Save Big: Up To 10% Off On Multiple GPU Servers!

Power Up AI Workloads with TensorFlow GPU Acceleration

GPU dedicated servers for TensorFlow of GPU4HOST are the servers with a special graphics card developed mainly for optimal computing. Get this GPU-dedicated hosting for sound recognition, video detection, deep learning, and many more. Boost your AI tasks with TensorFlow GPU integration, offering quicker training and smooth performance.

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Advantages of TensorFlow Server

TensorFlow servers provide boosted AI-based workloads along with GPU integration and smooth scalability for huge datasets. Their powerful ecosystem and versatility make them the perfect choice for productive deep learning and ML-based tasks. With TensorFlow server proficiencies, it gets easy to smooth the complex computations related to deep learning and AI/ML.

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Scalable

With its features being used on every single machine and the graphical depiction of any model, it lets all of its operators make any type of system with the help of it.

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Keras friendly

It has very good compatibility with Keras API. Its users can simply code some advanced functionality parts in it. Keras offers systematic functionality to it, like pipelining.

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Data visualization

It has the best computational data visualizations. It allows simple debugging of any node with TensorBoard. This decreases the effort of checking the complete code.

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Graphical support

Deep learning utilizes this hosting for its advancement, as it easily allows the development of neural networks with different types of graphs that depict all operations simply as nodes.

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Parallelism

Because of the parallelism of work models, it always recognized its uses as a hardware acceleration library. It uses various distribution approaches in both CPU and GPU systems.

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Compatibility

It is fully compatible with different languages such as Python, C++, and JavaScript. The language compatibility lets users easily work in an atmosphere in which they feel comfortable.

Scalable GPU Pricing for TensorFlow

Save 40%
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V100

$ 669.00/month

$50.99
  • Multi-GPU - 3xV100
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • GPU: 3 x Nvidia V100
  • Microarchitecture: Volta
  • Max GPUs: 3
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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A4000

$ 349.00/month

$50.99
  • Dual 12-Core E5-2697v2
  • 128GB RAM
  • 2TB SSD
  • 1Gbps Port Speed
  • GPU: Nvidia Quadro RTX A4000
  • Microarchitecture: Ampere
  • Max GPUs: 2
  • CUDA Cores: 6144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • FP32 Performance: 19.2 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Save 40%
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Multi-GPU - 3xRTX 3060 Ti

$ 569.00/month

$50.99
  • Dual 18-Core E5-2697v4
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • GPU: 3 x GeForce RTX 3060 Ti
  • Microarchitecture: Ampere
  • Max GPUs: 3
  • CUDA Cores: 4864
  • Tensor Cores: 152
  • GPU Memory: 8GB GDDR6
  • FP32 Performance: 16.2 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Save 40%
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RTX 4090

$ 455.00/month

$50.99
  • Enterprise GPU - RTX 4090
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • 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 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Save 40%
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Multiple GPU - 3xV100

$ 719.00/month

$50.99
  • Dual 18-Core E5-2697v4
  • 256GB RAM
  • 2TB NVMe
  • 1Gbps Port Speed
  • GPU: 3xNvidia V100
  • Microarchitecture: Volta
  • Max GPUs: 3
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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Save 40%
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Multi-GPU - 3xRTX A6000

$ 1,269.00/month

$50.99
  • Dual 18-Core E5-2697v4
  • 256GB RAM
  • 2TB NVMe + 8TB SATA
  • 1Gbps Port Speed
  • GPU: 3 x Quadro RTX A6000
  • Microarchitecture: Ampere
  • Max GPUs: 3
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPS
  • OS: Windows / Linux
  • Fully managed
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  • Instant Support
  • Quick Deploy
  • Robust Security

Use Cases of TensorFlow Hosting Service

Various Use Cases of Deep Learning Utilizing GPU servers for Hosting

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Image Recognition

Social media and many more uses image recognition. Image recognition is mainly utilized for: recognizing the face, image search, photo clustering, and machine vision. It also explores its uses in the automotive industry, etc.

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Voice Recognition

Any sound and voice recognition applications are one of the best use cases of deep learning. If all the neural networks have the correct input data feed, all these networks are fully capable of properly understanding sound signals.

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Time Series

Deep learning utilizes algorithms of time-series for examining data to get useful statistics. Deep-learning time series is mostly used in the fields of accounting, management, safety, and IoT with proper resource planning, etc.

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Text-Based Applications

Text-based applications are well-known for various use cases of deep learning. Basic text-based applications like sentiment evaluation, threat identification, and fraud awareness. Our dedicated GPU servers can easily run these apps.

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Video Detection

Customers also search for the TensorFlow with GPU-dedicated server for video detection, like movement detection and real-world threat identification in various fields such as gaming, safety, airports, and user interface/user experience.

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Customized Content Suggestions

It can be specially used to offer customized content suggestions on various streaming platforms. By examining user behavior and choices, hosted models can simply recommend movies, etc. according to a person's moods.

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Understand How to Install

If you are a learner or an expert, then it is a fully dedicated platform that makes it an appropriate choice for you to develop and deploy machine learning models. This GPU support needs a set of modern libraries and drivers, containing a CUDA toolkit, cuDNN, and graphics driver. This guide will help you to know how to install all these libraries as well as dependencies for simply starting a GPU-based TensorFlow easily.

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Need Technical Help?

We are available here all the time to solve your queries! Contact us via phone call or live chat.

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Frequently Asked Questions

This type of hosting service offers uncountable advantages, including reliable performance for advanced model deployment, decreased management of infrastructure, and simple integration with various cloud services. It always provides complete accessibility and scalability simply by letting you concentrate on model development and modernization while the hosting service easily manages the operational side.

This hosting provides rationalized model management, containing proper support for model updating and versioning. You can easily get the latest versions of your present model without any downtime, and the facility mainly offers various tools for going back to past versions if required. This flexibility makes sure that all your models remain present and work perfectly while decreasing disturbances to your service.

It is a kind of special framework that is mainly used for tasks related to ML, but it is one of the best tools for deploying and forming AI-based applications. It offers a complete ecosystem for developing ML models, containing different libraries and tools for different kinds of learning, from managed to unmanaged to enhanced learning. It can be easily employed to develop advanced AI systems that influence all these ML models.

It provides different APIs to carry out all requirements. The Keras API offers a convenient and advanced interface for developing and training all neural networks. The TensorFlow Core API supports precise personalization and full control over model development. The TFX API is utilized mainly for handling and deploying ML pipelines in management.

It provides both low-level as well as high-level APIs. The Keras API is a type of high-level API that easily strengthens the procedure of developing and training various neural networks with an user-friendly interface. The TensorFlow Core API is a type of low-level API that offers full access and personalization over model development and training.

Yes, it is highly demanded among everyone. As a freely available framework for deep learning and ML, it is famous among experts, developers, and researchers for its strong capabilities, reliability, and wide-spread ecosystem. Its applications easily span across different industries such as healthcare, technology, automotive, and finance, fulfilling requests for experts and associated technologies.

Big tech companies such as Facebook and Google both still use it for carrying out various tasks, experts and researchers for advanced research, startups and traditional organizations across different industries, and engineers and scientists for easily creating and deploying AI and ML-related results. Its proper suitability in industry and investigation carries out its constant acceptance.

PyTorch is usually favored for its changing computation graph and comfort of use in both new experimentation and research. On the other hand, TensorFlow is popular for its extensive ecosystem and solid help for development and reliability. PyTorch outshines scalability and inbuilt design, while other one provides robust tools for deployment and advanced applications.

TensorFlow is basically an open-source ML-based framework that is advanced by Google, engineered especially for developing and training complex models for AI projects such as deep learning, computer vision, and natural language processing. It provides a variety of tools for deploying models across numerous platforms, ranging from servers to smartphones.

To simply check the version of TensorFlow, utilize the below command in the Python environment:
import tensorflow as tf
print(tf.__version__)
This above code will display the version of TensorFlow.

Yes, TensorFlow automatically utilizes a GPU if any are accessible and its appropriate cuDNN and CUDA versions are installed. By default, TensorFlow gives priority to GPU over CPU for complex work, guaranteeing quicker computation. If various GPUs are accessible simultaneously, it utilizes the first one except if stated otherwise.

Yes, you can simply utilize TensorFlow without any GPU. By default, TensorFlow easily runs on CPUs if no appropriate GPU is accessible, letting you run AI/ML and deep learning-based projects, although at a very slow speed as compared to GPU working.