TensorFlow with GPU - GPU Mart

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Choose Your TensorFlow Hosting Plans

We offer TensorFlow Hosting plans with multiple GPU options, such as GT 730, P620, A2, A10, K40, and A5000.
Lite GPU Server
Nvidia GeForce GT 710

Mainly suitable for home games and Android emulators, such as BlueStacks and MEmu Play.

Starting at

$45.00

/month

  • 16GB RAM
  • Quad-Core Xeon X3440
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Windows & Linux
  • GPU:Nvidia GeForce GT 710
  • Max GPU: 1
  • CUDA Cores: 192
  • GPU Memory: 1GB
  • Monthly: $49.00/m
  • Semi-Annually: $48.00/m
  • Annually: $47.00/m
  • Biennially: $45.00/m
Lite GPU Server
Nvidia GeForce GT 730

Mainly suitable for home games and Android emulators, such as BlueStacks and MEmu Play.

Starting at

$49.00

/month

  • 16GB RAM
  • Quad-Core Xeon E3-1230
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Windows & Linux
  • GPU:Nvidia GeForce GT 730
  • Max GPU: 1
  • CUDA Cores: 384
  • GPU Memory: 2GB
  • Monthly: $55.00/m
  • Semi-Annually: $54.00/m
  • Annually: $53.00/m
  • Biennially: $49.00/m
Express GPU Server
Nvidia Quadro P600

Good choice for Android Emulators & gaming, video editing & rendering, and drawing workstations.

Starting at

$52.00

/month

  • 32 GB RAM
  • Quad-Core Xeon E5-2643
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Windows & Linux
  • GPU: Nvidia Quadro P600
  • Max GPU: 1
  • CUDA Cores: 384
  • GPU Memory: 2GB
  • Performance: 1.2 TFLOPS
  • Monthly: $59.00/m
  • Semi-Annually: $58.00/m
  • Annually: $56.00/m
  • Biennially: $52.00/m
Express GPU Server
Nvidia Quadro P620

Good choice for Android Emulators & gaming, video editing & rendering, and drawing workstations.

Starting at

$59.00

/month

  • 32 GB RAM
  • Eight-Core Xeon E5-2670
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Windows & Linux
  • GPU:Nvidia Quadro P620
  • Max GPU: 1
  • CUDA Cores: 512
  • GPU Memory: 2GB
  • Performance: 1.5 TFLOPS
  • Monthly: $69.00/m
  • Semi-Annually: $67.00/m
  • Annually: $64.00/m
  • Biennially: $59.00/m
Express GPU Server
Nvidia Quadro P1000

Good choice for Android Emulators & gaming, video editing & rendering, and drawing workstations.

Starting at

$64.00

/month

  • 32 GB RAM
  • Eight-Core Xeon E5-2690
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Windows & Linux
  • GPU:Nvidia Quadro P1000
  • Max GPU: 1
  • CUDA Cores: 640
  • GPU Memory: 4GB
  • Performance: 1.894 TFLOPS
  • Monthly: $74.00/m
  • Semi-Annually: $72.00/m
  • Annually: $69.00/m
  • Biennially: $64.00/m
Basic GPU Server
Nvidia Quadro T1000

Good choice for Android Emulators & gaming, video editing & rendering, 3D modeling & drawing workstations.

Starting at

$99.00

/month

  • 64 GB RAM
  • Eight-Core Xeon E5-2690
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Windows & Linux
  • GPU:Nvidia Quadro T1000
  • Max GPU: 1
  • CUDA Cores: 896
  • GPU Memory: 8GB
  • Performance: 2.5 TFLOPS
  • Monthly: $119.00/m
  • Semi-Annually: $114.00/m
  • Annually: $109.00/m
  • Biennially: $99.00/m
Basic GPU Server
Nvidia Tesla K40

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

Starting at

$109.00

/month

  • 64 GB RAM
  • Eight-Core Xeon E5-2670
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS: Windows & Linux
  • GPU:Nvidia Tesla K40
  • Max GPU: 2
  • CUDA Cores: 2880
  • GPU Memory: 12GB
  • Performance: 4.29 TFLOPS
  • Monthly: $129.00/m
  • Semi-Annually: $124.00/m
  • Annually: $119.00/m
  • Biennially: $109.00/m
Professional GPU Server
Nvidia Tesla K80

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

Starting at

$159.00

/month

  • 128 GB RAM
  • Dual 10-Core E5-2660v2
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS : Linux & Windows 10
  • GPU:Nvidia Tesla K80
  • Max GPU: 2
  • CUDA Cores: 4992
  • GPU Memory: 24GB
  • Performance: 8.73 TFLOPS
  • Monthly: $199.00/m
  • Semi-Annually: $189.00/m
  • Annually: $179.00/m
  • Biennially: $159.00/m
Advanced GPU Server
Nvidia RTX A4000

For professionals. It delivers real-time ray tracing, AI accelerated computing, and high-performance graphics to desktops.

Starting at

$209.00

/month

  • 128 GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS : Linux & Windows 10
  • GPU:Nvidia RTX A4000
  • Max GPU: 2
  • CUDA Cores: 6144
  • Tensor Cores: 192
  • GPU Memory: 16GB
  • Performance: 19.2 TFLOPS
  • Monthly: $279.00/m
  • Semi-Annually: $259.00/m
  • Annually: $239.00/m
  • Biennially: $209.00/m
Advanced GPU Server
Nvidia RTX A5000

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

Starting at

$269.00

/month

  • 128GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps Bandwidth
  • Supported OS : Linux & Windows 10
  • GPU:Nvidia RTX A5000
  • Max GPU: 2
  • CUDA Cores: 8192
  • GPU Memory: 24GB
  • Performance: 27.8 TFLOPS
  • Monthly: $349.00/m
  • Semi-Annually: $329.00/m
  • Annually: $309.00/m
  • Biennially: $269.00/m

Benefits of TensorFlow

Benefits of TensorFlow

With its capabilities, TensorFlow eases the computations of machine learning and deep learning.

Data visualization

Data visualization

TensorFlow has great computational graph visualizations. It also allows easy debugging of nodes with the help of TensorBoard. This reduces the effort of visiting the whole code and effectively resolves the neural network.

Keras friendly

Keras friendly

TensorFlow has compatibility with Keras. Its users can code some high-level functionality sections in it. Keras provides system-specific functionality to TensorFlow, such as pipelining, estimators, and eager execution.

Scalable

Scalable

With its characteristic of being deployed on every machine and the graphical representation of a model, TensorFlow allows its users to develop any kind of system using TensorFlow.

Compatibility

Compatibility

It is compatible with many languages, including C++, JavaScript, Python, C#, Ruby, and Swift. The language compatibility allows users to work in environments they are comfortable.

Parallelism

Parallelism

Due to the parallelism of work models, TensorFlow find its use as a hardware acceleration library. It uses different distribution strategies in GPU and CPU systems.

Graphical support

Graphical support

Deep learning uses TensorFlow for its development as it allows the building of neural networks with the help of graphs that represent operations as nodes.

Features of TensorFlow with GPU Servers

Add additional resources or services to your GPU-accelerated TensorFlow servers to ensure a high level of server performance.

Support and Management Features for GPU Server
Remote Access (RDP/SSH) RDP for Windows server and SSH for Linux Server
Control Panel Free Free install SolidCP for Windows or ISPConfig for Linux
Administrator Permission You have full control of your dedicated server.
24x7x365 Support We offer 24/7 tech support via Ticket and Livechat
Server Manual Reboot Free --
Hardware Replacement Free --
Operating System Re-Installation Free Maximum twice a month and $25.00 each time for additional reloads
Software Features for GPU Server
Operating System Optional Free CentOS, Ubuntu, Fedora, OpenSUSE, VMWare.
Microsoft Windows Server 2019/2016/2022/2012 R2 Standard Edition x64:$20/m
Microsoft Windows Server 10 Pro: 90-day free trial. Please purchase a Win10 Pro license by yourself after the trial period.
Free Shared DNS Service --
Optional Add-ons for GPU Server
Additional Memory $40.00/month/32GB --
Additional SATA Drives 2TB SATA: $19.00/month
4TB SATA: $29.00/month
--
Additional SSD Drives 240GB SSD: $9.00/month
960GB SSD: $19.00/month
2TB SSD: $29.00/month
4TB SSD: $49.00/month
--
Additional Dedicated IP $2.00/month/IPv4 ARIN Justification Required
Shared Hardware Firewall $49.00/month Cisco ASA 5505
Dedicated Hardware Firewall $99.00/month Cisco ASA5505 with superuser access
Remote Data Center Backup (twice per week) 40GB Disk Space: $30.00/month
80GB Disk Space: $60.00/month
120GB Disk Space: $90.00/month
160GB Disk Space: $120.00/month
We will use Backup For Workgroups to backup your server data (C: partition only) to our remote data center servers twice per week. You can restore the backup files in your server at any time by yourself.
Bandwidth Upgrade Upgrade to 200Mbps: $10.00/month
Upgrade to 1Gbps: $20.00/month
--
Additional GPU Cards Nvidia Tesla K40: $49.00/month
Nvidia Tesla K80: $99.00/month
Nvidia RTX A4000: $159.00/month
Nvidia RTX A5000: $229.00/month
--

TensorFlow Hosting Use Cases

Main Use Cases of Deep Learning Using TensorFlow with GPU servers
Voice/Sound Recognition

Voice/Sound Recognition

Voice and Sound recognition applications are the most well-known use cases of deep learning. If the neural networks have the proper input data feed, neural networks are capable of understanding audio signals.
Text-Based Applications

Text-Based Applications

Text-based applications are popular use cases of deep learning. Common text-based applications include sentiment analysis (for CRM and social media), threat detection (for social media and government), and fraud detection (insurance and finance). Furthermore, language detection and text summarization are the other most popular uses of text-based applications. Our TensorFlow with GPU servers can run these applications well.
Image Recognition

Image Recognition

Social Media, Telecom, and Handset Manufacturers mostly use image recognition. Image recognition is used for: face recognition, image search, motion detection, machine vision, and photo clustering. It also finds its use in the automotive, aviation, and healthcare industries. For example, businesses use image recognition to recognize and identify people and objects in images. By using the TensorFlow with GPU servers, users can implement deep neural networks for use in those image recognition tasks.
Time Series

Time Series

Deep learning uses time-series algorithms for analyzing data to extract meaningful statistics. For example, it can use time series to predict the stock market. So, deep learning is used to forecast non-specific periods in addition to generating alternative versions of the time series.
Deep-learning time series is used in finance, accounting, government, security, and the Internet of Things with risk detections, predictive analysis, and enterprise/resource Planning. All these use cases could rely on the high-performance computing in the TensorFlow with GPU server.
Video Detection

Video Detection

Clients also opt for the TensorFlow with GPU server for video detection, such as in motion detection, real-time threat detection in gaming, security, airports, and user experience/ user interface (UX/UI) fields. Some researchers are working on large-scale video classification datasets, such as YouTube, to accelerate research on large-scale video understanding, representation learning, noisy data modeling, transfer learning, and domain adaptation approaches for video.

FAQs of TensorFlow with GPU

Answers to common questions about GPU-Accelerated TensorFlow server hosting.

What is TensorFlow?

TensorFlow is an open-source library developed by Google primarily for deep learning applications. It also supports traditional machine learning. TensorFlow was originally developed for large numerical computations without keeping deep learning in mind.
It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and lets developers easily build and deploy ML-powered applications.

Why TensorFlow?

TensorFlow is an end-to-end platform that makes it easy for users to build and deploy ML models.
1. Easy model building: Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging.
2. Robust ML production anywhere: Easily train and deploy models in the cloud, on-prem, in the browser, or on-device, no matter what language you use.
3. Powerful experimentation for research: TensorFlow is a simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication fast.

What's ML(Machine learning)?

Machine learning is the practice of helping software perform a task without explicit programming or rules. With traditional computer programming, a programmer specifies the rules that a computer should use. ML requires a different mindset, though. Real-world ML focuses far more on data analysis than coding. Programmers provide a set of examples, and the computer learns patterns from the data. You can think of machine learning as "programming with data."

What's CUDA Toolkit?

The CUDA Toolkit from NVIDIA provides everything you need to develop GPU-accelerated applications. The CUDA Toolkit includes GPU-accelerated libraries, a compiler, development tools, and the CUDA runtime.

What's NVIDIA cuDNN?

The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers.
Deep learning researchers and framework developers worldwide rely on cuDNN for high-performance GPU acceleration. It allows them to focus on training neural networks and developing software applications rather than spending time on low-level GPU performance tuning. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PaddlePaddle, PyTorch, and TensorFlow.

Guidance

Learn how to install TensorFlow on our GPU servers

Whether you're an expert or a beginner, TensorFlow is an end-to-end platform that makes it easy for you to build and deploy ML models. TensorFlow GPU support requires a set of drivers and libraries, including a graphics driver, CUDA toolkit, and cuDNN. This guide will show you how to install these libraries and dependencies for starting a GPU-Accelerated TensorFlow step by step.

Learn More
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