PyTorch GPU, Accelerate Your Deep Learning

PyTorch, a widely-used deep learning framework, leverages CUDA support to fully utilize the powerful performance of NVIDIA GPUs. We provide best gpu servers that are specifically designed for installing PyTorch with CUDA.

PyTorch GPU Plans & Pricing

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

Basic GPU - RTX 4060

149.00/mo
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  • 64GB RAM
  • Eight-Core E5-2690
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia GeForece RTX 4060
  • Microarchitecture: Ada Lovelace
  • Max GPUs: 2
  • CUDA Cores: 3072
  • Tensor Cores: 96
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 15.11 TFLOPS
  • Powerful for Gaming, Streaming, Video Editing, Emulators, Rendering.

    Cost-Effective Choice for AI, Deep Learning.
Summer Sale

Professional GPU - P100

111.3/mo
Save 44% (Was $199.00)
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  • 128GB RAM
  • Dual 10-Core E5-2660v2
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Tesla P100
  • Microarchitecture: Pascal
  • Max GPUs: 2
  • CUDA Cores: 3584
  • Tensor Cores: 640
  • GPU Memory: 16 GB HBM2
  • FP32 Performance: 9.5 TFLOPS
  • Suitable for AI, Data Modeling, High Performance Computing, etc.

Advanced GPU - V100

229.00/mo
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  • 128GB RAM
  • Dual 12-Core E5-2690v3
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • 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 TFLOPS
Summer Sale

Advanced GPU - A4000

167.2/mo
Save 40% (Was $279.00)
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  • 128GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Quadro RTX A4000
  • Microarchitecture: Ampere
  • Max GPUs: 2
  • CUDA Cores: 6144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • FP32 Performance: 19.2 TFLOPS
  • Good Choice for Android Emulators, 3D Rendering, Video Editing, AI/Deep Learning, Data Science, etc

Advanced GPU - A5000

269.00/mo
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  • 128GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Quadro RTX A5000
  • Microarchitecture: Ampere
  • Max GPUs: 2
  • CUDA Cores: 8192
  • Tensor Cores: 256
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPS
  • High-Powered for Android Emulators, 3D Rendering, Video Editing, AI/Deep Learning, etc.

Enterprise GPU - RTX 4090

409.00/mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • 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 TFLOPS

Enterprise GPU - RTX A6000

409.00/mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • 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 TFLOPS
New Arrival

Multi-GPU - 3xV100

469.00/mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: 3 x Nvidia V100
  • Microarchitecture: Volta
  • Max GPUs: 3
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS
New Arrival

Enterprise GPU - A100

639.00/mo
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Order Now
  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia A100
  • Microarchitecture: Ampere
  • Max GPUs: 1
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2e
  • FP32 Performance: 19.5 TFLOPS
New Arrival

Multi-GPU - 3xRTX A6000

899.00/mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: 3 x Quadro RTX A6000
  • Microarchitecture: Ampere
  • Max GPUs: 3
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPS
New Arrival

Multi-GPU - 4xA100

1899.00/mo
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  • 512GB RAM
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: 4 x Nvidia A100 without NVLink
  • Microarchitecture: Ampere
  • Max GPUs: 4
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2e
  • FP32 Performance: 19.5 TFLOPS
More GPU Hosting Plansarrow_circle_right

How to Install PyTorch With CUDA

Using PyTorch with CUDA involves installing the correct version of PyTorch that supports CUDA and ensuring your system has the appropriate NVIDIA GPU drivers and CUDA toolkit installed.

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.

Installing CUDA 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 PyTorch GPU Servers

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.

Key Benefits of PyTorch CUDA

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

Accelerated Computations

By leveraging the parallel processing power of GPUs, PyTorch CUDA significantly speeds up the training and inference of deep learning models compared to CPU-based computations.
Effortless Data Parallelism

Effortless Data Parallelism

PyTorch can distribute the computational tasks among multiple CPUs or GPUs. CUDA allows for the efficient use of GPU resources, enabling larger batch sizes and more complex models to be processed simultaneously.
Useful Libraries

Scalability

With PyTorch CUDA, scaling up deep learning tasks across multiple GPUs becomes more manageable, allowing for handling more extensive datasets and more complex models.
Mobile Ready

Flexibility

PyTorch provides an intuitive interface for moving tensors and models between CPU and GPU, enabling developers to seamlessly switch between different computation modes as needed.

Applications of CUDA PyTorch

CUDA PyTorch is increasingly used for training deep learning models. Here are some popular applications of PyTorch with CUDA.
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 about PyTorch GPU

The most commonly asked questions about GPU Servers for PyTorch.

What is PyTorch?

PyTorch is an open-source deep learning framework developed by Facebook's AI Research lab. It is widely used in both academia and industry due to its ease of use, dynamic computation graph, and robust library for tensor computations. PyTorch facilitates building and training neural networks with its extensive support for machine learning and deep learning tasks.

What is CUDA?

CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by NVIDIA. It enables developers to leverage the parallel processing power of NVIDIA GPUs for computationally intensive tasks. CUDA provides the necessary tools and libraries to run complex calculations and algorithms significantly faster than on a CPU alone.

What is PyTorch CUDA?

PyTorch CUDA refers to the integration of CUDA support within the PyTorch framework. This integration allows PyTorch to utilize the powerful parallel processing capabilities of NVIDIA GPUs, enabling faster and more efficient computation for deep learning tasks.

Is PyTorch compatible with CUDA 11.x?

Yes, PyTorch is compatible with CUDA 11.x. The PyTorch development team regularly updates the framework to support the latest CUDA versions, ensuring compatibility with newer GPU architectures and performance improvements.

What is the latest stable version of PyTorch and what CUDA does it support?

As of July 2024, the latest stable version of PyTorch is 2.3.1, which supports CUDA 11.8 and CUDA 12.1. This allows users to benefit from the latest enhancements in GPU performance and features.

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 CUDA Tutorials for Beginners

Start deep learing with CUDA 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!