Hosted AI and Deep Learning Dedicated Server

GPUs can offer significant speedups over CPUs when it comes to training deep neural networks. We provide bare metal servers with GPUs that are specifically designed for deep learning and AI purposes.

Plans & Prices of GPU Servers for Deep Learning and AI

We offer cost-effective NVIDIA GPU optimized servers for Deep Learning and AI.

Professional GPU VPS - A4000

129.00/mo
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  • 32GB RAM
  • 24 CPU Cores
  • 320GB SSD
  • 300Mbps Unmetered Bandwidth
  • Once per 2 Weeks Backup
  • OS: Linux / Windows 10
  • Dedicated GPU: Quadro RTX A4000
  • CUDA Cores: 6,144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • FP32 Performance: 19.2 TFLOPSreport
  • Available for Entry Level of AI/Deep Learning, Data Science, CAD/CGI/DCC .etc
Spring Sale

Basic GPU - RTX 4060

104.3/mo
Save 42% (Was $179.00)
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  • 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
  • Powerful for Gaming, Streaming, Video Editing, Emulators, Rendering.

    Cost-Effective Choice for AI, Deep Learning.
Spring Sale

Advanced GPU - A4000

169/mo
Save 39% (Was $279.00)
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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
  • Good Choice for 3D Rendering, Video Editing, AI/Deep Learning, Data Science, etc

Advanced GPU - V100

229.00/mo
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  • 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 - A5000

269.00/mo
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  • 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

Enterprise GPU - RTX 4090

409.00/mo
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  • 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

Enterprise GPU - RTX A6000

409.00/mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4report
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: Nvidia Quadro RTX A6000
  • Microarchitecture: Ampere
  • Max GPUs: 1
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPSreport
New Arrival

Enterprise GPU - A100

639.00/mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4report
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: Nvidia A100
  • Microarchitecture: Ampere
  • Max GPUs: 1
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2e
  • FP32 Performance: 19.5 TFLOPSreport

Enterprise GPU - A40

439.00/mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4report
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: Nvidia A40
  • Microarchitecture: Ampere
  • Max GPUs: 1
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 37.48 TFLOPSreport

Multi-GPU - 2xRTX 4090

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

Multi-GPU - 3xV100

469.00/mo
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  • 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/mo
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  • 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
New Arrival

Multi-GPU - 3xRTX A5000

539.00/mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4report
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: 3 x Quadro RTX A5000
  • Microarchitecture: Ampere
  • Max GPUs: 3report
  • CUDA Cores: 8192
  • Tensor Cores: 256
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPSreport
New Arrival

Multi-GPU - 4xA100

1899.00/mo
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  • 512GB RAM
  • Dual 22-Core E5-2699v4report
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: 4 x Nvidia A100 with NVLink
  • Microarchitecture: Ampere
  • Max GPUs: 4
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2e
  • FP32 Performance: 19.5 TFLOPSreport
New Arrival

Multi-GPU - 8xV100

1499.00/mo
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  • 512GB RAM
  • Dual 22-Core E5-2699v4report
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: 8 x Nvidia Tesla V100
  • Microarchitecture: Volta
  • Max GPUs: 8report
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPSreport

Professional GPU - P100

159.00/mo
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  • 128GB RAM
  • Dual 10-Core E5-2660v2report
  • 120GB + 960GB SSD
  • 100Mbps-1Gbpsreport
  • OS: Windows / Linux
  • GPU: Nvidia Tesla P100
  • Microarchitecture: Pascal
  • Max GPUs: 2report
  • CUDA Cores: 3584
  • Tensor Cores: 640
  • GPU Memory: 16 GB HBM2
  • FP32 Performance: 9.5 TFLOPSreport
More GPU Hosting Plansarrow_circle_right

6 Reasons to Choose our GPU Servers for Deep Learning

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 CPU

Intel Xeon has extraordinary processing power and speed, which is very suitable for running deep learning frameworks. So you can totally use our Intel-Xeon-powered GPU servers for deep learning and AI.
SSD-Based Drives

SSD-Based Drives

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

Full Root/Admin Access

With full root/admin access, you will be able to take full control of your dedicated GPU servers for deep learning very easily and quickly.
99.9% Uptime Guarantee

99.9% Uptime Guarantee

With enterprise-class data centers and infrastructure, we provide a 99.9% uptime guarantee for hosted GPUs for deep learning and networks.
Dedicated IP

Dedicated IP

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

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 hosted GPUs for deep learning is not compromised.

How to Choose the Best GPU Servers for Deep Learning

When you are choosing GPU rental servers for deep learning, the following factors should be considered.
Performance

Performance

The higher the floating-point computing capability of the graphics card, the higher the arithmetic power that deep learning, and scientific computing use.
Memory Capacity

Memory Capacity

Large memory can reduce the number of times to read data and reduce latency.
Memory Bandwidth

Memory Bandwidth

GPU memory bandwidth is a measure of the data transfer speed between a GPU and the system across a bus, such as PCI Express (PCIe) or Thunderbolt. It's important to consider the bandwidth of each GPU in a system when developing your high-performance Metal apps.
RT Core

RT Core

RT Cores are accelerator units that are dedicated to performing ray tracing operations with extraordinary efficiency. Combined with NVIDIA RTX software, RT Cores enable artists to use ray-traced rendering to create photorealistic objects and environments with physically accurate lighting.
Tensor Cores

Tensor Cores

Tensor Cores enable mixed-precision computing, dynamically adapting calculations to accelerate throughput while preserving accuracy.
Budget Price

Budget Price

We offer many cost-effective GPU server plans on the market, so you can easily find a plan that fits your business needs and is within your budget.

Freedom to Create a Personalized Deep Learning Environment

The following popular frameworks and tools are system-compatible, so please choose the appropriate version to install. We are happy to help.
tensorflow
TensorFlow is an open-source library developed by Google primarily for deep learning applications. It also supports traditional machine learning.
Jupyter
The Jupyter Notebook is a web-based interactive computing platform. It allows users to compile all aspects of a data project in one place making it easier to show the entire process of a project to your intended audience.
PyTorch
PyTorch is a machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing. It provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration, Deep neural networks built on a tape-based autograd system.
Keras
Keras is a high-level, deep-learning API developed by Google for implementing neural networks. It is written in Python and is used to implement neural networks easily. It also supports multiple backend neural network computations.
Caffe
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is written in C++, with a Python interface.
Theano
Theano is a Python library that allows us to evaluate mathematical operations including multidimensional arrays so efficiently. It is mostly used in building Deep Learning Projects.

FAQs of GPU Servers for Deep Learning

The most commonly asked questions about our GPU Dedicated Server for AI and deep learning below:

What is deep learning?

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Deep learning is a subset of machine learning and works on the structure and functions similarly to the human brain. It learns from unstructured data and uses complex algorithms to train a neural net.
We primarily use neural networks in deep learning, which is based on AI.

What are teraflops?

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A teraflop is a measure of a computer's speed. Specifically, it refers to a processor's capability to calculate one trillion floating-point operations per second. Each GPU plan shows the performance of GPU to help you choose the best deep learning servers for AI researches.

What is FP32?

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Single-precision floating-point format,sometimes called FP32 or float32, is a computer number format, usually occupying 32 bits in computer memory. It represents a wide dynamic range of numeric values by using a floating radix point.

What GPU is good for deep learning?

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The NVIDIA Tesla V100 is good for deep learning. It has a peak single-precision (FP32) throughput of 15.0 teraflops and comes with 16 GB of HBM memory.

What is the best budget GPU servers for deep learning?

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The best budget GPU servers for deep learning is the NVIDIA Quadro RTX A4000/A5000 server hosting. Both have a good balance between cost and performance. It is best suited for small projects in deep learning and AI.

Does GPU matter for deep learning?

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GPUs are important for deep learning because they offer good performance and memory for training deep neural networks. GPUs can help to speed up the training process by orders of magnitude.

How do you choose GPU servers for deep learning?

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When choosing a GPU server for deep learning, you need to consider the performance, memory, and budget. A good starting GPU is the NVIDIA Tesla V100, which has a peak single-precision (FP32) throughput of 14 teraflops and comes with 16 GB of HBM memory.
For a budget option, the best GPU is the NVIDIA Quadro RTX 4000, which has a good balance between cost and performance. It is best suited for small projects in deep learning and AI.

What are the advantages of bare metal servers with GPU?

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Bare metal servers with GPU 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 deep learning are all bare metal servers, so we have the best GPU dedicated server for AI.

Why is a GPU best for neural networks?

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A GPU is best for neural networks because it has tensor cores on board. Tensor cores speed up the matrix calculations needed for neural networks. Also, the large amount of fast memory in a GPU is important for neural networks. The decisive factor for neural networks is the parallel computation, which GPUs provide.