Accelerate the training of your deep learning systems with a GPU that overcomes the data processing speed of common CPUs by up to 200x.
Get a fully dedicated GPU, no virtualization, no shared resources.
Get a GPU dedicated server that gives you more parallel-processing power and more memory bandwidth. Drastically improve your fair training and inference processing times in production environments.
The most efficient solution for parallel computing. Running a machine learning project with a large data set? Get faster results and improve your ROI with a GPU dedicated server or a cluster of servers.
Your business needs the latest GPUs, that generate results precisely and fast, every time. GPU servers are perfect for artificial intelligence projects with enormous volumes of parallel instructions.
Get instant access to a team of GPU server experts around the clock. We’re available via phone or live chat, with an average response time of just 45 seconds.
The T4 introduces Tensor Core technology with multi-precision computing, making it up to 40 times faster than a CPU and up to 3.5 times faster than its Pascal predecessor, the Tesla P4.
Get access to 8.1 TFLOPS of single precision performance from a single T4 GPU.
Transcode up to 38 full HD video streams simultaneously with a single Tesla T4 GPU paired with our HP BL460c blade server.
*Results may vary, based on server configuration.
Compatible: VMWare ESXi, Citrix Xenserver, KVM, Linux, Windows.
You can now add an Edge TPU coprocessor to any Linux-based system with the Coral USB Accelerator designed by Google. The small ASIC chip provides high-performance ML inferencing with low power cost. For example, it can execute 100fps on MobileNet v2 models, while using very little power (500mA at 5V).
Compatible with Linux machines, Debian 6.0 or higher, or any derivative (such as Ubuntu 10.0+), but also with Rasberry Pi (213 Mode B/B+).
NVIDIA’s new Turing chip architecture delivers up to six times the performance of previous generation GPU’s, with breakthrough technologies and next generation, ultra-fast GDDR6 memory.
Compatible with Linux, CUDA/OpenCL, KVM.
NVIDIA’s previous chip architecture, great for mining, graphics rendering and computing. The NVIDIA Pascal architecture delivers excellent performance at a budget friendly price.
Compatible with Linux, CUDA/OpenCL, KVM.
A optimal chip for machine learning and video transcoding, can be found in the NVIDIA Tesla P4 and P100 GPU’s. NVIDIA’s Pascal chip architecture has been proven to be faster and more power efficient than its Maxwell predecessor.
Transcode up to 20 simultaneous video streams with a single Tesla P4 paired with our HP BL460c blade server. *
A more powerful version of the Tesla P4 is the Tesla P40, with more than twice the processing power of the Tesla P4.
The Tesla P100 GPU, is most suitable for deep learning and remote graphics. With 18.7 TeraFLOPS of inference performance, a single Tesla P100 can replace over 25 CPU servers.
Compatible: VMWare ESXi, Citrix Xenserver, KVM, Linux, Windows.
*Results may vary, based on server configuration and video resolution of each stream.
The first GPU to break the 100 teraflop barrier of deep learning performance. NVIDIA’s Volta chip, is up to 3x faster than it’s Pascal chip predecessor.
Your deep learning project design can now be a reality, with little investment. Get the maximum per machine deep learning performance, replacing up to 30 single CPU servers with just one Titan V configuration.
Use the Titan V for high performance computing, from predicting the weather to discovering or finding new energy sources. Get your results up to 1.5x faster than NVIDIA’s Pascal predecessor.
Compatible: VMWare ESXi, Citrix Xenserver, KVM, Linux, Windows.
Your startup or small business needs a dedicated server that can help train your machine learning model efficiently. Your Turo or Tesla based GPU server can run several deep learning models, with various frameworks and still get amazing training and inference time. Your services are backed by our industry leading 99.9% uptime SLA and supported by a team of machine learning experts, around the clock.
