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How you perform GPU computation is very important if your company works with 3D visualizations, machine learning (ML), artificial intelligence (AI), or heavy computing of any kind.

Organizations used to spend a lot of time on training and computation tasks for deep learning models. They used to be less productive because it wasted their time, was expensive, and caused storage and space problems.

Modern GPUs are made to address this issue. They provide high efficiency for carrying out demanding computations and faster parallel training for your AI models.

Indigo research claims that when it comes to deep learning neural network training, GPUs can perform 250 times faster than CPUs.

Cloud GPUs are revolutionizing the field of data science and other emerging technologies by providing even faster performance, simple maintenance, lower costs, quick scaling, and time savings thanks to the development of cloud computing.

This article will introduce you to cloud GPU concepts, their connections to AI, ML, and deep learning, as well as some of the best cloud GPU deployment platforms available.

Read on to learn more about GPUs and which one is best for you!

What are GPUs?

The microprocessors known as graphics processing units, or GPUs, are used to accelerate the creation of graphics and simultaneous computations. They do this by using parallel processing capabilities and higher memory bandwidth. 

For the dense computing necessary in some applications, such as gaming, 3D imaging, video editing, crypto mining, and machine learning, they have come to be indispensable. 

It is no secret that GPUs perform dense computations, for which CPUs are incredibly slow, much more quickly and efficiently.

Due to the high resource requirements of the training phase, GPUs perform deep learning operations much faster than CPUs. Due to the numerous convolutional and dense operations, such operations require extensive data-point processing. 

For the kind of large-scale input data and deep networks that define deep learning projects, involve numerous matrix operations between tensors, weights, and layers.

GPUs are much more effective for running deep learning processes than CPUs because they can process these multiple tensor operations more quickly thanks to their numerous cores and accommodate more data thanks to their higher memory bandwidth. 

On a low-end GPU, a dense operation that takes a CPU 50 minutes to complete might only take a minute.

In short, in order to run the extensive operations needed to support contemporary compute-intensive applications, we simply need higher computing resources – which are done by GPUs.

Why utilize cloud GPUs?

Although some users choose to have on-premise GPUs, the acceptance of cloud GPUs has increased among data scientists. Custom installation, management, maintenance, and upgrade are frequently upfront costs and time commitments associated with having an on-premise GPU. 

In contrast, cloud platforms that offer GPU instances only require users to use the service, without the need for any of those technical tasks, and for a reasonable service fee.

These platforms are in charge of managing the entire GPU infrastructure and offering all the services necessary to use GPUs for computing.

Users can concentrate on their area of expertise in business by eliminating the technical procedures needed to manage on-premise GPUs themselves. resulting in a simplification of business processes and increased productivity.

Utilizing cloud GPUs eliminates the difficulties associated with managing on-premise GPUs and is also more economical than purchasing and maintaining on-site infrastructure. 

Smaller businesses gain from this because it lowers their barrier to developing deep learning infrastructures by converting the capital costs needed to install and manage such computing resources into an operational cost for using cloud GPU services.

Data migration, accessibility, integration, storage, security, upgrade, scalability, collaboration, control, and support for stress-free and effective computing are additional benefits offered by cloud platforms.

It would make perfect sense to have someone else provide the necessary ingredients so you can concentrate on preparing the meal, much like a chef and their assistants.

What Advantages Do Cloud GPUs Offer?

The main advantages of utilizing Cloud GPUs are:

High Scalability

Your organization’s workload will eventually increase if you decide to grow it. A GPU that can scale with your increased workload is required. 

By making it simple and hassle-free to add more GPUs, cloud GPUs can assist you in meeting your increased workloads. On the other hand, it’s also quick and easy to scale back.

Reduces Cost

You can choose cloud GPUs on a rental that is available at a lower cost on an hourly basis rather than purchasing physically expensive high-power GPUs. 

In contrast to physical GPUs, which would have cost you a lot even if you hardly used them, you will only be charged for the hours that you used the cloud GPUs.

Makes Local Resources Clear

Unlike physical GPUs, which take up a lot of space on your computer, cloud GPUs don’t use any of your local resources. Additionally, running a complex ML model or rendering a task slows down your computer.

This way, you can use your computer without strain by outsourcing the computational power to the cloud. 

Instead of putting too much pressure on the computer to handle the workload and computational tasks, just let it take control of everything.

Reduces Time

Cloud GPUs give designers the freedom to iterate quickly while speeding up rendering. By finishing a task that used to take hours or days in a matter of minutes, you can save a lot of time. 

As a result, your team’s productivity will significantly increase, allowing you to devote more time to innovation rather than rendering or computations.

How to get started with Cloud GPUs?

As cloud platforms create more user-friendly interfaces for customers, utilizing cloud GPUs is becoming simpler.

Selecting a cloud platform would be the first step in using cloud GPUs. Making an informed decision that is suitable for your needs requires comparing platforms based on their individual services. 

While this article offers some recommendations in this article for the best cloud GPU platforms and instances for your deep learning workloads, feel free to independently investigate additional options to see what best suits your requirements.

After selecting a platform, the following step would be to become familiar with its interface and infrastructure. 

In this case, repetition leads to mastery. 

To learn how to use the majority of cloud platforms, there is a wealth of documentation, instructional videos, and blogs. These act as a user’s manual.

Next, we will start to go over which are the best Cloud GPU providers.

Top Cloud GPU Providers

RunPod

Save over 80% on GPUs.

RunPod strives to provide the best user experience possible in core GPU computing. We currently provide container-based instances that can be launched in seconds. 

Custom bare-metal and virtual machine deployments are also available. 

They started small by offering automated on-demand virtual machines in Q4 2022, with the goal of soon offering serverless GPU compute.

However, they are lower priced by a large margin – and much easier to deal with than any other provider we have used.

OVHcloud

Cloud servers from OVHcloud are built to handle enormous parallel workloads. To meet the needs of deep learning and machine learning, the GPUs have numerous NVIDIA Tesla V100 graphic processing units integrated into them.

They support the acceleration of artificial intelligence and graphic computing. In order to provide the best GPU-accelerated platform for deep learning, AI, and high-performance computing, OVH teams up with NVIDIA.

Utilize a comprehensive catalog to deploy and maintain GPU-accelerated containers in the simplest possible manner. It doesn’t use a virtualization layer and instead delivers one of the four cards to the instances directly via PCI Passthrough.

The infrastructure and services provided by OVHcloud have ISO/IEC 27017, 27001, 27701, and 27018 certifications. The accreditations show that OVHcloud has a privacy information management system (PIMS), a business continuity management system (BCMS), and an information security management system (ISMS) to manage vulnerabilities (PIMS).

NVIDIA Tesla V100 also comes with many useful features like PCIe 32 GB/s, 16 GB of HBM2 capacity, 900 GB/s of bandwidth, double precision-7 teraFLOPs, single precision-14 teraFLOPs, and deep learning-112 teraFLOPs.

Linode

For parallel processing workloads like video processing, scientific computing, machine learning, AI, and more, Linode offers on-demand GPUs. It offers GPU-optimized virtual machines (VMs) that are accelerated by NVIDIA Quadro RTX 6000, Tensor, and RT cores, and it makes use of the CUDA power to run complex computation, deep learning, and ray tracing workloads.

By utilizing Linode GPU access, you can convert your capital expense into an operating expense while maximizing the true value of the cloud. 

Additionally, Linode enables you to focus on your core competencies rather than worrying about the hardware.

With Linode GPUs, it is now possible to use them for sophisticated applications like video streaming, artificial intelligence, and machine learning. 

In addition, depending on the amount of processing power required for anticipated workloads, you may receive up to 4 cards for each instance.

4,608 CUDA cores, 576 Tensor cores, 72 RT cores, 24 GB of GDDR6 GPU memory, 84T RTX-OPS, 10 Giga Rays/sec Rays Cast, and 16.3 TFLOPs of FP32 performance are all features of the Quadro RTX 6000.

The dedicated plus RTX6000 GPU plan costs $1.5 per hour.

Paperspace CORE

Utilize Paperspace CORE’s cutting-edge accelerated computing infrastructure to speed up organizational workflow. 

It provides simple onboarding, collaboration tools, and desktop applications for Mac, Linux, and Windows through an easy-to-use interface. Use it to run resource-intensive applications with boundless computing power.

A super-fast network, immediate provisioning, support for 3D apps, and a complete API for programmatic access are all offered by CORE. 

Get a comprehensive view of your infrastructure in one location using an easy-to-use GUI. Additionally, the CORE’s management interface gives you excellent control by allowing you to filter, sort, connect, or create machines, networks, and users, among other functions.

The capable management console of CORE quickly adds VPN or Active Directory integration. 

You can complete tasks more quickly with a few clicks and manage complicated network configurations with ease.

You will also discover a lot of optional integrations that will aid you in your work. With this cloud GPU platform, you can get advanced security features, shared drives, and more. 

Take advantage of affordable GPUs by receiving educational discounts, billing alerts, second billing, etc.

For a starting price of $0.07/hour, make the workflow more straightforward.

Google Cloud GPUs

Utilize Google Cloud GPUs to obtain powerful GPUs for machine learning, 3D visualization, and scientific computing. HPC can be accelerated, a variety of GPUs can be chosen to match price and performance, and machine customizations and flexible pricing help to reduce workload.

They also provide a wide variety of GPUs, including the NVIDIA K80, P4, V100, A100, T4, and P100. Additionally, Google Cloud GPUs balance each instance’s memory, processor, high-performance disk, and up to 8 GPUs for the specific workload.

You also have access to networking, data analytics, and storage that are at the top of their fields. 

In some regions, GPU devices are only accessible in certain zones. The region, GPU you select, and machine type will all affect the cost. 

By specifying your needs in the Google Cloud Pricing Calculator, you can determine your price.

As an alternative, consider these options:

Elastic GPU Service

Elastic GPU Service (EGS) uses GPU technology to offer parallel and robust computing capabilities. Numerous applications, including video processing, visualization, scientific computing, and deep learning, are ideal for it. NVIDIA Tesla M40, NVIDIA Tesla V100, NVIDIA Tesla P4, NVIDIA Tesla P100, and AMD FirePro S7150 are just a few of the GPUs used by EGS.

Benefits include 4K/8K HD live, content identification, image and voice recognition, HD media coding, video conferencing, source film repair, and online deep learning inference services and training.

Get options like genetic engineering, non-linear editing, collision simulation, computational finance, climate prediction, engineering design, and video rendering as well.

Up to 4 AMD FirePro S7150 GPUs, 160 GB of memory, and 56 vCPUs are offered by the GA1 instance. It has 32 GB of parallel GPU memory and 8192 cores, which together produce 15 TFLOPS of single precision and 1 TFLOPS of double precision.

Up to 2 NVIDIA Tesla M40 GPUs, 96 GB of memory, and 56 vCPUs are offered by the GN4 instance. With 6000 cores and 24 GB of GPU memory, it offers 14 TFLOPS of single-precision performance. Similar situations include GN5, GN5i, and GN6, among others.

To provide the highest network performance required by the computational nodes, EGS internally supports 25 Gbit/s and up to 2,000,000 PPS of network bandwidth. It has an ultra-fast local cache that is connected to SSD or flash storage.

With an I/O latency of 200 s, high-performance NVMe drives can handle 230,000 IOPS while offering 1900 Mbit/s of read bandwidth and 1100 Mbit/s of write bandwidth.

Depending on your needs, you can select from a variety of purchasing options to obtain the resources and only pay for those.

Azure N Series

The Azure Virtual Machines (VMs) in the Azure N series are GPU-capable. Deep learning, predictive analytics, and remote visualization are just a few examples of the types of workloads that GPUs excel at, enabling users to accelerate innovation.

For particular workloads, different N series have separate offerings.

High-performance machine learning and computing workloads are the main topics of the NC series. The most recent version, NCsv3, includes NVIDIA’s Tesla V100 GPU.

The ND series primarily focuses on deep learning inference and training scenarios. NVIDIA Tesla P40 GPUs are employed. The NDv2 version, which uses NVIDIA Tesla V100 GPUs, is the most recent.

The NVIDIA Tesla M60 GPU-powered NV series focuses on remote visualization and other demanding application workloads.

Scale-up performance is made possible by the InfiniBand interconnect provided by the NC, NCsv3, NDs, and NCsv2 VMs. Benefits like deep learning, graphics rendering, video editing, gaming, etc. are available here.

IBM Cloud 

You have a lot of GPU options, power, and flexibility with IBM Cloud. Since a GPU is what a CPU lacks in extra brainpower, IBM Cloud enables you to directly access a wider range of servers for easier integration with the IBM Cloud architecture, applications, and APIs as well as a distributed network of data centers around the world.

You can choose from bare metal server GPU options like the Intel Xeon 4210 and NVIDIA T4 Graphics card, which both have 20 cores, 32 GB of RAM, and 2.20 GHz. You also have the choice of the Intel Xeon 5218 and Intel Xeon 6248.

You receive the AC1.860 virtual server, which has eight vCPU, 60 GB of RAM, and one P100 GPU. You can choose between AC2.860 and AC2.860 here as well.

Purchase a virtual server GPU for as little as $1.95/hour or a bare metal server GPU for as little as $819/month.

NVIDIA and AWS

Together, AWS and NVIDIA are continuously delivering GPU-based solutions that are affordable, adaptable, and powerful. 

It consists of Amazon EC2 instances running on NVIDIA GPUs and services like AWS IoT Greengrass, which is deployed using NVIDIA Jetson Nano modules.

For virtual workstations, machine learning (ML), Internet of Things (IoT) services, and high-performance computing, users use AWS and NVIDIA. 

Scalable performance is provided by the NVIDIA GPUs that power Amazon EC2 instances. Additionally, use AWS IoT Greengrass to connect NVIDIA-based edge devices to the AWS cloud services.

Amazon EC2 P4d instances are powered by NVIDIA A100 Tensor Core GPUs, which provide the industry’s lowest latency networking and highest throughput. Similar to this, there are numerous other instances available for particular scenarios, including Amazon EC2 P3, Amazon EC2 G4, etc.

Lambda GPU

With Lambda GPU Cloud, you can quickly scale from a single machine to all the virtual machines while training deep learning, machine learning, and AI models. 

Get the most recent Lambda Stack, which includes CUDA drivers and deep learning frameworks, pre-installed with the major frameworks.

From the dashboard, quickly access each machine’s dedicated Jupyter Notebook development environment. For direct access, use SSH directly with one of the SSH keys or connect via the Web Terminal in the cloud dashboard.

A maximum of 10 Gbps of inter-node bandwidth is supported by each instance, allowing for scattered training using frameworks like Horovod. Scaling to the number of GPUs on one or many instances can also speed up model optimization.

You can even save 50% on computing costs with Lambda GPU Cloud, lower cloud TCO, and avoid multi-year commitments. Use a single RTX 6000 GPU for $1.25/hour with six VCPUs, 46 GiB RAM, and 658 GiB of temporary storage. 

To receive an on-demand price for your use, select from a variety of instances based on your needs.

Genesis Cloud

Genesis Cloud offers a powerful cloud GPU platform at a very low cost. They are working together to provide a wide range of applications thanks to their access to numerous effective data centers around the world.

All of the services are automated, scalable, reliable, and secure. For use in visual effects, machine learning, transcoding or storage, big data analysis, and many other applications, Genesis Cloud offers limitless GPU computing power.

Free features that Genesis Cloud offers include storage volumes for large data sets, security groups to control network traffic, FastAI, PyTorch, preconfigured images, and a public TensorFlow API.

Different kinds of NVIDIA and AMD GPUs are present. Moreover, you can use GPU computing to create animated movies or train neural networks. To reduce carbon emissions, their data centers utilize 100% renewable energy from geothermal sources.

Because you pay in minute-level increments, their pricing is 85% less than that of other providers. Long-term and preventive discounts also enable you to make larger savings.

What GPU cloud provider should I pick?

To be completely honest, we cannot pick one cloud provider from the ones listed in this article for you because that choice depends on your use case and several other variables.

However, as a general rule, you ought to pick a cloud GPU provider based on your spending limit and the availability in your neighborhood (because this affects price). When selecting the GPU instance or model you’ll be using, additional factors will be relevant.

Nearly all cloud providers offer the extremely potent NVIDIA Teslas V100 GPU, which is ideal for running intensive computing tasks like machine learning, advanced graphics rendering, and 3D applications.

Use the Tesla if your application requires a lot of GPU processing power, but be sure to shop around for the best deal. For the V100 GPU’s high speed and dependability, Paperspace charges a reasonably low price.

Another potent GPU that is less expensive than the Tesla V100 is the Tesla K80. It works best for training some card programs, mid-level machine learning models, and high-definition video rendering.

Every GPU model is created for a specific use case, and the cost varies from cloud platform to cloud platform.

Conclusion

Remarkable performance, speed, scaling, space, and convenience are all features that Cloud GPUs are built to provide. As a result, think about selecting your preferred cloud GPU platform with ready-to-use features to speed up your deep learning models and manage AI workloads with ease.