GPU Dedicated Server for XGBoost Machine Learning

Gradient boosting is a powerful machine learning algorithm used to achieve state-of-the-art accuracy on a variety of tasks, such as regression, classification, and ranking. We provide bare metal servers with GPUs that are specifically designed for XGBoost.

Windows or Linux OS
Full Root/Admin Access
Support RDP/SSH Access
Free 24/7/365 Expert Online Support
Servers Delivered within 4 to 48 Hours

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Plans & Prices of GPU Servers for XGBoost

We offer cost-effective and optimized NVIDIA GPU servers for XGBoost.

Basic XGBoost GPU

Nvidia Tesla K40

For high-performance computing and large data workloads, such as deep learning and AI reasoning.

Starting at

$109.00

/month

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64 GB RAM
Eight-Core Xeon E5-2670
120GB SSD + 960GB SSD
100Mbps-1Gbps Bandwidth
Supported OS: Windows & Linux

GPU: Nvidia Tesla K40
Microarchitecture: Kepler
Max GPU: 2
CUDA Cores: 2880
GPU Memory: 12GB
Performance: 4.29 TFLOPS

Professional XGBoost GPU

Nvidia Tesla K80

For high-performance computing and large data workloads, such as deep learning and AI reasoning.

Starting at

$159.00

/month

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128 GB RAM
Dual 10-Core E5-2660v2
120GB SSD + 960GB SSD
100Mbps-1Gbps Bandwidth
Supported OS: Linux & Windows

GPU: Nvidia Tesla K80
Microarchitecture: Kepler
Max GPU: 2
CUDA Cores: 4992
GPU Memory: 24GB
Performance: 8.73 TFLOPS

Spring Sale! Save 30%

Advanced XGBoost GPU

Nvidia RTX A4000

RTX A4000 delivers real-time ray tracing, AI accelerated computing, and high-performance graphics to desktops.

30% off

209.00/m

$ 146.30/m

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128 GB RAM
Dual 12-Core E5-2697v2
240GB SSD + 2TB SSD
100Mbps-1Gbps Bandwidth
Supported OS: Linux & Windows

GPU: Nvidia RTX A4000
Microarchitecture: Ampere
Max GPU: 2
CUDA Cores: 6144
Tensor Cores: 192
GPU Memory: 16GB GDDR6
Performance: 19.2 TFLOPS

Advanced XGBoost GPU

Nvidia RTX A5000

RTX A5000 achieves an excellent balance between function, performance, and reliability. Assist designers, engineers, and artists to realize their visions.

Starting at

$269.00

/month

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128GB RAM
Dual 12-Core E5-2697v2
240GB SSD + 2TB SSD
100Mbps-1Gbps Bandwidth
Supported OS: Linux & Windows

GPU: Nvidia RTX A5000
Microarchitecture: Ampere
Max GPU: 2
CUDA Cores: 8192
GPU Memory: 24GB GDDR6
Performance: 27.8 TFLOPS

Enterprise XGBoost GPU

Nvidia A40

Accelerate data science and computation-based workloads. A40 is very suitable for AI and deep learning projects.

Starting at

$369.00

/month

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256 GB RAM
Dual E5-2697v4
240GB SSD + 2TB SSD + 2TB NVMe
100Mbps-1Gbps Bandwidth
Supported OS: Linux & Windows 10

GPU: Nvidia A40
Microarchitecture: Ampere
Max GPU: 1
CUDA Cores: 10,752
Tensor Cores: 336
GPU Memory: 48GB
Performance: 37.4 TFLOPS

Enterprise XGBoost GPU

Nvidia V100

V100 server is a cloud product that can accelerate for more than 600 HPC applications and various deep learning frameworks.

Starting at

$369.00

/month

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256 GB RAM
Dual E5-2697v4
240GB SSD + 2TB SSD + 2TB NVMe
100Mbps-1Gbps Bandwidth
Supported OS: Linux & Windows 10

GPU: Nvidia V100
Microarchitecture: Volta
Max GPU: 1
CUDA Cores: 5,120
Tensor Cores: 640
GPU Memory: 16GB
Performance: 14 TFLOPS

More GPU Hosting Plans

6 Reasons to Choose our GPU Servers for XGBoost

DBM enables powerful GPU hosting features on raw bare metal hardware, served on-demand. No more inefficiency, noisy neighbors, or complex pricing calculators.

Intel Xeon CPU

Intel Xeon has extraordinary processing power and speed, which is very suitable for running machine learning algorithms and frameworks. So you can totally use our Intel-Xeon-powered GPU Servers for XGBoost.

SSD-Based Drives

You can never go wrong with our own top-notch dedicated GPU servers for XGBoost, loaded with the latest Intel Xeon processors, terabytes of SSD disk space, and 128 GB of RAM per server.

Full Root/Admin Access

With full root/admin access, you are able to take full control of your dedicated GPU servers for XGBoost 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 XGBoost and networks.

Dedicated IP

One of the premium features is the dedicated IP address. Even the cheapest XGBoost GPU hosting plan is fully packed with dedicated IPv4 & IPv6 Internet protocols.

DDoS Protection

Resources among different users are fully isolated to ensure your data security. DBM protects against DDoS from the edge fast while ensuring legitimate traffic of your hosted GPUs for XGBoost is not compromised.

Advantages of XGBoost

Brand Selection

The companies manufacturing GPU chips include Nvidia, AMD, EVGA, ASUS, MSI, and Gigabyte. Nvidia has a larger market share. EVGA card may be more expensive because it is more like a high-end brand, while a Gigabyte card is more budget-friendly.

Ray Tracing

The newer GPUs feature ray tracing. Ray tracing creates more realistic in-game lighting effects. This does have an impact on the overall graphics of games that use ray tracing. GPU with ray tracing function usually has an "RTX" label in the product name.

GPU Memory

Most GPUs are equipped with up to 12 GB of video memory. Some newer cards, such as the Nvidia RTX A5000, come with up to 24 GB of video memory. The higher the memory, the higher the resolution you can play.

Clock Speed

The clock speed affects the frame rate that your GPU can produce. The higher the clock speed, the higher the frame rate of the game.

GPU Cooling

Since the GPU emits a lot of heat, it is important to have enough fans to cool the GPU. If the GPU is running very hot, we will ensure that the rest of the PC has enough fans to keep the temperature cool.

Stable Power Supply

Each GPU has the recommended minimum power in watts. The power supply in the data left must comply with this requirement because it powers not only your GPU but the entire PC and all its components.

FAQs of XGBoost GPU Server

A list of frequently asked questions about GPU servers for XGBoost.

What XGBoost is used for?

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solves many data science problems fast and accurately.

What is the XGBoost algorithm?

XGBoost is a popular and efficient open-source implementation of the gradient boosted trees algorithm. Gradient boosting is a supervised learning algorithm, which attempts to accurately predict a target variable by combining the estimates of a set of simpler and weaker models.

Is XGBoost a classification or regression?

XGBoost (eXtreme Gradient Boosting) is a popular supervised-learning algorithm used for regression and classification on large datasets.

Is XGBoost better than Random Forest?

Since the gradient of the data is considered for each tree, XGBoost is faster, and the precision is more accurate than Random Forest. This makes developers to depend on XGBoost than Random Forest. XGBoost is more complex than any other decision tree algorithm.

Is XGBoost better than SVM?

SVM and XGBoost models are developed for modeling global solar radiation. The two algorithms show comparable prediction accuracy. However, XGBoost models are more stable and efficient than SVM algorithms.

How can I run XGBoost on GPU?

XGBoost uses NVIDIA's CUDA parallel computing platform. You need to install the CUDA toolkit and XGBoost with CUDA support, then enable training of an XGBoost model using the GPU is straightforward—set the hyperparameter tree_method to "gpu_hist."

When to Use XGBoost GPU Servers?

1. Classification problems, especially those related to real-world business problems.

2. Problems in which the range or distribution of target values present in the training set can be expected to be similar to that of real-world testing data.

3. Situations in which there are many categorical variables.

4. Large number of observations in training data.

5. Number of features is smaller than the number of observations in training data.

When to NOT use XGBoost GPU Servers?

1. Number of observations in training data are significantly smaller than the number of features.

2. Computer vision

3. Natural language processing

4. Regression tasks that involve predicting a continuous output.

5. Predict increases in targets beyond the range present in the training data.

6. Tasks involving extrapolation.

TensorFlow vs Keras: Key Differences Between Them

1. Keras is a high-level API that can run on top of TensorFlow, CNTK, and Theano, whereas TensorFlow is a framework that offers both high and low-level APIs.

2. Keras is perfect for quick implementations, while Tensorflow is ideal for Deep learning research and complex networks.

3. Keras uses API debug tools, such as TFDBG. On the other hand, in Tensorflow, you can use Tensor board visualization tools for debugging.

4. Keras has a simple architecture that is readable and concise, while Tensorflow is not very easy to use.

5. Keras is usually used for small datasets, but TensorFlow is used for high-performance models and large datasets.

6. In Keras, community support is minimal, while in TensorFlow, it is backed by a large community of tech companies.

7. Keras is mostly used for low-performance models, whereas TensorFlow can be used for high-performance models.

Get Started Resources for XGBoost on GPU Servers

Guide and resources to help you get started with XGBoost GPU quickly.

Installation Prerequisites

1. Choose a plan and place an order.
2. Ubuntu 16.04 or higher, Windows 10 or higher.
3. Install NVIDIA® CUDA® Toolkit & cuDNN.
4. Python 3.6 - 3.8 recommended.

Step-by-Step Installation Instructions

Go to XGBoost docs site , read the install guide.

Note: Please note that training with multiple GPUs is only supported for Linux platform.

1. Installation method 1 - Install XGBoost with pip
- Sample:
- pip install --upgrade pip
- pip install xgboost
2. Installation method 2 - Use the Conda to Install XGBoost
The py-xgboost-gpu is currently not available on Windows. If you are using Windows, please use pip to install XGBoost with GPU support.
- Sample:
- # Use NVIDIA GPU
- conda install -c conda-forge py-xgboost-gpu
3. Verify the Installation
See examples here - GPU Acceleration Demo
- Sample:
- # GPU-Accelerated SHAP values
- model.set_param({"predictor": "gpu_predictor"})
- shap_values = model.predict(dtrain, pred_contribs=True)
- shap_interaction_values = model.predict(dtrain, pred_interactions=True)

XGBoost Tutorials

Quickstart Videos

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