GPU Dedicated MXNet Server, GPU Deep Learning Server Rental

Plans & Prices of GPU Servers for MXNet

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

Basic MXNet GPU

Nvidia Tesla K40

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

Starting at

$109.00

/month

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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 MXNet GPU

Nvidia Tesla K80

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

Starting at

$159.00

/month

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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 MXNet 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 MXNet 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

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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 MXNet 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

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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 MXNet 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

6 Reasons to Choose Our GPU Servers for MXNet

6 Reasons to Choose our GPU Servers for MXNet

DBM enables powerful MXNet 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.

Advantages of Deep Learning with MXNet

Here are some of the areas in which MXNet compares favorably to existing alternatives.

User-Friendly and Easy-to-Use

MXNet has the NumPy-like programming interface and is integrated with the new, easy-to-use Gluon 2.0 interface. NumPy users can easily adopt MXNet and start deep learning projects.

Hybrid Front-End

Automatic hybridization provides imperative programming with the performance of traditional symbolic programming.

Rich Ecosystem

Lightweight, memory-efficient, and portable to smart devices through native cross-compilation support on ARM, and through ecosystem projects, such as TVM, TensorRT, OpenVINO.

Distributed Training

Scales up to multi-GPUs-and-distributed setting with auto parallelism through ps-lite, Horovod, and BytePS.

Efficiency and Flexibility

Extensible backend that supports full customization, allowing integration with custom accelerator libraries and in-house hardware without the need to maintain a fork.

10+ Language Bindings

Support for Python, Java, C++, R, Scala, Clojure, Go, Javascript, Perl, Julia , etc.

Feature Comparison: MXNet vs Keras vs PyTorch vs TensorFlow

Everyone's situation and needs are different, so it boils down to which features matter the most for your AI projects.

Features	MXNet	Keras	PyTorch	TensorFlow
API Level	High and low	High	Low	High and low
Architecture	Complex, less readable	Simple, concise, readable	Complex, less readable	Not easy to use
Datasets	Large datasets, high performance	Smaller datasets	Large datasets, high performance	Large datasets, high performance
Debugging	Hard to debug pure symbol codes	Simple network, so debugging is not often needed	Good debugging capabilities	Difficult to conduct debugging
Trained Models Included	Yes	Yes	Yes	Yes
Popularity	Fourth most popular	Most popular	Third most popular	Second most popular
Speed	Fastest on ResNet-50, high performance	Slow, low performance	Fastest on Faster-RCNN, high performance	Fastest on VGG-16, high performance
Written In	C++, Python	Python	Lua, LuaJIT, C, CUDA, and C++	C++, CUDA, Python

FAQs of GPUs for MXNet

A list of frequently asked questions about GPU servers for MXNet.

What is MXNet used for?

MXNet is an open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of devices, from cloud infrastructure to mobile devices. It's highly scalable, allowing for fast model training, and supports a flexible programming model and multiple languages. To get started Deep learning with MXNet, you often need GPUs for MXNet.

Who uses MXNet?

MXNet is supported by public cloud providers including Amazon Web Services (AWS) and Microsoft Azure. Amazon has chosen MXNet as its deep learning framework of choice at AWS.

What is the relationship between MXNet and TensorFlow?

Both MXNet and TensorFlow use computation graph abstraction, which was initially used by Theano, then adopted by other packages, such as CGT, Caffe2, and Purine. Currently, TensorFlow adopts an optimized symbolic API. MXNet supports a mixed approach, with a dynamic dependency scheduler to combine symbolic and imperative programming.

What are the best GPUs for MXNet deep learning?

Today, leading vendor NVIDIA offers the best GPUs for MXNet 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 MXNet in 2023.

Feel free to choose the best plan that has the right CPU, resources, and GPUs for MXNet.

How can I run a MXNet model on multiple GPUs?

To do the multiple MXNet GPUs training, you need to initialize a model on all GPUs, split the batches of data into separate splits where each is stored on a different GPU and run the model separately on every split. The synchronization of gradients and parameters between GPUs is done automatically by Apache MXNet.

Is MXNet faster than PyTorch?

In Apache MXNet, you don't need to flatten the 4-D input into 2-D when feeding the data into forward pass. MXNet has the fastest training speed on ResNet-50, and PyTorch is the fastest on Faster-RCNN. Though you need to be cautious with such toy comparisons.

Is MXNet better than TensorFlow?

Compared to TensorFlow, MXNet has a smaller open-source community. Improvements, bug fixes, and other features take longer due to a lack of major community support. Despite being widely used by many organizations in the tech industry, MXNet is not as popular as Tensorflow.

What is the advantage of bare metal GPUs for MXNet?

Bare metal GPU servers for MXNet 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 MXNet are all bare metal servers, so we have best GPU dedicated server for AI.

Get Started Resources

Get what you need to get started with MXNet quickly.

Prerequisites

1. Choose a plan and place an order
2. Ubuntu 16.04 or higher, Windows 10 or higher
3. Install NVIDIA® CUDA® Toolkit & cuDNN
4. Python 3.6 - 3.8 recommended

Step-by-Step Instructions

Go to MXNet's site, read the installation guide.

1. Indicate your preferred configuration to see specific instructions.
Sample:
2. Install MXNet with pip
Make sure your installed CUDA version matches the CUDA version in the pip package.
- Sample:
- pip install --upgrade pip
- pip install mxnet-cu112
3. Verify the installation
- Sample:
- # Python with GPU, use mx.gpu(), to set MXNet context to be GPUs
- import mxnet as mx
- a = mx.nd.ones((2, 3), mx.gpu())
- b = a * 2 + 1
- b.asnumpy()
- # If you don't get an import error, then MXNet is ready for python.

GPU Dedicated Server for MXNet and Deep Learning

Plans & Prices of GPU Servers for MXNet

6 Reasons to Choose Our GPU Servers for MXNet

Advantages of Deep Learning with MXNet

Feature Comparison: MXNet vs Keras vs PyTorch vs TensorFlow

FAQs of GPUs for MXNet

What is MXNet used for?

Who uses MXNet?

What is the relationship between MXNet and TensorFlow?

What are the best GPUs for MXNet deep learning?

How can I run a MXNet model on multiple GPUs?

Is MXNet faster than PyTorch?

Is MXNet better than TensorFlow?

What is the advantage of bare metal GPUs for MXNet?

Get Started Resources

Prerequisites

Step-by-Step Instructions

Apache MXNet Tutorials

Quickstart Videos