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 RTX 3060 Ti, A5000, A6000, and A40.

Basic GPU - RTX 4060

149.00/m
1m3m12m24m
  • 64GB RAM
  • Eight-Core E5-2690report
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: Nvidia GeForece RTX 4060
  • Microarchitecture: Ada Lovelace
  • Max GPUs: 2report
  • CUDA Cores: 3072
  • Tensor Cores: 96
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 15.11 TFLOPSreport
  • Contact us to make a reservation

Advanced GPU - V100

229.00/m
1m3m12m24m
  • 128GB RAM
  • Dual 12-Core E5-2690v3report
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: Nvidia V100
  • Microarchitecture: Volta
  • Max GPUs: 1
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPSreport

Advanced GPU - A4000

209.00/m
1m3m12m24m
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  • 128GB RAM
  • Dual 12-Core E5-2697v2report
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: Nvidia Quadro RTX A4000
  • Microarchitecture: Ampere
  • Max GPUs: 2report
  • CUDA Cores: 6144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • FP32 Performance: 19.2 TFLOPSreport

Advanced GPU - A5000

269.00/m
1m3m12m24m
  • 128GB RAM
  • Dual 12-Core E5-2697v2report
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: Nvidia Quadro RTX A5000
  • Microarchitecture: Ampere
  • Max GPUs: 2report
  • CUDA Cores: 8192
  • Tensor Cores: 256
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPSreport
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Multi-GPU - 3xRTX 3060 Ti

369.00/m
1m3m12m24m
  • 256GB RAM
  • Dual 18-Core E5-2697v4report
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: 3 x GeForce RTX 3060 Ti
  • Microarchitecture: Ampere
  • Max GPUs: 3report
  • CUDA Cores: 4864
  • Tensor Cores: 152
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 16.2 TFLOPSreport

Enterprise GPU - RTX 4090

409.00/m
1m3m12m24m
  • 256GB RAM
  • Dual 18-Core E5-2697v4report
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: GeForce RTX 4090
  • Microarchitecture: Ada Lovelace
  • Max GPUs: 1
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24 GB GDDR6X
  • FP32 Performance: 82.6 TFLOPSreport
  • Contact us to make a reservation
New Arrival

Multi-GPU - 3xV100

469.00/m
1m3m12m24m
  • 256GB RAM
  • Dual 18-Core E5-2697v4report
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: 3 x Nvidia V100
  • Microarchitecture: Volta
  • Max GPUs: 3report
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPSreport
New Arrival

Multi-GPU - 3xRTX A6000

899.00/m
1m3m12m24m
  • 256GB RAM
  • Dual 18-Core E5-2697v4report
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: 3 x Quadro RTX A6000
  • Microarchitecture: Ampere
  • Max GPUs: 3report
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPSreport
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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)doneRDP for Windows server and SSH for Linux Server
Control PanelFreeFree control panel for management of servers, orders, tickets, invoices, etc.
Administrator PermissiondoneYou have full control of your dedicated server.
24/7/365 SupportdoneWe offer 24/7 tech support via Ticket and Livechat
Server RebootFree
Hardware ReplacementFree
Operating System Re-InstallationFreeMaximum twice a month and $25.00 each time for additional reloads.
Software Features for GPU Server
Operating SystemOptionalFree CentOS, Ubuntu, Fedora, OpenSUSE, Almalinux, VMWare.
Microsoft Windows Server 2016/2019/2022 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 Servicedone
Optional Add-ons for GPU Server
Additional Memory16GB: $10.00/month
32GB: $18.00/month
64GB: $32.00/month
128GB: $56.00/month
256GB: $96.00/month
Additional SATA Drives2TB SATA: $19.00/month
4TB SATA: $29.00/month
8TB SATA: $39.00/month
16TB SATA (3.5’ Only): $49.00/month
Additional SSD Drives240GB 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 or IPv6IP purpose required. Maximum 16 per package.
Shared Hardware Firewall$29.00/monthShared firewall is used by 2-7 users who share a single Cisco ASA 5520 firewall, including shared bandwidth. It does not have superuser privileges.
Dedicated Hardware Firewall$99.00/monthDedicated firewall allocates one user to a Cisco ASA 5520/5525 firewall, providing superuser access for independent and personalized configurations, such as firewall rules and VPN settings.
Remote Data Center Backup(Windows Only)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 UpgradeUpgrade to 200Mbps(Shared): $10.00/month
Upgrade to 1Gbps(Shared): $20.00/month
The bandwidth of your server represents the maximum available bandwidth. Real-time bandwidth usage depends on the current situation in the rack where your server is located and the shared bandwidth with other servers. The speed you experience may also be influenced by your local network and geographical distance from the server.
Additional GPU CardsNvidia Tesla K80: $99.00/month
Nvidia RTX 2060: $99.00/month
Nvidia RTX 3060 Ti: $149.00/month
Nvidia RTX 4060: $149.00/month
Nvidia RTX A4000: $159.00/month
Nvidia RTX A5000: $229.00/month
HDMI Dummy$15 setup fee per serverA one-time setup fee is charged for each server and cannot be transferred to other servers.

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?

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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?

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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)?

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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?

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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?

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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.
tensorflow guidance
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.