TensorFlow Hosting | Managed GPU & TPU ML Deployment – B2BHostingClub

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Choose Your TensorFlow AI Hosting Plans

Unlock the power of TensorFlow hosting with B2BHOSTINGCLUB's high-performance GPU servers. Enhance your AI projects with speed and reliability.

Professional GPU Dedicated Server - RTX 2060

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  • 128GB RAM
  • GPU: Nvidia GeForce RTX 2060
  • Dual 8-Core E5-2660
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Ampere
  • CUDA Cores: 1920
  • Tensor Cores: 240
  • GPU Memory: 6GB GDDR6
  • FP32 Performance: 6.5 TFLOPS

Advanced GPU Dedicated Server - V100

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  • 128GB RAM
  • GPU: Nvidia V100
  • Dual 12-Core E5-2690v3
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Volta
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS

Multi-GPU Dedicated Server - 3xV100

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  • 256GB RAM
  • GPU: 3 x Nvidia V100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Volta
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS

Enterprise GPU Dedicated Server - RTX A6000

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  • 256GB RAM
  • GPU: Nvidia Quadro RTX A6000
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Ampere
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPS

Multi-GPU Dedicated Server - 3xRTX A6000

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  • 256GB RAM
  • GPU: 3 x Quadro RTX A6000
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ampere
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPS

Multi-GPU Dedicated Server - 4xRTX A6000

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  • 512GB RAM
  • GPU: 4 x Quadro RTX A6000
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ampere
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPS

Enterprise GPU Dedicated Server - RTX 4090

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  • 256GB RAM
  • GPU: GeForce RTX 4090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Ada Lovelace
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24 GB GDDR6X
  • FP32 Performance: 82.6 TFLOPS

Multi-GPU Dedicated Server- 2xRTX 4090

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  • 256GB RAM
  • GPU: 2 x GeForce RTX 4090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ada Lovelace
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24 GB GDDR6X
  • FP32 Performance: 82.6 TFLOPS

Enterprise GPU Dedicated Server - RTX 5090

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  • 256GB RAM
  • GPU: GeForce RTX 5090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Blackwell 2.0
  • CUDA Cores: 21,760
  • Tensor Cores: 680
  • GPU Memory: 32 GB GDDR7
  • FP32 Performance: 109.7 TFLOPS
  • This is a pre-sale product. Delivery will be completed within 2–10 days after payment.

Multi-GPU Dedicated Server- 2xRTX 5090

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  • 256GB RAM
  • GPU: 2 x GeForce RTX 5090
  • Dual E5-2699v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Blackwell 2.0
  • CUDA Cores: 21,760
  • Tensor Cores: 680
  • GPU Memory: 32 GB GDDR7
  • FP32 Performance: 109.7 TFLOPS
  • This is a pre-sale product. Delivery will be completed within 2–10 days after payment.

Enterprise GPU Dedicated Server - A100

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  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2
  • FP32 Performance: 19.5 TFLOPS

Multi-GPU Dedicated Server - 4xA100

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  • 512GB RAM
  • GPU: 4 x Nvidia A100
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2
  • FP32 Performance: 19.5 TFLOPS

Enterprise GPU Dedicated Server - A100(80GB)

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  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 80GB HBM2e
  • FP32 Performance: 19.5 TFLOPS

Enterprise GPU Dedicated Server - H100

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  • 256GB RAM
  • GPU: Nvidia H100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Hopper
  • CUDA Cores: 14,592
  • Tensor Cores: 456
  • GPU Memory: 80GB HBM2e
  • FP32 Performance: 183TFLOPS

TensorFlow vs PyTorch vs Keras: A Practical Comparison

Here’s a concise comparison of TensorFlow vs PyTorch vs Keras, tailored for developers, researchers, and businesses evaluating which deep learning framework best fits their needs.

Feature
TensorFlow
PyTorch
Keras
Developer Google Meta (Facebook) Initially independent, now part of TensorFlow
Release Year 2015 2016 2015
Language Python, C++, Java, Swift Python, C++, Cuda Python
Ease of Use Moderate High (Pythonic & intuitive) Very High (High-level API)
Flexibility High (especially with TF 2.x + tf.keras) Very High (dynamic graph) Low (high abstraction)
Execution Mode Static Graph (TF 1.x), Eager Execution (TF 2.x) Eager by default, supports TorchScript for static Uses TensorFlow backend
Model Deployment TensorFlow Serving, TFLite, TensorFlow.js TorchServe, ONNX Via TensorFlow tools
Community Support Large, production-ready tools Research-focused, rapidly growing Simplified entry point to TensorFlow
Best For Production deployment, mobile inference, enterprise Research, prototyping, custom models Beginners, quick prototyping
GPU Support CUDA + cuDNN CUDA + cuDNN Via TensorFlow GPU

8 Typical Use Cases of TensorFlow Hosting

Here are 8 Typical Use Cases of TensorFlow Hosting, ideal for promoting GPU servers on B2BHOSTINGCLUB.

Deep Learning Model Training

Train large-scale neural networks such as CNNs, RNNs, and Transformers using high-performance GPUs. Ideal for computer vision, speech recognition, and generative AI.

Real-Time Inference Serving

Deploy TensorFlow models in production to serve real-time predictions for apps like recommendation systems, chatbots, and fraud detection APIs.

Transfer Learning & Fine-Tuning

Use pre-trained models like BERT, EfficientNet, or ResNet and fine-tune them for your specific task. Save time and resources while achieving state-of-the-art results.

AI Research & Academic Projects

Universities, labs, and students can leverage GPU servers to run experiments, publish papers, and explore cutting-edge AI theories without hardware limitations.

Image Recognition & Classification

Build and train image classification models for use in security, retail, autonomous driving, or healthcare diagnostics.

Natural Language Processing (NLP)

Run text classification, sentiment analysis, machine translation, and question answering using models such as BERT, GPT, or T5 with TensorFlow.

Time Series Forecasting

Use LSTM, GRU, or Transformer models to predict stock prices, energy consumption, or IoT sensor data trends.

Reinforcement Learning Experiments

Train agents in simulated environments using TensorFlow’s RL libraries — useful for robotics, gaming, and strategy optimization.

Frequently asked questions

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.
TensorFlow Hosting refers to running TensorFlow models and workloads on powerful remote servers — typically equipped with high-performance NVIDIA GPUs — to train, test, and deploy machine learning models more efficiently.
Local machines often lack the GPU power, memory, and stability needed for deep learning workloads. Hosting on B2BHOSTINGCLUB gives you: Access to enterprise-grade GPUs (A100, A6000, RTX 4090, etc.), Fast NVMe storage, Preinstalled TensorFlow environments and Scalable infrastructure without upfront hardware costs.
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.
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."
Yes. You get full root access and can install custom Python packages, dependencies, or upload your own TensorFlow projects and datasets.
We support TensorFlow 2.x series, including the latest stable release. If you need a specific version or CUDA compatibility, we can customize the environment on request.
Absolutely! TensorFlow works seamlessly in Jupyter as long as the GPU is properly configured and detected. You can install and run JupyterLab freely.
GPUs dramatically accelerate training and inference by parallelizing matrix operations, leading to faster results, reduced training time, and real-time inference capabilities.
Yes — if a compatible GPU is detected and properly configured (CUDA + cuDNN installed), TensorFlow will automatically use the GPU for supported operations.
Yes — depending on your selected plan. We offer multi-GPU and multi-instance support, so you can train, fine-tune, and serve models in parallel.

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