Cost-effective
Renting GPU servers may be a more cost-effective solution than purchasing your own hardware, especially if you only need to use computing resources in a limited time.
Keras GPU Hosting – Powered by Database Mart GPU Servers
Run deep learning models faster and more efficiently with Keras GPU Hosting by Database Mart. Whether you're building neural networks for computer vision, NLP, or time-series forecasting, our GPU-powered infrastructure ensures maximum speed and flexibility.
Keras GPU Hosting Plans & Pricing
Elevate your deep learning applications with Keras GPU hosting by Database Mart. Benefit from high-speed GPU servers designed for optimal performance and efficiency.
Advanced Dedicated GPU Server - V100
$ 125.58/mo
58% OFF (Was $299.00)
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Order Now- GPU Model: V100
- CPU: 24-Core Dual E5-2690v3
- Memory: 128GB RAM
- Disk: 240GB SSD+2TB SSD
- Bandwidth: 100Mbps Unmetered
- GPU Memory: 16 GB HBM2
- IP: 1 Dedicated IPv4
- Location: USA
Enterprise Multi-GPU Dedicated Server - 3xV100
$ 469.00/mo
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Order Now- GPU Model: 3 x V100
- CPU: 36-Core Dual E5-2697v4
- Memory: 256GB RAM
- Disk: 240GB SSD+2TB NVMe+8TB SATA
- Bandwidth: 1000Mbps Unmetered
- GPU Memory: 16 GB HBM2
- IP: 1 Dedicated IPv4
- Location: USA
Advanced Dedicated GPU Server - RTX A5000
$ 269.00/mo
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Order Now- GPU Model: RTX A5000
- CPU: 24-Core Dual E5-2697v2
- Memory: 128GB RAM
- Disk: 240GB SSD+2TB SSD
- Bandwidth: 100Mbps Unmetered
- GPU Memory: 24 GB GDDR6
- IP: 1 Dedicated IPv4
- Location: USA
Enterprise Dedicated GPU Server - RTX A6000
$ 409.00/mo
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Order Now- GPU Model: RTX A6000
- CPU: 36-Core Dual E5-2697v4
- Memory: 256GB RAM
- Disk: 240GB SSD+2TB NVMe+8TB SATA
- Bandwidth: 100Mbps Unmetered
- GPU Memory: 48 GB GDDR6
- IP: 1 Dedicated IPv4
- Location: USA
Enterprise Multi-GPU Dedicated Server - 4xRTX A6000
$ 1199.00/mo
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Order Now- GPU Model: 4 x RTX A6000
- CPU: 44-core Dual E5-2699v4
- Memory: 512GB RAM
- Disk: 240GB SSD+4TB NVMe+16TB SATA
- Bandwidth: 1000Mbps Unmetered
- NVLink: 2xNVLink
- GPU Memory: 48 GB GDDR6
- IP: 1 Dedicated IPv4
- Location: USA
Enterprise Dedicated GPU Server - RTX 4090
$ 301.95/mo
45% OFF (Was $549.00)
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Order Now- GPU Model: RTX 4090
- CPU: 36-Core Dual E5-2697v4
- Memory: 256GB RAM
- Disk: 240GB SSD+2TB NVMe+8TB SATA
- Bandwidth: 100Mbps Unmetered
- GPU Memory: 24 GB GDDR6X
- IP: 1 Dedicated IPv4
- Location: USA
Enterprise Multi-GPU Dedicated Server - 2xRTX 4090
$ 539.40/mo
40% OFF (Was $899.00)
1mo3mo12mo24mo
Order Now- GPU Model: 2 x RTX 4090
- CPU: 36-Core Dual E5-2697v4
- Memory: 256GB RAM
- Disk: 240GB SSD+2TB NVMe+8TB SATA
- Bandwidth: 1000Mbps Unmetered
- GPU Memory: 24 GB GDDR6X
- IP: 1 Dedicated IPv4
- Location: USA
Enterprise Multi-GPU Dedicated Server - 2xRTX 5090
$ 604.45/mo
45% OFF (Was $1099.00)
1mo3mo12mo24mo
Order Now- GPU Model: 2 x RTX 5090
- CPU: 44-core Dual E5-2699v4
- Memory: 256GB RAM
- Disk: 240GB SSD+2TB NVMe+8TB SATA
- Bandwidth: 1000Mbps Unmetered
- GPU Memory: 32 GB GDDR7
- IP: 1 Dedicated IPv4
- Location: USA
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System Requirements for Keras GPU Hosting
Here is a table summarizing the System Requirements for Keras GPU Hosting (assuming TensorFlow is used as the backend):
| Category | Recommended Specification |
|---|---|
| Operating System | Ubuntu 20.04 / 22.04, or CentOS 7/8 |
| GPU | NVIDIA GPU with CUDA Compute Capability ≥ 3.5 (e.g., V100, A100, RTX 4090) |
| GPU Memory | Minimum 8 GB (16 GB+ recommended for large models) |
| CPU | 4+ cores (Intel or AMD x86_64) |
| RAM | Minimum 16 GB (32 GB+ recommended) |
| Python Version | Python 3.8 – 3.10 |
| Keras Version | Keras 2.11+ (included in TensorFlow 2.11+) |
| TensorFlow | TensorFlow 2.11+ (GPU version) |
| CUDA Toolkit | CUDA 11.2 or 11.8 |
| cuDNN Version | cuDNN 8.1 or 8.6 |
| Drivers | NVIDIA Driver ≥ 450.x (for CUDA 11.x) |
| Storage | SSD preferred; minimum 50 GB free space |
| Internet | Required for package installs and model downloads |
💡 Note: Keras now comes bundled with TensorFlow, so installing tensorflow ≥ 2.11 is typically enough to enable full GPU-accelerated Keras support.
How to Install Keras with GPU
To install Keras with GPU support, you need to ensure you have the necessary software and drivers installed. Here are requirements and a step-by-step guide:
Prerequisites for installing keras
- Choose a plan and place an order
- Ubuntu 16.04 or higher (64-bit) + WSL2
- Install NVIDIA® CUDA® Toolkit & cuDNN
- Python 3.7 - 3.10 recommended
Getting started with programming using keras
Go to TensorFlow's site , read the pip install guide.
- Install Miniconda or Anaconda
- Create a Conda Environment
conda create --name tf python=3.9- Install TensorFlow with GPU Support
pip install --upgrade pip
pip install tensorflow- Verify GPU Availability
If a list of GPU devices is returned, you've installed TensorFlow successfully.
import tensorflow as tf;
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
from tensorflow import keras6 Reasons to Choose our Keras GPU Servers
Utilizing Keras with GPU support provides significant benefits in terms of speed, efficiency, scalability, and overall performance, making it a powerful choice for deep learning applications.
Dedicated GPU Cards
When you purchase a GPU server from GPU Mart, you benefit from dedicated GPU resources. This means you have exclusive access to the entire GPU card's computing power, including all GPU memory, cores, and other resources.
Full Root/Admin Access
With full root/admin access, you will be able to take full control of your dedicated GPU servers for Keras very easily and quickly.
99.9% Uptime Guarantee
With enterprise-class data centers and infrastructure, we provide a 99.9% uptime guarantee for hosted GPUs for Keras and networks.
NVIDIA CUDA
NVIDIA CUDA is a parallel computing platform and API model created by NVIDIA. It provides a range of advantages that significantly enhance the performance and capabilities of various computational tasks.
Customization
The GPU Mart provides a series of hardware configurations, enabling you to select the specific GPU, memory, storage and other components that best suit your needs.
8 Use Cases for Keras GPU Hosting
Here are 8 typical use cases for Keras GPU Hosting, ideal for deployment on GPU servers such as those from Database Mart:
Image Classification
Train CNNs (e.g., ResNet, VGG) on large image datasets like CIFAR-10, ImageNet, or medical scans.
Object Detection
Build YOLO, SSD, or custom Keras models for detecting objects in real-time.
Text Classification & Sentiment Analysis
Use LSTM/GRU or Transformer models with word embeddings to analyze language.
Time Series Forecasting
Predict stock prices, weather, or IoT data using RNNs, LSTMs, or 1D-CNNs.
Speech Recognition
Process audio data using spectrogram-based CNNs or recurrent models for ASR tasks.
Generative Models (GANs)
Create GANs for synthetic image generation, deep fakes, or artistic style transfer.
Autoencoders & Anomaly Detection
Train unsupervised models to detect rare events in industrial, finance, or security systems.
Transfer Learning
Fine-tune large pretrained models on custom datasets for fast, accurate results.
Features Comparison: Keras vs PyTorch vs TensorFlow
Everyone's situation and needs are different, so it boils down to which features matter the most for your AI project.
| Features | Keras | TensorFlow | PyTorch | MXNet |
|---|---|---|---|---|
| API Level | High | High and low | Low | Hign and low |
| Architecture | Simple, concise, readable | Not easy to use | Complex, less readable | Complex, less readable |
| Datasets | Smaller datasets | Large datasets, high performance | Large datasets, high performance | Large datasets, high performance |
| Debugging | Simple network, so debugging is not often needed | Difficult to conduct debugging | Good debugging capabilities | Hard to debug pure symbol codes |
| Trained Models | Yes | Yes | Yes | Yes |
| Popularity | Most popular | Second most popular | Third most popular | Fourth most popular |
| Speed | Slow, low performance | Fastest on VGG-16, high performance | Fastest on Faster-RCNN, high performance | Fastest on ResNet-50, high performance |
| Written In | Python | C++, CUDA, Python | Lua, LuaJIT, C, CUDA, and C++ | C++, Python |
FAQs of Keras GPU Server
A list of frequently asked questions about GPU servers for Keras.
What Keras is used for?
Keras is a high-level, deep-learning API developed by Google for implementing neural networks. It is written in Python and is used to simplify the implementation of the neural network. It also supports multiple backend neural network computations. For these uses, you often need GPUs for Keras.
Is Keras better than PyTorch?
Keras is mostly used for small datasets due to its slow speed. While PyTorch is preferred for large datasets and high performance.
What is Keras GPU?
Keras is a Python-based, deep learning API that runs on top of the TensorFlow machine learning platform, and fully supports GPUs. Keras was historically a high-level API sitting on top of a lower-level neural network API. It served as a wrapper for lower-level TensorFlow libraries.
When do I need GPUs for Keras?
If you're training a real-life project or doing some academic or industrial research, then for sure you need a GPU for fast computation.
If you're just learning Keras and want to play around with its different functionalities, then Keras without GPU is fine and your CPU in enough for that.
If you're just learning Keras and want to play around with its different functionalities, then Keras without GPU is fine and your CPU in enough for that.
How can I run a Keras model on multiple GPUs?
We recommend doing so using the TensorFlow backend. There are two ways to run a single model on multiple GPUs: data parallelism and device parallelism. In most cases, what you need is most likely data parallelism.
What are the advantages of bare metal GPUs for Keras?
Bare metal GPU servers for Keras will provide you with an improved application and data performance while maintaining high-level security. When there is no virtualization, there is no overhead for a hypervisor, so the performance benefits. Most virtual environments and cloud solutions come with security risks.
DBM GPU Servers for Keras use all bare metal servers, so we have best GPU dedicated server for AI.
DBM GPU Servers for Keras use all bare metal servers, so we have best GPU dedicated server for AI.
Can I use Jupyter Notebook with Keras hosting?
Absolutely. Jupyter Notebook is included in our server images. You can train, test, and visualize your Keras models directly from a web-based notebook interface.
What support do you offer for Keras hosting?
We offer 24/7 technical support for server-related issues. While we don’t provide code-level AI development support, we can assist with environment setup, performance tuning, and GPU optimization.
Why do we need Keras?
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load:
It offers consistent & simple APIs.
It minimizes the number of user actions required for common use cases.
It provides clear and actionable feedback upon user error.
It offers consistent & simple APIs.
It minimizes the number of user actions required for common use cases.
It provides clear and actionable feedback upon user error.
Does Keras automatically use GPU?
Keras models will transparently run on a single GPU with no code changes required. Note: Use tf. config. list_physical_devices('GPU') to confirm that TensorFlow is using the GPU.
Do I need to install Keras if I have TensorFlow?
Thanks to a new update in TensorFlow 2.0+, if you installed TensorFlow as instructed, you don't need to install Keras anymore because it is installed with TensorFlow. For those using TensorFlow versions before 2.0, here are the instructions for installing Keras using Pip.
What are the best GPUs for Keras deep learning?
Today, leading vendor NVIDIA offers the best GPUs for Keras deep learning in 2022. The models are the RTX 3090, RTX 3080, RTX 3070, RTX A6000, RTX A5000, RTX A4000, Tesla K80, and Tesla K40. We will offer more suitable GPUs for Keras in 2023.
Feel free to choose the best plan that has the right CPU, resources, and GPUs for Keras.
Feel free to choose the best plan that has the right CPU, resources, and GPUs for Keras.
How can I run Keras on GPU?
If you are running on the TensorFlow or CNTK backends, your code will automatically run on GPU if any available GPU is detected.
If you are running on the Theano backend, you can use theano flags or manually set config at the beginning of your code.
If you are running on the Theano backend, you can use theano flags or manually set config at the beginning of your code.
Is Keras pre-installed on your hosting servers?
No, we provide GPU bare metal servers, which only have GPU drivers installed by default. You are free to install other software environments. However, if you have a need, we are happy to help. We can offer pre-configured environments with Keras, TensorFlow, CUDA, cuDNN, and other necessary libraries ready to use. Custom installations are also available upon request.
Do I get root/administrator access to the server?
Yes. You receive full SSH or Remote Desktop access (depending on OS), allowing you to install additional libraries or customize the environment as needed.