
The T4 is up to 40 times faster than a conventional CPU and up to 3.5 times faster than its Pascal predecessor.
Transcode up to 38 full HD video streams simultaneously, when you pair a Tesla T4 with one of our HPE server configurations.
*Results may vary, based on server configuration.
Compatible: VMWare ESXi, Citrix Xenserver, KVM, Linux, Windows.
Coral USB Accelerator
With the new Coral USB Accelerator, you can add Edge TPU to any Linux-based system. A low cost for power that can provide high-performance ML.
Compatible with Linux machines, Debian 6.0 or higher, or any derivative (such as Ubuntu 10.0+), but also with Raspberry Pi (213 Mode B/B+).
NVIDIA GeForce RTX 2080 / RTX 2080 Ti
Get up to six times the performance of the Pascal chip predecessor, with the RTX 2080, powered by NVIDIA’s new Turing chip architecture.
Compatible with Linux, CUDA/OpenCL, KVM.
NVIDIA GeForce GTX 1080/1070 TI
Get the best performance in graphics rendering, computing or mining, with the Pascal architecture based GPU from NVIDIA, the GeForce GTX 1070/1080.
Compatible with Linux, CUDA/OpenCL, KVM.
NVIDIA TESLA P4/P40/P100
NVIDIA’s Pascal chip based GPU boards are best suited for video transcoding and machine learning tasks. Use a Tesla P4 GPU to transcode up to 20 simultaneous video streams, H.264 or H.265, including H.265 8k. Results may vary based on stream bitrate and server configuration.
A more robust version of the P4 is the Tesla P40, with more than twice the processing power.
Looking for the perfect GPU for machine learning? The Telsa P100 GPU board can process up to 18.7 TeraFLOPS of inference performance. A single P100 GPU dedicated server can replace up to 25 CPU servers. Result may vary based on server configuration.
Compatible: VMWare ESXi, Citrix Xenserver, KVM, Linux, Windows.
NVIDIA TITAN V
Get your deep learning results up to 1.5x faster, when compared to the P100 GPU board. Process up to 110 TeraFLOPS of inference performance with the Titan V GPU. Use the Titan V to predict the weather or to discover new energy sources. It’s the optimal GPU choice for precise, fast results.
A single Titan V GPU server can replaced up to 30 single CPU servers. Results may vary based on server configuration.
Compatible: VMWare ESXi, Citrix Xenserver, KVM, Linux, Windows.
INTEL XEON PHI COPROCESSOR 7120P
Add a coprocessor board to your dedicated server to exponentially increase your processing power. A single Phi 7120P board adds 61 cores to your server, creating one of the most powerful servers available on the market to date.
Compatible: VMWare ESXi, Citrix Xenserver, KVM, Linux, Windows.
Your resource intensive applications need a GPU dedicated server that can process artificial intelligence tasks with high efficiency. Get access to server hardware that is matched with the optimal GPU boards to deliver to best possible results for Deep Learning, Machine Learning and AI tasks. Your services are backed by our industry leading 99.9% uptime SLA and supported by a team of machine learning experts, around the clock.
Configure your GPU dedicated server and submit your order. It only takes a few moments.
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