Fastest And Best GPU SERVERS PROVIDER
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.
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Express GPU VPS - GT730
Features
- 8GB RAM
- 6 CPU Cores
- 120GB SSD
- 100Mbps Unmetered
- Bandwidth
- Once per 4 Weeks Backup
- OS: Linux / Windows 10
- Dedicated GPU: GeForce GT730
- CUDA Cores: 384
- GPU Memory: 2GB DDR3
- FP32 Performance: 0.692
- TFLOPS
Price: $21.00/m
Express GPU VPS - K620
Features
- 12GB RAM
- 9 CPU Cores
- 160GB SSD
- 100Mbps Unmetered
- Bandwidth
- Once per 4 Weeks Backup
- OS: Linux / Windows 10
- Dedicated GPU: Quadro K620
- CUDA Cores: 384
- GPU Memory: 2GB DDR3
- FP32 Performance: 0.692
- TFLOPS
Price: $21.00/m
Basic GPU VPS - P600
Subheading 2
- 16GB RAM
- 12 CPU Cores
- 200GB SSD
- 200Mbps Unmetered
- Bandwidth
- Once per 4 Weeks Backup
- OS: Linux / Windows 10
- Dedicated GPU: Quadro P600
- CUDA Cores: 384
- GPU Memory: 2GB GDDR5
- FP32 Performance: 1.2 TFLOPS
Price: $29.00/m
Lite GPU - GT710
Features
- 16GB RAM
- Quad-Core Xeon X3440
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia GeForce GT710
- Microarchitecture: Kepler
- Max GPUs: 1
- CUDA Cores: 192
- GPU Memory: 1GB DDR3
- FP32 Performance: 0.336 TFLOPS
Price: $45.00/m
Lite GPU - GT730
Features
- 16GB RAM
- Quad-Core Xeon E3-1230
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia GeForce GT730
- Microarchitecture: Kepler
- Max GPUs: 1
- CUDA Cores: 384
- GPU Memory: 2GB DDR3
- FP32 Performance: 0.692 TFLOPS
Price: $49.00/m
Lite GPU - K620
Features
- 16GB RAM
- Quad-Core Xeon E3-1270v3r
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro K620
- Microarchitecture: Maxwell
- Max GPUs: 1
- CUDA Cores: 384
- GPU Memory: 2GB DDR3
- FP32 Performance: 0.863 TFLOPS
Price: $49.00/m
Express GPU - P600
Features
- 32GB RAM
- Quad-Core Xeon E5-2643
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro P600
- Microarchitecture: Pascal
- Max GPUs: 1
- CUDA Cores: 384
- GPU Memory: 2GB GDDR5
- FP32 Performance: 1.2 TFLOPS
Price: $52.00/m
Express GPU - P620
Features
- 32GB RAM
- Eight-Core Xeon E5-2670
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro P620
- Microarchitecture: Pascal
- Max GPUs: 1
- CUDA Cores: 512
- GPU Memory: 2GB GDDR5
- FP32 Performance: 1.5 TFLOPS
Price: $59.00/m
Express GPU - P1000
Features
- 32GB RAM
- Eight-Core Xeon E5-2690
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro P1000
- Microarchitecture: Pascal
- Max GPUs: 1
- CUDA Cores: 640
- GPU Memory: 4GB GDDR5
- FP32 Performance: 1.894 TFLOPS
Price: $64.00/m
Basic GPU - GTX 1650
Features
- 64GB RAM
- Eight-Core Xeon E5-2667v3
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia GeForce GTX 1650
- Microarchitecture: Turing
- Max GPUs: 1
- CUDA Cores: 896
- GPU Memory: 4GB GDDR5
- FP32 Performance: 3.0 TFLOPSr
Price: $99.00/m
Basic GPU - T1000
features
- 64GB RAM
- Eight-Core Xeon E5-2690
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro T1000
- Microarchitecture: Turing
- Max GPUs: 1
- CUDA Cores: 896
- GPU Memory: 8GB GDDR6
- FP32 Performance: 2.5 TFLOPS
Price: $79.2/m
Basic GPU - K80
Features
- 64GB RAM
- Eight-Core Xeon E5-2690
- 120GB + 960GB SSD
- 1100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Tesla K80
- Microarchitecture: Turing
- Max GPUs: 2
- CUDA Cores: 4992
- GPU Memory: 24GB GDDR5
- FP32 Performance: 8.73 TFLOPS
Price: $129.00/m
Professional GPU VPS - A4000
Features
- 32GB RAM
- 24 CPU Cores
- 320GB SSD
- 300Mbps Unmetered
- Bandwidth
- Once per 4 Weeks Backup
- OS: Linux / Windows 10
- Dedicated GPU: Quadro RTX A4000
- CUDA Cores: 6,144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPS
Price: $129.00/m
Basic GPU - GTX 1660
Features
- 64GB RAM
- Dual 10-Core Xeon E5-2660v2
- 120GB + 960GB SSD
- 100Mbps-1Gbpsr
- OS: Linux / Windows 10
- GPU: Nvidia GeForce GTX 1660
- Microarchitecture: Turing
- Max GPUs: 1
- CUDA Cores: 1408
- GPU Memory: 6GB GDDR6
- FP32 Performance: 5.0 TFLOPS
Price: $139.00/m
Basic GPU - RTX 4060
features
- 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
Price: $149.00/m
Professional GPU - RTX 2060
features
- 128GB RAM
- Dual 10-Core E5-2660v2
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia GeForce RTX 2060
- Microarchitecture: Ampere
- Max GPUs: 2
- CUDA Cores: 1920
- Tensor Cores: 240
- GPU Memory: 6GB GDDR6
- FP32 Performance: 6.5 TFLOPS
Price: $111.3/m
Advanced GPU - RTX 3060 Ti
features
- 128GB RAM
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: GeForce RTX 3060 Ti
- Microarchitecture: Ampere
- Max GPUs: 2
- CUDA Cores: 4864
- Tensor Cores: 152
- GPU Memory: 8GB GDDR6
- FP32 Performance: 16.2 TFLOPS
Price: $179.00/m
Advanced GPU - A4000
features
- 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
Price: $209.00/m
Advanced GPU - V100
features
- 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
Price: $229.00/m
Advanced GPU - A5000
features
- 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
Price: $269.00/m
Enterprise GPU - A40
features
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia A40
- Microarchitecture: Ampere
- Max GPUs: 1
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 37.48 TFLOPS
Price: $439.00/m
Enterprise GPU - RTX 4090
features
- 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
Price: $409.00/m
Enterprise GPU - RTX A6000
features
- 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
Price: $409.00/m
Enterprise GPU - A100
features
- 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
Price: $639.00/m
Multi-GPU - 3xRTX 3060 Ti
Features
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: 3 x GeForce RTX 3060 Ti
- Microarchitecture: Ampere
- Max GPUs: 3
- CUDA Cores: 4864
- Tensor Cores: 152
- GPU Memory: 8GB GDDR6
- FP32 Performance: 16.2 TFLOPS
Price: $369.00/m
Multi-GPU - 3xRTX A5000
features
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: 3 x Quadro RTX A5000
- Microarchitecture: Ampere
- Max GPUs: 3
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS
Price: $539.00/m
Multi-GPU - 3xRTX A6000
features
- 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
Price: $899.00/m
Multi-GPU - 3xV100
features
- 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
Price: $469.00/m
Multi-GPU - 2xRTX 4090
features
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: 2 x GeForce RTX 4090
- Microarchitecture: Ada Lovelace
- Max GPUs: 2
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPS
Price: $639.00/m
Multi-GPU - 8xV100
features
- 512GB RAM
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: 8 x Nvidia Tesla V100
- Microarchitecture: Volta
- Max GPUs: 8
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPS
Price: $1499.00/m
Multi-GPU - 4xA100
features
- 512GB RAM
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: 4 x Nvidia A100 with NVLink
- Microarchitecture: Ampere
- Max GPUs: 4
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2e
- FP32 Performance: 19.5 TFLOPS
Price: $1899.00/m
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
Sample: conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
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
GPUHUT 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 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 MXNet.
SSD-Based Drives
You can never go wrong with our own top-notch GPU dedicated servers, loaded with the latest Intel Xeon processors, terabytes of SSD disk space, and 128 GB of RAM per server.
Full Root/Admin Access
With full root/admin access, you will be able to take full control of your GPU dedicated server very easily and quickly.
99.9% Uptime Guarantee
With enterprise-class data centers and infrastructure, we provide a 99.9% uptime guarantee for hosted GPUs for MXNet and networks.
Dedicated IP
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.
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 MXNet is not compromised.
FAQs of GPU Servers for PyTorch
The most commonly asked questions about GPU Servers for 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.
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.
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.
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.
Yes, a GPU dedicated server can be used for streaming and running Android emulators. By providing improved performance, faster processing, multi-tasking, and customization options, a GPU dedicated server can help you to create a stable and efficient environment for these applications.
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.
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.