GPU Dedicated Server for PyTorch Deep Learning

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Plans & Prices of GPU Servers for PyTorch

We offer cost-effective and optimized NVIDIA GPU servers for PyTorch.
Basic PyTorch GPU
Nvidia Tesla K40

For high-performance computing and large data workloads, such as deep learning and AI reasoning.

Starting at

$109.00

/month

Order Now
  • 64 GB RAM
  • Eight-Core Xeon E5-2670
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Windows & Linux
  • GPU: Nvidia Tesla K40
  • Microarchitecture: Kepler
  • Max GPU: 2
  • CUDA Cores: 2880
  • GPU Memory: 12GB
  • Performance: 4.29 TFLOPS
Professional PyTorch GPU
Nvidia Tesla K80

For high-performance computing and large data workloads, such as deep learning and AI reasoning.

Starting at

$159.00

/month

Order Now
  • 128 GB RAM
  • Dual 10-Core E5-2660v2
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Linux & Windows
  • GPU: Nvidia Tesla K80
  • Microarchitecture: Kepler
  • Max GPU: 2
  • CUDA Cores: 4992
  • GPU Memory: 24GB
  • Performance: 8.73 TFLOPS
Spring Sale! Save 30%
Advanced PyTorch GPU
Nvidia RTX A4000

RTX A4000 delivers real-time ray tracing, AI accelerated computing, and high-performance graphics to desktops.

30% off
209.00/m
$ 146.30/m
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  • 128 GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Linux & Windows
  • GPU: Nvidia RTX A4000
  • Microarchitecture: Ampere
  • Max GPU: 2
  • CUDA Cores: 6144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • Performance: 19.2 TFLOPS
Advanced PyTorch GPU
Nvidia RTX A5000

RTX A5000 achieves an excellent balance between function, performance, and reliability. Assist designers, engineers, and artists to realize their visions.

Starting at

$269.00

/month

Order Now
  • 128GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Linux & Windows
  • GPU: Nvidia RTX A5000
  • Microarchitecture: Ampere
  • Max GPU: 2
  • CUDA Cores: 8192
  • GPU Memory: 24GB GDDR6
  • Performance: 27.8 TFLOPS
Enterprise PyTorch GPU
Nvidia A40

Accelerate data science and computation-based workloads. A40 is very suitable for AI and deep learning projects.

Starting at

$369.00

/month

Order Now
  • 256 GB RAM
  • Dual E5-2697v4
  • 240GB SSD + 2TB SSD + 2TB NVMe
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Linux & Windows 10
  • GPU: Nvidia A40
  • Microarchitecture: Ampere
  • Max GPU: 1
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB
  • Performance: 37.4 TFLOPS
Enterprise PyTorch GPU
Nvidia V100

V100 server is a cloud product that can accelerate for more than 600 HPC applications and various deep learning frameworks.

Starting at

$369.00

/month

Order Now
  • 256 GB RAM
  • Dual E5-2697v4
  • 240GB SSD + 2TB SSD + 2TB NVMe
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Linux & Windows 10
  • GPU: Nvidia V100
  • Microarchitecture: Volta
  • Max GPU: 1
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB
  • Performance: 14 TFLOPS
More GPU Hosting Plans

Install PyTorch With CUDA - Quick And Easy

Getting started with PyTorch is very easy. The recommended option is to use the Anaconda Python Package Manager.
With Anaconda, it's easy to get and manage Python, Jupyter Notebook, and other commonly used packages, like PyTorch, for scientific computing and data science!

Prerequisites

  • 1. Choose a plan and place an order.
  • 2. Install NVIDIA® CUDA® Toolkit & cuDNN.
  • 3. Python 3.7, 3.8 or 3.9 recommended.

Install PyTorch in 4 Steps

1. Download and install Anaconda (choose the latest Python version).

2. Go to PyTorch's site, specify the appropriate configuration options for your particular environment. Sample:

instruction

3. Run the presented command in the terminal to install PyTorch.

    • Sample:
    • conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

4. Verify the installation

    • import torch
    • # check what version is installed
    • print(torch.__version__)
    • # construct a randomly initialized tensor
    • x = torch.rand(5, 3)
    • print(x)
    • # check if your GPU driver and CUDA is enabled and accessible
    • torch.cuda.is_available()

6 Reasons to Choose Our GPU Servers for PyTorch

6 Reasons to Choose our GPU Servers for PyTorch

DBM enables powerful GPU hosting features on raw bare metal hardware, served on-demand. No more inefficiency, noisy neighbors, or complex pricing calculators.

Intel Xeon CPU

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 PyTorch.

SSD-Based Drives

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.

Full Root/Admin Access

Full Root/Admin Access

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

99.9% Uptime Guarantee

99.9% Uptime Guarantee

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

Dedicated IP

Dedicated IP

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

DDoS Protection

DDoS Protection

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 PyTorch is not compromised.

Advantages of PyTorch

Advantages of PyTorch

PyTorch is one of the most popular deep learning frameworks due to its flexibility and computation power. Here are some of the reasons why developers and researchers learn PyTorch.

Easy to Learn

Easy to Learn

PyTorch is easy to learn for both programmers and non-programmers.

Higher Developer Productivity

Higher Developer Productivity

It has an interface with python and with different powerful APIs and can be implemented in Windows or Linux OS.

Easy to Debug

Easy to Debug

As PyTorch develops a computational graph at runtime, programmers can use Pythons IDE PyCharm for debugging.

Effortless Data Parallelism

Effortless Data Parallelism

It can distribute the computational tasks among multiple CPUs or GPUs.

Useful Libraries

Useful Libraries

It has a large community of developers and researchers who built tools and libraries to extend PyTorch.

Mobile Ready

Mobile Ready

Starting v1.3, PyTorch has added support for deployment on Android and iOS devices.

Applications of PyTorch

PyTorch is increasingly used for training deep learning models. Here are some popular applications of PyTorch.
Computer Vision

Computer Vision

It uses a convolution neural network to develop image classification, object detection, and generative application. Using PyTorch, a programmer can process images and videos to develop a highly accurate and precise computer vision model.

Natural Language Processing

People can use it to develop language translators, language models, and chatbots. It uses architectures like RNN and LSTM to develop natural language and processing models.

Natural Language
Reinforcement Learning

Reinforcement Learning

More uses include Robotics for automation, Business strategy planning, and robot motion control. It uses Deep Q learning architecture to build a model.

FAQs of GPU Servers for PyTorch

The most commonly asked questions about GPU Servers for PyTorch.

What is PyTorch?

PyTorch is a Python-based scientific computing package serving two broad purposes:
· a replacement for NumPy to use the power of GPUs and other accelerators.
· an automatic differentiation library that is useful to implement neural networks.
For these uses, you often need GPUs for PyTorch.

Which is better, PyTorch or TensorFlow?

TensorFlow offers better visualization, which allows developers to debug better and track the training process. PyTorch, however, provides only limited visualization.
PyTorch has long been the preferred deep-learning library for researchers, while TensorFlow is much more widely used in production. PyTorch's ease of use makes it convenient for fast, hacky solutions, and smaller-scale models.

Is PyTorch only for deep learning?

PyTorch is an open-source machine learning library used for developing and training deep learning models based on neural networks. It is primarily developed by Facebook's AI research group.

Should I learn PyTorch or TensorFlow in 2022?

If you're just starting to explore deep learning, you should learn PyTorch first due to its popularity in the research community. However, if you're familiar with machine learning and deep learning and focused on getting a job in the industry as soon as possible, learn TensorFlow first.
Whether you start deep learning with PyTorch or TensorFlow, our dedicated GPU server can meet you needs.

When do I need GPUs for PyTorch?

If you're training a real-life project or doing some academic or industrial research, then for sure you need a GPU for fast computation. We provide multiple GPU server options for you running deep learning with PyTorch.
If you're just learning PyTorch and want to play around with its different functionalities, then PyTorch without GPU is fine and your CPU in enough for that.

What are the best GPUs for PyTorch deep learning?

Today, leading vendor NVIDIA offers the best GPUs for PyTorch deep learning in 2022. The models are the RTX 3090, RTX 3080, RTX 3070, RTX A6000, RTX A5000, RTX A4000, Tesla K80, and Tesla K40. We will offer more suitable GPUs for Pytorch in 2023.
Feel free to choose the best plan that has the right CPU, resources, and GPUs for PyTorch.

What are the advantages of bare metal GPU for PyTorch?

Our bare metal GPU servers for PyTorch will provide you with an improved application and data performance while maintaining high-level security. When there is no virtualization, there is no overhead for a hypervisor, so the performance benefits. Most virtual environments and cloud solutions come with security risks.
DBM GPU Servers for Pytorch are all bare metal servers, so we have best GPU dedicated server for AI.

Quickstart Video - PyTorch Tutorials for Beginners

Start deep learing with PyTorch faster and easier with the help of these beginners tutorials!
PyTorch Tutorial

Deep Learning with PyTorch: A 60-Minute Blitz

This tutorial helps you understand what PyTorch and neural networks are. Upon completing this, you will be able to build and train a simple image classification network.

PyTorch Beginner Series

PyTorch Beginner Series

An introduction to the world of PyTorch. Each video will guide you through the different parts and help get you started today!

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Leave us a note when purchasing, or contact us to apply a trial GPU server. You have enough time to test the performance, network latency, compatibility, multiple instance capacity, etc.

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