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Cloud GPU Providers

Whether your company is involved in 3D visualization, machine learning, artificial intelligence, or any other type of heavy computing, your GPU computing strategy is critical.

Deep learning models used to take a long time to train and compute in businesses. As a result, their productivity suffered because it took up a lot of their time, was expensive, and left them with storage and space issues.

The latest generation of GPUs addresses this issue. They can handle large calculations and accelerate the training of your AI models due to their excellent parallel processing efficiency.

GPUs can train neural networks connected to deep learning 250 times faster than CPUs - and a new generation of cloud GPUs is transforming data science and other emerging technologies by providing even higher performance at a lower cost while allowing for easy scalability and rapid deployment.

This post will introduce you to cloud GPU concepts, how they relate to AI, ML, and deep learning, and some of the best cloud GPU platforms for deploying your preferred cloud GPU.

Our Top Pick for the Absolute Best Cloud GPU Provider




runpod cloud gpus

Out of all the Cloud GPU providers we tried (we tried a lot of them... and for significant amounts of time) RunPod is by leagues above the rest. It's not even a comparison.

If you're one of those people who doesn't feel like reading an entire article on GPUs, how they work, and who invented the damn things... And you just need a nudge on the right path - proceed no further, because this is really all you need.

If you need a little primer on how it all breaks down - keep reading.

What are Cloud GPUs?

To better understand a cloud GPU, let's first talk about GPUs.

GPUs are specialized electronic circuitry that can rapidly alter and manipulate memory so that images and graphics can be created at a much faster rate.

It is because of their parallel structure that modern graphics processing units are more efficient at manipulating images and computer graphics than traditional CPUs (CPUs). A GPU may be found on the PC's motherboard, video card, or even the CPU die.

Cloud Graphics Units (GPUs), which are computer instances with powerful hardware acceleration, can be used to perform enormous AI and deep learning tasks in the cloud. It is not necessary to have a GPU installed on your computer to use this feature.

AMD, NVIDIA, Radeon, and GeForce are just some of the popular GPU brands.

GPU Strengths


If you wish to grow your company, the burden will inevitably increase. You'll need a GPU that can keep up with the additional workload. Cloud GPUs can assist you with this by allowing you to easily add extra GPUs without any problems in order to handle your rising demands. If you wish to scale down, you may do it quickly as well.


Instead of purchasing high-powered hardware GPUs, which are extremely expensive, you can rent cloud GPUs on an hourly basis for a lesser cost. You will be charged for the amount of hours you used the cloud GPUs, as opposed to the physical ones, which would have cost you a lot even if you didn't use them much.

"OPR" Other People's Resources

You've heard of "OPM", but how about "OPR"?

Cloud GPUs do not utilize your local resources, unlike real GPUs, which take up a lot of space on your PC. Furthermore, running a large-scale ML model or rendering a job slows down your machine.

You might consider outsourcing the processing power to the cloud to avoid taxing your computer and allowing it to be used with comfort. Simply utilize the computer to control everything rather than putting all of the strain and computational responsibilities on it.

The role of GPUs in AI / ML / DL

Artificial intelligence is built on deep learning. It is a sophisticated ML approach that emphasizes representational learning using Artificial Neural Networks (ANNs). Deep learning models are used to process massive datasets or computationally intensive procedures.

So, how do GPUs come into play?

GPUs are intended to execute parallel computations or many calculations at the same time. GPUs can use the deep learning model's capabilities to accelerate big computational workloads.

GPUs provide great parallel processing capabilities due to their multiple cores. Furthermore, they have increased memory bandwidth to support enormous volumes of data for deep learning systems. As a result, they are commonly used for training AI models, generating CAD models, and playing graphics-intensive video games, among other things.

Furthermore, if you wish to test various algorithms at the same time, you may use multiple GPUs. It allows various processes to run on separate GPUs without parallelism. To distribute massive data models, you can employ many GPUs across separate physical computers or on a single system.

A few examples of how GPUs are utilized

  • AI and ML are being used for picture recognition in AI.
  • Computer images and CAD designs that use 3D computer graphics.
  • Rendering polygons with texture mapping.
  • Translation and rotation of vertices in a coordinate system, for example,
  • Textures and vertices may now be modified through the use of programmable shaders.
  • Video streaming, encoding, and decoding on the GPU
  • Cloud-based and high-quality video games.
  • Analytical and deep learning applications that demand general-purpose GPUs to handle large amounts of data in a scalable manner.
  • All aspects of the production process from filming to creating content.

But where to begin...?

It is not difficult to get started with cloud GPUs. In reality, once you grasp the fundamentals, everything becomes simple and quick. First and foremost, you must select a cloud GPU provider, such as Google Cloud Platform (GCP).

Sign up for GCP next. All of the typical features are available here, such as cloud functions, storage choices, database administration, interaction with apps, and more. You may also utilize their Google Colboratory, which is similarly to Jupyter Notebook, to use one GPU for free. Finally, you can begin rendering GPUs for your application.

So, let's have a look at the various cloud GPU solutions for AI and large workloads.

Other options for Cloud GPU Providers

Google Cloud GPUs

google cloud gpus

With Google Cloud GPUs, you can get high-performance GPUs for 3D visualization, scientific computing, and machine learning. By customizing your machines and offering several pricing options, you may reduce your burden while also speeding up HPC.

As a result, they also offer a wide range of graphics cards from NVIDIA such as the K80 and P4. Aside from that, Google Cloud GPUs optimize each instance's memory, processor, fast disk, and up to eight GPUs.

In addition, you'll get access to networking, data insights, and storage that are among the best in the business. There are only a limited number of GPUs available in various places. When it comes to the cost, it depends on where you live, the type of system, and the kind of GPU you choose.


ovh cloud gpus

OVHcloud's cloud servers are built to handle large simultaneous workloads. The GPUs feature several instances of NVIDIA Tesla V100 graphics processors integrated to fulfill deep learning and artificial learning requirements.

Additionally, they aid in the development of graphics processing units (GPUs). With NVIDIA, OVH offers the greatest GPU-accelerated system for high-performance computation, AI, and deep, learning.

Use a full catalog to install and maintain GPU-accelerated containers most easily. There is no virtualization layer in the way, so you get the full power of one of the four cards.

OVHcloud's services and facilities are certified to ISO/IEC 27017, 27701, 27001, and 27018. Certifications show that the information security management system or ISMS of OVHcloud is in place to manage risks and vulnerabilities and to build a privacy information management system or PIMS.

The NVIDIA Tesla V100, on the other hand, provides a wide range of useful characteristics, including PCIe 32 Gbps, 16 GB HBM2 of memory, 900 GB/s bandwidth, single precision-14 teraFLOPs, double precision-7 teraFLOPs, and deep learning-112 teraFLOPs.

Lambda GPU

lambda ai ml cloud gpus

With Lambda GPU Cloud, you can quickly scale your deep learning, machine learning, and artificial intelligence (AI) models from a single computer to an unlimited number of VMs. In addition to CUDA drivers & deep learning frameworks, you'll get the newest edition of the lambda stack pre-installed and ready to go.

The dashboard provides rapid access to the specialized Jupyter Notebook programming environment for each computer. If you want direct access, use SSH with one of your SSH keys or a Web Terminal in your cloud dashboard's management interface.

Inter-node bandwidth is limited to 10 Gbps per instance, which allows for distributed training using frameworks like Horovod. Model optimization can also be accelerated by expanding the number of GPUs on one or more instances.

Lambda GPU Cloud allows you to save up to 50% on compute costs, lower cloud TCO, and avoid multi-year contracts. Use one RTX 6000 with 6 VCPUs, 46 gigabytes of RAM, and 658 gigabytes of temporary storage for about $1.25 an hour. Choose from a variety of instances based on your specific needs and receive an on-demand price quote for your use.


paperspace cloud gpu provider

Paperspace CORE's next-generation accelerated computing infrastructure will turbocharge your company's workflow. Simple onboarding, collaborative tools, and desktop programs for Mac, Linux, and Windows are all available through this system, which features an easy-to-navigate user interface. Unlimited processing power can be used for high-demand applications.

Immediate provisioning, 3D app compatibility, and a full API for programmable access are all included in CORE's network. All of your infrastructures can be seen in one location, with an easy-to-use GUI. The CORE's administration interface also gives you complete control, with powerful capabilities for filtering, sorting, connecting, and creating machines, connections, and users.

Integrating an Active Directory connection or a VPN is simple and easy with CORE's robust administration panel. You'll also be able to quickly and simply handle complicated network setups with a few mouse clicks.

Optional integrations can be found, but they can be useful in your job. This cloud GPU platform offers improved security features, shared storage, and more. Take advantage of the cheap GPUs by receiving educational discounts, billing notifications, or being charged for a single second, etc.

Make your workflow easier and faster for just $0.07/hour.


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