GPU Dedicated Server for PyTorch Deep Learning

The PyTorch framework has been gaining popularity in recent years. Google Trends data confirms that interest in PyTorch is growing rapidly, and it has overtaken TensorFlow & Keras. We provide bare metal servers with GPU that are specifically designed for deep learning with PyTorch.

Plans & Prices of GPU Servers for PyTorch

We offer cost-effective and optimized NVIDIA GPU rental servers for PyTorch.

Basic GPU - RTX 4060

149.00/m
1m3m12m24m
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  • 64GB RAM
  • Eight-Core E5-2690report
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: Nvidia GeForece RTX 4060
  • Microarchitecture: Ada Lovelace
  • Max GPUs: 2report
  • CUDA Cores: 3072
  • Tensor Cores: 96
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 15.11 TFLOPSreport

Advanced GPU - V100

229.00/m
1m3m12m24m
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  • 128GB RAM
  • Dual 12-Core E5-2690v3report
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: Nvidia V100
  • Microarchitecture: Volta
  • Max GPUs: 1
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPSreport

Advanced GPU - A4000

209.00/m
1m3m12m24m
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  • 128GB RAM
  • Dual 12-Core E5-2697v2report
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: Nvidia Quadro RTX A4000
  • Microarchitecture: Ampere
  • Max GPUs: 2report
  • CUDA Cores: 6144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • FP32 Performance: 19.2 TFLOPSreport

Advanced GPU - A5000

269.00/m
1m3m12m24m
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  • 128GB RAM
  • Dual 12-Core E5-2697v2report
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: Nvidia Quadro RTX A5000
  • Microarchitecture: Ampere
  • Max GPUs: 2report
  • CUDA Cores: 8192
  • Tensor Cores: 256
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPSreport

Enterprise GPU - RTX 4090

409.00/m
1m3m12m24m
  • 256GB RAM
  • Dual 18-Core E5-2697v4report
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: GeForce RTX 4090
  • Microarchitecture: Ada Lovelace
  • Max GPUs: 1
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24 GB GDDR6X
  • FP32 Performance: 82.6 TFLOPSreport

Enterprise GPU - RTX A6000

409.00/m
1m3m12m24m
Order Now
  • 256GB RAM
  • Dual 18-Core E5-2697v4report
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: Nvidia Quadro RTX A6000
  • Microarchitecture: Ampere
  • Max GPUs: 1
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPSreport
New Arrival

Multi-GPU - 3xV100

469.00/m
1m3m12m24m
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  • 256GB RAM
  • Dual 18-Core E5-2697v4report
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: 3 x Nvidia V100
  • Microarchitecture: Volta
  • Max GPUs: 3report
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPSreport
New Arrival

Multi-GPU - 3xRTX A6000

899.00/m
1m3m12m24m
Order Now
  • 256GB RAM
  • Dual 18-Core E5-2697v4report
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: 3 x Quadro RTX A6000
  • Microarchitecture: Ampere
  • Max GPUs: 3report
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPSreport
More GPU Hosting Plansarrow_circle_right

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

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

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

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.
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?

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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?

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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?

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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?

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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?

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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?

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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?

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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!

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

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