GPU Dedicated Server for TensorFlow and Deep Learning

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.

24/7 Free Tech Support
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

GT 710 is suitable for home games and Android emulators, such as BlueStacks and LDPlayer.

Starting at

$45.00

/month

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16GB RAM
Quad-Core Xeon X3440
120GB SSD + 960GB SSD
100Mbps-1Gbps Bandwidth
Supported OS: Windows & Linux

GPU: Nvidia GeForce GT 710
Microarchitecture: Kepler
Max GPU: 1
CUDA Cores: 192
GPU Memory: 1GB

Lite GPU Server

Nvidia GeForce GT 730

GT 730 is suitable for home games and Android emulators, such as BlueStacks and LDPlayer.

Starting at

$49.00

/month

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16GB RAM
Quad-Core Xeon E3-1230
120GB SSD + 960GB SSD
100Mbps-1Gbps Bandwidth
Supported OS: Windows & Linux

GPU: Nvidia GeForce GT 730
Microarchitecture: Fermi
Max GPU: 1
CUDA Cores: 384
GPU Memory: 2GB

Express GPU Server

Nvidia Quadro P600

P600 is a good choice for Android Emulators, gaming, video editing, and drawing workstations.

Starting at

$52.00

/month

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32 GB RAM
Quad-Core Xeon E5-2643
120GB SSD + 960GB SSD
100Mbps-1Gbps Bandwidth
Supported OS: Windows & Linux

GPU: Nvidia Quadro P600
Microarchitecture: Pascal
Max GPU: 1
CUDA Cores: 384
GPU Memory: 2GB GDDR5
Performance: 1.2 TFLOPS

Express GPU Server

Nvidia Quadro P620

P620 is a good choice for Android Emulators, gaming, video editing, OBS streaming, and drawing workstations.

Starting at

$59.00

/month

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32 GB RAM
Eight-Core Xeon E5-2670
120GB SSD + 960GB SSD
100Mbps-1Gbps Bandwidth
Supported OS: Windows & Linux

GPU: Nvidia Quadro P620
Microarchitecture: Pascal
Max GPU: 1
CUDA Cores: 512
GPU Memory: 2GB
Performance: 1.5 TFLOPS

Express GPU Server

Nvidia Quadro P1000

P1000 is a good choice for Android Emulators, gaming, video editing, OBS streaming, and drawing workstations.

Starting at

$64.00

/month

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32GB RAM
Eight-Core Xeon E5-2690
120GB SSD + 960GB SSD
100Mbps-1Gbps Bandwidth
Supported OS: Windows & Linux

GPU: Nvidia Quadro P1000
Microarchitecture: Pascal
Max GPU: 1
CUDA Cores: 640
GPU Memory: 4GB GDDR5
Performance: 1.894 TFLOPS

Basic GPU Server

Nvidia Quadro T1000

T1000 is a good choice for Android Emulators, gaming, video editing, OBS streaming, 3D modeling, drawing workstations.

Starting at

$99.00

/month

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64 GB RAM
Eight-Core Xeon E5-2690
120GB SSD + 960GB SSD
100Mbps-1Gbps Bandwidth
Supported OS: Windows & Linux

GPU: Nvidia Quadro T1000
Microarchitecture: Turing
Max GPU: 1
CUDA Cores: 896
GPU Memory: 8GB GDDR6
Performance: 2.5 TFLOPS

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

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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 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

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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 GPU Server

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 GPU Server

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 GPU Server

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 GPU Server

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

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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 V100
Microarchitecture: Volta
Max GPU: 1
CUDA Cores: 5,120
Tensor Cores: 640
GPU Memory: 16GB
Performance: 14 TFLOPS

More GPU Hosting Plans

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.

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 10 Pro Evaluation: 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: $39.00/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 GTX 1650: $49.00/month Nvidia GTX 1660: $69.00/month Nvidia GTX 2060: $99.00/month Nvidia GTX 3060 Ti: $159.00/month Nvidia RTX A4000: $159.00/month Nvidia RTX A5000: $229.00/month Nvidia RTX A6000: $399.00/month Nvidia Tesla K40: $49.00/month Nvidia Tesla K80: $99.00/month Nvidia V100: $399.00/month Nvidia A40: $399.00/month	--

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.

Learn More

Leave us a note when purchasing, or contact us to apply a trial GPU server. You have enough time to test the performance, network latency, compatibility, multiple instance capacity, etc.

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