11 best cloud GPU platforms for AI and large-scale workloads

Emerging technologies such as deep learning, AI, and ML are driving the demand for cloud GPUs.

If your organization deals with 3D visualization, machine learning (ML), artificial intelligence (AI), or some type of heavy computing, how you perform GPU calculations becomes very important.

Traditionally, deep learning models within organizations require enormous amounts of time to train and compute tasks. Previously, it was wasting time, costing a lot of money, creating storage and space issues, and reducing productivity.

New-age GPUs are designed to solve this problem. They efficiently perform large amounts of computation and speed up training of AI models.

According to Indigo research, GPUs can provide 250 times faster performance than CPUs when training neural networks related to deep learning.

And with advances in cloud computing, cloud GPUs have emerged to provide even faster performance, easier maintenance, lower costs, faster scaling, and time savings for data science and other emerging technologies. We are transforming the world.

This article introduces the concept of cloud GPUs, their relationship to AI, ML, and deep learning, and some of the best cloud GPU platforms you can find to deploy your favorite cloud GPU.

Let’s get started!

What is cloud GPU?

To understand cloud GPUs, let’s first talk about GPUs.

A graphics processing unit (GPU) refers to a specialized electronic circuit that is used to quickly modify and manipulate memory to speed up the creation of images and graphics.

Modern GPUs are more parallel than central processing units (CPUs), making them more efficient for image processing and computer graphics operations. The GPU can be built into the motherboard or placed on your PC’s video card or CPU die.

A cloud graphics unit (GPU) is a computer instance with robust hardware acceleration that helps you run applications that process large-scale AI and deep learning workloads in the cloud. There is no need to deploy a physical GPU on the device.

Popular GPUs include NVIDIA, AMD, Radeon, and GeForce.

GPUs are used for:

• mobile phone
• game machine
• workstation
• embedded system
• computer

GPU usage:

Here are some examples of using GPUs.

• AI and ML for image recognition
• 3D computer graphics and CAD drawing calculations
• Texture mapping and polygon rendering
• Geometric calculations such as translation and rotation of vertices to a coordinate system
• Programmable shader support for working with textures and vertices
• GPU-accelerated video encoding, decoding, and streaming
• Graphics-rich games and cloud gaming
• Extensive mathematical modeling, analysis, and deep learning that requires the parallel processing capabilities of general-purpose GPUs.
• Video editing, graphic design, content creation

What are the benefits of cloud GPUs? 👍

The main benefits of using cloud GPUs are:

Highly scalable

As you try to expand your organization, your workload will eventually increase. You need a GPU that can scale as your workload grows. Cloud GPUs allow you to easily add GPUs without any hassle to accommodate increased workloads. On the other hand, if you want to scale down, you can do this quickly as well.

minimize costs

Instead of buying an incredibly expensive, high-powered physical GPU, you can rent a cloud GPU for a low cost by the hour. Unlike physical GPUs, which can be expensive even if you use them infrequently, cloud GPUs charge you based on the number of hours you use them.

clear local resources

Unlike physical GPUs, which take up large amounts of space on your computer, cloud GPUs do not consume local resources. Needless to say, running large ML models and rendering tasks slows down your computer.

To this end, you can consider outsourcing your computing power to the cloud without putting a strain on your computer, and you can use it with confidence. Instead of putting pressure on your computer to handle workloads and computational tasks, you just use your computer to control everything.

save time

Cloud GPUs give designers the flexibility to reduce render times and iterate quickly. Tasks that previously took hours or days can now be completed in minutes, saving you a lot of time. Therefore, your team will be much more productive, allowing you to invest your time in innovation instead of rendering and computation.

How can GPUs help with deep learning and AI?

Deep learning is the basis of artificial intelligence. It is an advanced ML technique that focuses on representation learning using artificial neural networks (ANN). Deep learning models are used to process large datasets or advanced computational processes.

So how do GPUs come into play?

GPUs are designed to perform parallel computations or multiple computations simultaneously. GPUs can leverage the power of deep learning models to streamline large-scale computational tasks.

Because GPUs have many cores, they offer great parallel processing computations. Additionally, it has higher memory bandwidth to accommodate large amounts of data for deep learning systems. Therefore, it is widely used for training AI models, rendering CAD models, playing graphics-rich video games, etc.

Additionally, if you want to experiment with multiple algorithms simultaneously, you can run many GPUs independently. Facilitates different processes on separate GPUs without parallelism. Therefore, multiple GPUs can be used across different physical machines or on a single machine to distribute heavy data models.

How to get started with cloud GPUs

Getting started with cloud GPUs is not rocket science. In fact, it’s all easy and quick to do once you understand the basics. First of all, you need to choose a cloud GPU provider such as Google Cloud Platform (GCP).

Next, register with GCP. Here you can take advantage of all the standard benefits that come with it, including cloud capabilities, storage options, database management, and integration with applications. You can also use one GPU for free using Google Colboratory, which works like a Jupyter Notebook. Finally, you can start rendering on the GPU for your use case.

So let’s take a look at the different options cloud GPUs have for processing AI and large-scale workloads.

linode

Linode provides on-demand GPUs for parallel processing workloads such as video processing, scientific computing, machine learning, and AI. It offers GPU-optimized VMs accelerated by NVIDIA Quadro RTX 6000, Tensor, and RT Cores to harness the power of CUDA to run ray tracing workloads, deep learning, and complex processing.

Turn capital expenditures into operational costs by accessing and harnessing the power of GPUs with Linode GPUs and benefiting from the true value proposition of the cloud. Additionally, Linode allows you to focus on your core competencies without worrying about hardware.

Linode GPUs eliminate barriers to leveraging complex use cases such as video streaming, AI, and machine learning. Additionally, you can get up to four cards per instance, depending on the horsepower needed for your anticipated workload.

Quadro RTX 6000 features 4,608 CUDA cores, 576 Tensor Cores, 72 RT Cores, 24 GB GDDR6 GPU memory, 84T RTX-OPS, 10 Gigaray/s raycast, and 16.3 TFLOPs of FP32 performance. We are prepared.

The dedicated Plus RTX6000 GPU plan costs $1.5/hour.

latitude.sh

Latitude.sh is a game-changer for cloud GPU platforms, specifically designed to power AI and machine learning workloads. Powered by NVIDIA’s H100 GPU, Latitude.sh’s infrastructure provides up to 2x faster model training compared to competing GPUs such as A100.

When you choose Latitude.sh, you have the freedom to deploy high-performance, dedicated servers in over 18 locations around the world, ensuring minimal latency and optimal performance.

Each instance is optimized for AI workloads and comes preinstalled with deep learning tools such as TensorFlow, PyTorch, and Jupyter. No more fiddling with complicated settings. Just deploy and run.

Latitude.sh’s API-first approach simplifies automation and makes it easy to integrate with tools like Terraform. Latitude.sh’s intuitive dashboard lets you do more by creating views, managing projects, and adding resources with just a few clicks.

For performance-minded users, Latitude.sh’s top instance boasts up to eight NVIDIA H100 80GB NVLink GPUs, dual AMD 9354s, 64 cores @ 3.25 GHz, and 1536 GB RAM. And for those who have a lot of work to do, on-demand rates start at $17.60 per hour.

Unlock the full potential of your AI and ML projects with Latitude.sh , the most efficient and scalable cloud GPU platform.

paper space core

Power your organization’s workflows with next-generation accelerated computing infrastructure powered by Paperspace CORE . It offers an easy-to-use and straightforward interface, and offers simple onboarding, collaboration tools, and desktop apps for Mac, Linux, and Windows. Use it to run high-demand applications through unlimited computing power.

CORE provides ultra-fast networking, instant provisioning, 3D app support, and a complete API for programmatic access. Get a complete view of your infrastructure using an easy and intuitive GUI in one place. Additionally, you get greater control with robust tools and CORE’s management interface that allows you to filter, sort, connect, and create machines, networks, and users.

CORE’s powerful management console quickly performs tasks such as Active Directory integration and adding VPNs. It also allows you to easily manage complex network configurations and get the job done quickly with just a few clicks.

Additionally, you’ll find many integrations that are optional but useful for your work. Get advanced security features, shared drives, and more with this cloud GPU platform. Enjoy low-cost GPUs with educational discounts, billing alerts, per-second billing, and more.

Add simplicity and speed to your workflow for a starting price of $0.07 per hour.

Google Cloud GPU

Get high-performance GPUs for scientific computing, 3D visualization, and machine learning with Google Cloud GPUs . Accelerate your HPC, choose from a wide range of GPUs across price points and performance, and minimize workloads with machine customization and flexible pricing.

We also offer many GPUs such as NVIDIA K80, P4, V100, A100, T4, and P100. In addition, Google Cloud GPU balances memory, processors, high-performance disk, and up to eight GPUs on each instance to suit individual workloads.

Plus, you get access to industry-leading networking, data analytics, and storage. GPU devices are only available in certain zones in some regions. Pricing varies by region, GPU selected, and machine type. You can calculate your pricing by defining your requirements with the Google Cloud Pricing Calculator.

Alternatively, you can choose the following solution:

Elastic GPU service

Elastic GPU Service (EGS) provides parallel and powerful computing capabilities with GPU technology. Ideal for many scenarios such as video processing, visualization, scientific computing, and deep learning. EGS uses multiple GPUs such as NVIDIA Tesla M40, NVIDIA Tesla V100, NVIDIA Tesla P4, NVIDIA Tesla P100, and AMD FirePro S7150.

Benefit from online deep learning inference services and training, content identification, image and audio recognition, HD media coding, video conferencing, source film restoration, 4K/8K HD Live, and more.

Additional options include video rendering, computational finance, climate prediction, crash simulation, genetic engineering, nonlinear editing, distance learning applications, and engineering design.

• GA1 instances offer up to 4 AMD FirePro S7150 GPUs, 160 GB of memory, and 56 vCPUs. It has 8192 cores and 32 GB GPU memory, running in parallel and delivering 15 TFLOPS single precision and 1 TFLOPS double precision.
• GN4 instances offer up to two NVIDIA Tesla M40 GPUs, 96 GB of memory, and 56 vCPUs. It has 6000 cores and 24 GB GPU memory, delivering 14 TFLOPS of single precision. Similarly, you will find many instances of GN5, GN5i, GN6, etc.
• EGS internally supports 25 Gbit/s and up to 2,000,000 PPS of network bandwidth, providing the maximum network performance required by compute nodes. It has a fast local cache attached to an SSD or ultra cloud disk.
• High-performance NVMe drives handle 230,000 IOPS with 200 𝝻s I/O latency and provide 1900 Mbit/s read bandwidth and 1100 Mbit/s write bandwidth.

You can choose from a variety of purchasing options and pay only for what you need to acquire resources.

Azure N series

Azure N-series Azure Virtual Machines (VMs) have GPU capabilities. GPUs are ideal for graphics and compute-intensive workloads, helping users drive innovation through a variety of scenarios such as deep learning, predictive analytics, and remote visualization.

Different N-series products are available for specific workloads.

• The NC series focuses on high-performance machine learning and computing workloads. The latest version is NCsv3, which is powered by NVIDIA’s Tesla V100 GPU.
• The ND series primarily focuses on deep learning inference and training scenarios. Uses NVIDIA Tesla P40 GPU. The latest version is NDv2 with NVIDIA Tesla V100 GPU.
• The NV series focuses on remote visualization and other intensive application workloads leveraging the NVIDIA Tesla M60 GPU.
• NC, NCsv3, NDs, and NCsv2 VMs provide InfiniBand interconnects that allow you to scale up performance. Here you can benefit from deep learning, graphics rendering, video editing, gaming, and more.

IBM Cloud

IBM Cloud offers flexibility, power, and many GPU options. Because the GPU is an additional brain over the CPU, IBM Cloud makes it more accessible to seamlessly integrate with IBM Cloud architecture, applications, and APIs, along with a distributed network of data centers around the world. You will have direct access to server selection.

• Bare metal server GPU options are available, including Intel Xeon 4210, NVIDIA T4 graphics card, 20 cores, 32 GB RAM, 2.20 GHz, 20 TB bandwidth. Similarly, Intel Xeon 5218 and Intel Xeon 6248 options are also available.
• For virtual servers, we offer AC1.8×60 with 8 vCPUs, 60 GB RAM, and 1 x P100 GPU. AC2.8×60 and AC2.8×60 options are also provided here.

Bare metal server GPUs are available at a starting price of $819 per month, and virtual server GPUs are available at a starting price of $1.95 per hour.

AWS and NVIDIA

Together, AWS and NVIDIA continue to deliver cost-effective, flexible, and powerful GPU-based solutions. This includes Amazon EC2 instances with NVIDIA GPUs and services such as AWS IoT Greengrass deployed using NVIDIA Jetson Nano modules.

Users use AWS and NVIDIA for virtual workstations, machine learning (ML), IoT services, and high-performance computing. Amazon EC2 instances powered by NVIDIA GPUs provide scalable performance. Additionally, use AWS IoT Greengrass to extend AWS cloud services to NVIDIA-based edge devices.

NVIDIA A100 Tensor Core GPUs power Amazon EC2 P4d instances, delivering industry-leading low-latency networking and high throughput. Similarly, you can find many other instances for specific scenarios, such as Amazon EC2 P3, Amazon EC2 G4, etc.

Sign up for a free trial and experience the power of GPUs from the cloud to the edge.

OVH cloud

OVHcloud provides cloud servers designed to handle massively parallel workloads. GPUs have many instances integrated with NVIDIA Tesla V100 graphics processors to meet your deep learning and machine learning needs.

These help accelerate computing in the graphical computing field and artificial intelligence. OVH has partnered with NVIDIA to provide the ideal GPU acceleration platform for high performance computing, AI, and deep learning.

Use the easiest way to deploy and maintain GPU-accelerated containers through our complete catalog. You can present one of the four cards directly to your instance via PCI passthrough, without the need for a virtualization layer that dedicates all the privileges to its use.

OVHcloud’s services and infrastructure are ISO/IEC 27017, 27001, 27701, and 27018 certified. This certification indicates that OVHcloud has an Information Security Management System (ISMS) for vulnerability management, business continuity implementation, risk management, and Privacy Information Management System (PIMS) implementation.

In addition, NVIDIA Tesla V100 has many valuable features such as PCIe 32 GB/s, 16 GB HBM2 capacity, 900 GB/s bandwidth, double precision – 7 teraflops, single precision – 14 teraflops, deep learning – 112 teraflops, etc. It has several functions.

Lambda GPU

Train deep learning, ML, and AI models using Lambda GPU Cloud and scale from machines to a total number of VMs with just a few clicks. Get the latest version of the lambda stack, including preinstalled key frameworks and CUDA drivers and deep learning frameworks.

Quickly access each machine’s dedicated Jupyter Notebook development environment from the dashboard. Access it directly using SSH using one of your SSH keys, or by connecting through the cloud dashboard’s web terminal.

All instances support up to 10 Gbps of inter-node bandwidth, enabling distributed training using frameworks like Horovod. You can also save time optimizing your model by scaling to the number of GPUs for a single or many instances.

With Lambda GPU Cloud, you can also save 50% on compute and reduce cloud TCO, without the need for multi-year commitments. Get one RTX 6000 GPU with 6 VCPUs, 46 GiB RAM, and 658 GiB ephemeral storage for just $1.25 per hour. Choose from many instances according to your requirements and get on-demand pricing based on your usage.

genesis cloud

Get an efficient cloud GPU platform at a very affordable price from Genesis Cloud . They have access to many efficient data centers around the world and work with these data centers to provide a wide range of applications.

All services are secure, scalable, robust, and automated. Genesis Cloud provides unlimited GPU computing power for visual effects, machine learning, transcoding or storage, big data analytics, and more.

Genesis Cloud has many rich features, including snapshots to save your work, security groups for network traffic, storage volumes for big data sets, FastAI, PyTorch, preconfigured images, and public APIs for TensorFlow. will be provided free of charge.

It comes with various types of NVIDIA and AMD GPUs. Additionally, you can harness the power of GPU computing to train neural networks and generate animated movies. The company’s data centers run on 100% renewable energy from geothermal sources to reduce carbon emissions.

You pay by the minute, which is 85% cheaper than other providers. You can also save even more with long-term and advance discounts.

Conclusion👩‍🏫

Cloud GPUs are designed to provide incredible performance, speed, scaling, space, and convenience. Therefore, consider choosing your preferred cloud GPU platform with out-of-the-box features to accelerate your deep learning models and handle your AI workloads with ease.