Dedicated CPU

What is a dedicated CPU ?

A dedicated CPU means that your company would have CPUs or cores that are exclusively assigned to you, and only you individually can use them.
Functions such as data analytics, machine learning, encoding and high-speed operations can process smoothly on such CPUs.

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Benefits & Advantages

The most important benefit of using a dedicated CPU unit is that the CPU's performance will be more consistent and efficient throughout its term of use.

Exclusive Use

Through these CPU units, you'll utilize the CPUs Center exclusively.

Total Control

Since the CPU unit is elite to you, it smoothens operations by giving you total control.

High-level Security and Lower risks

As the utilization of these units is greatly limited, these frameworks give high-level security and lower dangers of a information breach.

Fast processing and Data Backup features

Through these CPUs, you get fast processing and data backup features.

Frequently Asked Questions

You may need a dedicated CPU: 

● If you are running any CPU eater or memory eater application

● If you are receiving “high usage” notifications for consecutive days and facing processing delays on your machine

● If you are running more than 10 MT4 on a single VPS

● If you are running more than 20 charts on 1 MT4 on a single VPS


Dedicated CPUs can handle workloads like: 

● Medium-to-high-traffic web servers’ load

● Maintain E-commerce sites

● Medium-sized database handling

● Running Enterprise Software as a Service (SaaS)

● CPU-intensive game servers, like Team Fortress, Minecraft, and Rust

● Audio and video streaming and transcoding

● Machine learning tools such as Apache, TensorFlow, and PyTorch

● Data streaming and programming businesses

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Shared CPU

In a shared CPU, the core and the processing unit will be shared by multiple users under a specific server.

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High Memory


Cloud Graphics Units (GPUs) are computer instances with robust hardware acceleration helpful for running applications to handle massive AI and deep learning workloads in the cloud.

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