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.
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.
Choose Your TensorFlow Hosting Plans
We offer TensorFlow hosting rental plans with multiple GPU options, such as GT 730, P620, A2, A10, K40, and A5000.
October Sale
Lite GPU-GT710
$ 24.50/m
50% OFF (Was $49.00)
1M3M12M24M
Order Now- 16GB RAM
- Quad-Core Xeon X3440report
- 120GB + 960GB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia GeForce GT 710
- Microarchitecture: Kepler
- Max GPUs: 1report
- CUDA Cores: 192
- GPU Memory: 1GB DDR3
- $24.50 first month if monthly payment, then $49.00/m. For new client only! Limit one per client.
October Sale!
Lite GPU-GT730
$ 34.30/m
30% OFF (Was $49.00)
1M3M12M24M
Order Now- 16GB RAM
- Quad-Core Xeon E3-1230report
- 120GB + 960GB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia GeForce GT 730
- Microarchitecture: Kepler
- Max GPUs: 1report
- CUDA Cores: 384
- GPU Memory: 2GB DDR3
- Discounts apply to the Quarterly billing and above. For new orders only! Limite one per client.
October Sale
Express GPU-P600
$ 29.50/m
50% OFF (Was $59.00)
1M3M12M24M
Order Now- 32GB RAM
- Quad-Core Xeon E5-2643report
- 120GB + 960GB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia Quadro P600
- Microarchitecture: Pascal
- Max GPUs: 1report
- CUDA Cores: 384
- GPU Memory: 2GB GDDR5
- FP32 Performance: 1.2 TFLOPSreport
- $29.50 first month if monthly payment, then $59.00/m. For new client only! Limit one per client.
October Sale
Express GPU-P620
$ 41.30/m
30% OFF (Was $59.00)
1M3M12M24M
Order Now- 32GB RAM
- Eight-Core Xeon E5-2670report
- 120GB + 960GB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia Quadro P620
- Microarchitecture: Pascal
- Max GPUs: 1report
- CUDA Cores: 512
- GPU Memory: 2GB GDDR5
- FP32 Performance: 1.5 TFLOPSreport
- Discounts apply to the Quarterly billing and above. For new orders only! Limit one per client.
October Sale
Express GPU-P1000
$ 51.20/m
20% OFF (Was $64.00)
1M3M12M24M
Order Now- 32GB RAM
- Eight-Core Xeon E5-2690report
- 120GB + 960GB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia Quadro P1000
- Microarchitecture: Pascal
- Max GPUs: 1report
- CUDA Cores: 640
- GPU Memory: 4GB GDDR5
- FP32 Performance: 1.894 TFLOPSreport
- For new orders only! Limit one per client.
Basic GPU-T1000
$ 99.00/m
1M3M12M24M
Order Now- 64GB RAM
- Eight-Core Xeon E5-2690report
- 120GB + 960GB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia Quadro T1000
- Microarchitecture: Turing
- Max GPUs: 1report
- CUDA Cores: 896
- GPU Memory: 8GB GDDR6
- FP32 Performance: 2.5 TFLOPSreport
October Sale
Basic GPU-K40
$ 64.50/m
50% OFF (Was $129.00)
1M3M12M24M
Order Now- 64GB RAM
- Eight-Core Xeon E5-2670report
- 120GB + 960GB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia Tesla K40report
- Microarchitecture: Kepler
- Max GPUs: 2report
- CUDA Cores: 2880
- GPU Memory: 12GB GDDR5
- FP32 Performance: 4.29 TFLOPSreport
- $64.50 first month if monthly payment, then $129.00/m. For new client only!
October Sale
Professional GPU-K80
$ 111.30/m
30% OFF (Was $159.00)
1M3M12M24M
Order Now- 128GB RAM
- Dual 10-Core E5-2660v2report
- 120GB + 960GB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia Tesla K80report
- Microarchitecture: Turing
- Max GPUs: 2report
- CUDA Cores: 4992
- GPU Memory: 24GB GDDR5
- FP32 Performance: 8.73 TFLOPSreport
- For new orders only! Limit one per client.
October Sale
Advanced GPU-A4000
$ 146.30/m
30% OFF (Was $209.00)
1M3M12M24M
Order Now- 128GB RAM
- Dual 12-Core E5-2697v2report
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia RTX A4000report
- Microarchitecture: Ampere
- Max GPUs: 2report
- CUDA Cores: 6144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPSreport
- For new orders only! Limit one per client.
Advanced GPU-A5000
$ 269.00/m
1M3M12M24M
Order Now- 128GB RAM
- Dual 12-Core E5-2697v2report
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia RTX A5000report
- Microarchitecture: Ampere
- Max GPUs: 2report
- CUDA Cores: 8192
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPSreport
New Arrival
Enterprise GPU-A40
$ 369.00/m
1M3M12M24M
Order Now- 256GB RAM
- Dual E5-2697v4report
- 240GB SSD + 2TB SSD + 2TB NVMe
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia A40report
- Microarchitecture: Ampere
- Max GPUs: 1
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 37.48 TFLOPSreport
New Arrival
Enterprise GPU-V100
$ 369.00/m
1M3M12M24M
Order Now- 256GB RAM
- Dual E5-2697v4report
- 240GB SSD + 2TB SSD + 2TB NVMe
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia V100report
- Microarchitecture: Volta
- Max GPUs: 1
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPSreport
More GPU Hosting Plansarrow_circle_right
Benefits of TensorFlow
With its capabilities, TensorFlow eases the computations of machine learning and deep learning.
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) | done | RDP for Windows server and SSH for Linux Server |
| Control Panel | Free | Free install SolidCP for Windows or ISPConfig for Linux |
| Administrator Permission | done | You have full control of your dedicated server. |
| 24/7/365 Support | done | We offer 24/7 tech support via Ticket and Livechat |
| Server 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 | done |
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 8TB SATA: $39.00/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 or IPv6 | ARIN Justification Required |
| Shared Hardware Firewall | $29.00/month | Cisco ASA 5520. Limited-time 40% OFF regular price $49/m. |
| Dedicated Hardware Firewall | $99.00/month | Cisco ASA 5520 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 GTX 2060: $99.00/month Nvidia GTX 3060 Ti: $159.00/month Nvidia RTX A4000: $159.00/month Nvidia RTX A5000: $229.00/month | |
| HDMI Dummy | $15 one time fee |
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.
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.
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.
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.
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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