Intel Xeon CPU

Intel Xeon has extraordinary processing power and speed, which is very suitable for running deep learning frameworks. So you can totally use our Intel-Xeon-powered GPU servers for deep learning and AI.

SSD-Based Drives

You can never go wrong with our own top-notch dedicated GPU servers for PyTorch, loaded with the latest Intel Xeon processors, terabytes of SSD disk space, and 128 GB of RAM per server.

With full root/admin access, you will be able to take full control of your dedicated GPU servers for deep learning very easily and quickly.

With enterprise-class data centers and infrastructure, we provide a 99.9% uptime guarantee for hosted GPUs for deep learning and networks.

One of the premium features is the dedicated IP address. Even the cheapest GPU dedicated hosting plan is fully packed with dedicated IPv4 & IPv6 Internet protocols.

Resources among different users are fully isolated to ensure your data security. DBM protects against DDoS from the edge fast while ensuring legitimate traffic of hosted GPUs for deep learning is not compromised.

The higher the floating-point computing capability of the graphics card, the higher the arithmetic power that deep learning, and scientific computing use.

Large memory can reduce the number of times to read data and reduce latency.

GPU memory bandwidth is a measure of the data transfer speed between a GPU and the system across a bus, such as PCI Express (PCIe) or Thunderbolt. It's important to consider the bandwidth of each GPU in a system when developing your high-performance Metal apps.

RT Cores are accelerator units that are dedicated to performing ray tracing operations with extraordinary efficiency. Combined with NVIDIA RTX software, RT Cores enable artists to use ray-traced rendering to create photorealistic objects and environments with physically accurate lighting.

Tensor Cores enable mixed-precision computing, dynamically adapting calculations to accelerate throughput while preserving accuracy.

We offer many cost-effective GPU server plans on the market, so you can easily find a plan that fits your business needs and is within your budget.

The Jupyter Notebook is a web-based interactive computing platform. It allows users to compile all aspects of a data project in one place making it easier to show the entire process of a project to your intended audience.

PyTorch is a machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing. It provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration, Deep neural networks built on a tape-based autograd system.

Keras is a high-level, deep-learning API developed by Google for implementing neural networks. It is written in Python and is used to implement neural networks easily. It also supports multiple backend neural network computations.

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is written in C++, with a Python interface.

Theano is a Python library that allows us to evaluate mathematical operations including multidimensional arrays so efficiently. It is mostly used in building Deep Learning Projects.