TensorFlow with GPU - GPU Mart

TensorFlow with GPU

DBM's TensorFlow with GPU server is a dedicated server with a GPU graphics card designed for high performance computing. Get this GPU-accelerated TensorFlow hosting for deep learning, voice/sound recognition, image recognition, video detection, etc.

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Dedicated GPU Servers
Starting at $45.00/mo

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

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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

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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

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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

Order Now

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

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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

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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

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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

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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

Out of Stock

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

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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

Out of Stock

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

Order Now

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

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

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

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

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

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

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

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 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 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

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

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

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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