What is a Cloud GPU? Understanding the Benefits and Major Cloud GPU Providers

According to Moore’s Law, the number of transistors on a single chip has doubled approximately every two years for decades. This directly shows that computer technology is advancing at an astounding rate. It is pushing the boundaries of what is possible. Hence, the question is very frequent: What is a cloud GPU server?

Modern applications such as AI/ML and physical simulations for scientific purposes demand a lot of computational power in today’s environment. This is where graphics processing units, or GPUs, as they are commonly referred to, come in.

Not only the details of GPU cloud servers are circulated, but also the demand for top cloud GPU providers is increasing at an alarming rate. GPUs initially developed for graphics processing are now faced as valuable assets for data-parallel computation. 

On the other hand, the cost of acquiring and maintaining on-premise GPUs is a major drawback many companies will face. This is where cloud GPU servers come in. These cloud-powered servers provide the extra muscle to virtualization in cloud computing and the accessibility that is needed for all mission-critical projects. 

Furthermore, we have compiled a list of top cloud GPU providers, their detailed analysis, and more about GPUs to give you an insightful reading journey. So let’s hop on the blog without a delay of a second. 

Covering The Basics: What Is A Cloud GPU?

A cloud GPU is an off-premise GPU available from a cloud service provider, for instance, MilesWeb, AWS, GCP, or the other cloud solution providers. These virtual GPUs offer the same processing power as physical GPUs, but with the added benefit of using GPUs in cloud computing.

The other benefits associated with this solution include scalability, flexibility, and cost of the solution. Legacy on-premise GPUs entail significant capital expenditure in acquiring the hardware, the cost of powering it, and the incidental costs of maintaining them. On the contrary, cloud GPUs do not pose this problem. Here, users are only charged for the resources they consume.

Further, a cloud-based GPU has the advantage of flexibility, where users can easily change the amount of processing power that the GPU needs. Similarly, Ruby on Rails, with its rapid development and convention over configuration approach, can be effectively utilized with cloud GPU servers.

Use Cases for Cloud GPUs

Cloud GPUs are establishing the fundamental structure of many business industries today, especially given modern technological advancement. These operational-natured and slender servers have a monumental configuration to handle rich complications. Because they make it possible to perform two or more operations at once, parallel computing with neural networks is also useful in computations. Some of the most prominent use cases for cloud GPUs include:

• Since training complex machine learning models is computationally intensive, cloud GPUs offer the necessary computation points conveniently.
• A cloud GPU helps scientists build a realistic model of various phenomena, ranging from the weather to the interaction of biomolecules, for faster research.
• Top video editing software and rendering, specifically in cases that deal with high-definition content, can take a lot of advantage of cloud GPUs’ parallel processing abilities.
• Laravel can benefit from cloud GPU servers when integrated with libraries leveraging GPU capabilities.
• Cloud GPUs underpin advanced medical imaging and drug discovery simulations, bringing improvements to the healthcare sector.

Cloud GPUs: A Simplified Overview

Major Cloud GPU Providers

Now moving on to the most-awaited part of this comprehensive guide. Since we have already discussed what GPU is in cloud computing and how it benefits, it’s also essential to cover another major aspect: learning about the GPU cloud computing service providers. 

With the increase of simulations, parallel processing, and AI/ML-infused critical projects, GPU cloud servers for deep learning streamline the process. Hence, users look for GPU cloud providers that offer the best cloud GPU price comparison, impeccable features, top-line cloud configurations, and much more. 

Here’s our list of the top and cheapest GPU cloud providers with all the resources that you need. 

1. MilesWeb: Next-Level Computing

MilesWeb has set itself as the dynamic and cheapest GPU cloud provider. They offer solutions with expansive and versatile GPU features, like AWS, Google Cloud, and Azure. They have not only uplifted from the basic computer science student level but also offer a primary focus in the Indian market around a global server presence. Also, you get to experience their world-class GPU dedicated server hosting solutions at a competitive price tag.

For businesses needing 24/ 7 customer support and an affordable price, MilesWeb is the answer to what is GPU in cloud computing for businesses with specific needs. 

Plan Cost: Starting at $255.00/month.

2. Amazon Web Services (AWS): GPU Power

Amazon Web Services presents a large number of cloud GPU instances based on Nvidia GPUs meeting various necessities. Starting from high computing power Tesla V100 instances for complex AI tasks to P4D instances that are designed for basic simulation purposes and more. 

The drawback is that since they are recognized cloud GPU service providers, you’ll find their plans out of your budget with expensive costs. 

Plan Cost: Add services as per your need and get an estimation. 

3. Google Cloud Platform (GCP): High-Performance Computing

GCP has a versatile cloud GPU selection that is inclusive of NVIDIA Tesla and AMD Radeon. They like standard offerings of pre-configured instances for certain situations, such as the use of machine learning or scientific computations to make deployment easier.

Plan Cost: Similar to AWS, you have to add your required configurations to get started with an estimated plan cost. 

4. Microsoft Azure: Accelerated Computing

When it comes to what is GPU in cloud computing, Azure Virtual Machines supports the NVIDIA Tesla M60 and T4 series with different configurations. It is also compatible with other services developed by Microsoft, such as Azure Machine Learning, for a perfect AI development process.

Plan Cost: Users need to sign up a form with their requirements to get the quotations about the GPU cloud server. 

5. OVHcloud: Parallel Processing

OVHcloud, along with several other web hosting solutions, offers cloud GPS servers. It offers a set of GPU instances that are physical hardware-isolated servers offering the best performance and security for your GPU-accelerated jobs.

Plan Cost: Starting at $329.26/month.

A Comprehensive Comparison of Cloud GPU Providers

Best Practices for Optimizing Cloud GPU Usage

Cloud GPUs are capable of processing large amounts of data, but optimizing their use is necessary. With precise optimization practices, users can fully utilize the potential of their cloud GPU servers. 

You can guarantee effective use of these priceless resources, save expenses, and improve performance by adhering to best practices. Here are a few of the optimization practices that you can follow and save your bucks effortlessly.

1. Choose the right instance type

• To optimize your GPU usage and the necessity for workload, it is crucial to align it with the correct instance types.

2. Monitor resource utilization

• Reliable cloud GPU providers like MilesWeb offer tools that help in the tracking of resources and knowing which ones are wasting resources.

3. Utilize containerization

• With some techniques, such as the use of containers like Docker, it will be easier to manage the deployment and the software in cloud GPUs.

4. Take advantage of auto-scaling

• It is also possible to set up your cloud environment in an automated process. Thus, your GPU resources scale automatically without creating hindrances to the performance.