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 Unmetered Bandwidth
  • GPU:Nvidia GeForce GT 710
  • 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 Unmetered Bandwidth
  • GPU:Nvidia GeForce GT 730
  • CUDA Cores: 384
  • GPU Memory: 2GB
  • Monthly: $55.00/m
  • Semi-Annually: $54.00/m
  • Annually: $53.00/m
  • Biennially: $49.00/m
Sale Ends Soon
Express GPU Server
Nvidia Quadro P600

Positioning in professional drawing workstations. Good choice for video editing & rendering, content creation, and gaming.

50% Off
Only $ 29.50 first month

then $59.00/m

  • 32 GB RAM
  • Quad-Core Xeon E5-2643
  • 120GB SSD + 960GB SSD
  • 100Mbps Unmetered Bandwidth
  • GPU: Nvidia Quadro P600
  • CUDA Cores: 384
  • GPU Memory: 2GB
  • Performance: 1.2 TFLOPS
  • Monthly: $59.00/m $29.50/m
  • Semi-Annually: $58.00/m
  • Annually: $56.00/m
  • Biennially: $52.00/m
Sale Ends Soon
Express GPU Server
Nvidia Quadro P620

Positioning in professional drawing workstations. Good choice for video editing & rendering, content creation, and gaming.

50% Off
Only $ 34.50 first month

then $69.00/m

  • 32 GB RAM
  • Eight-Core Xeon E5-2670
  • 120GB SSD + 960GB SSD
  • 100Mbps Unmetered Bandwidth
  • GPU:Nvidia Quadro P620
  • CUDA Cores: 512
  • GPU Memory: 2GB
  • Performance: 1.5 TFLOPS
  • Monthly: $69.00/m $34.50/m
  • Semi-Annually: $67.00/m
  • Annually: $64.00/m
  • Biennially: $59.00/m
Sale Ends Soon
Express GPU Server
Nvidia Quadro P1000

Positioning in professional drawing workstations. Good choice for Video editing & rendering, content creation, and gaming.

50% Off
Only $ 37.00 first month

then $74.00/m

  • 32 GB RAM
  • Eight-Core Xeon E5-2690
  • 120GB SSD + 960GB SSD
  • 100Mbps Unmetered Bandwidth
  • GPU:Nvidia Quadro P1000
  • CUDA Cores: 640
  • GPU Memory: 4GB
  • Performance: 1.894 TFLOPS
  • Monthly: $74.00/m $37.00/m
  • Semi-Annually: $72.00/m
  • Annually: $69.00/m
  • Biennially: $64.00/m
Basic GPU Server
Nvidia Quadro T1000

Enable professionals to tackle multi-app workflows, from 3D modeling to video editing.

Starting at

$99.00

/month

  • 64 GB RAM
  • 10-Core Xeon E5-2690v2
  • 120GB SSD + 960GB SSD
  • 100Mbps Unmetered Bandwidth
  • GPU:Nvidia Quadro T1000
  • 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
Sale Ends Soon
Basic GPU Server
Nvidia Tesla K40

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

50% Off
Only $ 64.50 first month

then $129.00/m

  • 64 GB RAM
  • Eight-Core Xeon E5-2670
  • 120GB SSD + 960GB SSD
  • 100Mbps Unmetered Bandwidth
  • GPU:Nvidia Tesla K40
  • CUDA Cores: 2880
  • GPU Memory: 12GB
  • Performance: 4.29 TFLOPS
  • Monthly: $129.00/m $64.50/m
  • Semi-Annually: $124.00/m
  • Annually: $119.00/m
  • Biennially: $109.00/m
Sale Ends Soon
Professional GPU Server
Nvidia Tesla K80

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

50% Off
Only $ 99.50 first month

then $199.00/m

  • 128 GB RAM
  • Dual 10-Core E5-2660v2
  • 120GB SSD + 960GB SSD
  • 100Mbps Unmetered Bandwidth
  • GPU:Nvidia Tesla K80
  • CUDA Cores: 4992
  • GPU Memory: 24GB
  • Performance: 8.73 TFLOPS
  • Monthly: $199.00/m $99.50/m
  • Semi-Annually: $189.00/m
  • Annually: $179.00/m
  • Biennially: $159.00/m
Professional GPU Server
Nvidia A2

Entry-level AI reasoning and edge computing accelerator based on ampere architecture.

Starting at

$199.00

/month

  • 128GB RAM
  • Dual 10-Core E5-2660v2
  • 240GB SSD + 960GB SSD
  • 100Mbps Unmetered Bandwidth
  • GPU:Nvidia A2
  • CUDA Cores: 1280
  • GPU Memory: 16GB
  • Performance: 4.5 TFLOPS
  • Monthly: $249.00/m
  • Semi-Annually: $239.00/m
  • Annually: $219.00/m
  • Biennially: $199.00/m
Sale Ends Soon
Advanced GPU Server
Nvidia RTX A4000

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

50% Off
Only $ 149.50 first month

then $299.00/m

  • 128 GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps Unmetered Bandwidth
  • GPU:Nvidia RTX A4000
  • CUDA Cores: 6144
  • Tensor Cores: 192
  • GPU Memory: 16GB
  • Performance: 19.2 TFLOPS
  • Monthly: $299.00/m $149.50/m
  • Semi-Annually: $281.00/m
  • Annually: $262.00/m
  • Biennially: $225.00/m
Advanced GPU Server
Nvidia A10

High performance, low power consumption. The mainstream choice for enterprises in AI reasoning, training, graphics, and computing workloads.

Starting at

$269.00

/month

  • 128GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps Unmetered Bandwidth
  • GPU:Nvidia A10
  • CUDA Cores: 9216
  • GPU Memory: 24GB
  • Performance: 31.2 TFLOPS
  • Monthly: $349.00/m
  • Semi-Annually: $329.00/m
  • Annually: $309.00/m
  • Biennially: $269.00/m
Sale Ends Soon
Advanced GPU Server
Nvidia RTX A5000

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

50% Off
Only $ 174.50 first month

then $349.00/m

  • 128GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps Unmetered Bandwidth
  • GPU:Nvidia RTX A5000
  • CUDA Cores: 8192
  • GPU Memory: 24GB
  • Performance: 27.8 TFLOPS
  • Monthly: $349.00/m $174.50/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.

Add-ons of TensorFlow with GPU Servers

Add additional resources or services to your GPU-accelerated TensorFlow servers to ensure a high level of server performance.
Memory
$40/month/32GB
SSD Disk Space
$9/240GB, $19/960GB, $29/2TB, $49/4TB
SATA Drives
$19/2TB, $29/4TB
Dedicated IP
$2/month/IPv4
Shared Hardware Firewall
$49/month
Dedicated Hardware Firewall
$99/month
MSSQL Database
$29/month/4 cores
Remote Data Center Backup
$30.00/month/40GB Disk

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.

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