Cloud GPU Platforms are in high demand due to the introduction of emerging technologies such as deep learning, AI, and ML.
If you work with machine learning (ML), artificial intelligence (AI), 3D visualization, or any kind of heavy computing, how you do GPU computing makes a big difference.
In the past, business deep learning models required a lot of time to compute and train. It wasted their time, was very expensive, caused them storage and space problems, and reduced their productivity.
Modern GPUs aim to solve this problem. They provide quick training for your AI models to handle large computations in parallel and with exceptional performance.
Cloud GPU: What Is It?
Let’s discuss GPUs first to understand Cloud GPU.
A specialized electronic circuit called a graphics processing unit or GPU is used to rapidly change and control memory to speed up the creation of images or graphics.
These days, GPUs are more efficient than CPUs in manipulating images and computer graphics due to their parallel architecture. A GPU can be installed on a PC’s video card, CPU, motherboard, or both.
GPUs, or cloud graphics units, are examples of computers with strong hardware acceleration that are useful for running programs to manage large AI and deep learning workloads in the cloud. It is not necessary to install a physical GPU on your device.
Several well-known GPUs include Radeon, GeForce, AMD, and NVIDIA.
GPUs are employed in:
- Cell phones
- Consoles for games
- Embedded Systems
- Personal PC
Advantages of Cloud GPU 👍
The following are key benefits of using cloud GPUs:
If you want to expand it, your organization’s burden will inevitably increase. A GPU that can keep up with your growing workload is what you’ll need. Cloud GPUs can help you meet your growing workload by allowing you to install more GPUs easily and without any hassle. On the other hand, you can easily scale down if that’s what you want to do.
Instead of buying very expensive real high-power GPUs, you can opt for cloud GPU rentals, which are available at low hourly rates. Unlike real GPUs, which would be expensive even if you don’t use them often, you’ll only be paid for the hours you’ve used cloud GPUs.
Unclear Local Resources
Unlike hardware GPUs, which take up a lot of space on your computer, cloud GPUs don’t use any of your local resources. Not adding, rendering a task or running a large-scale machine learning model slows down your computer.
In response, you might consider outsourcing processing power to the cloud, which you can use easily and without taxing your computer. Instead of putting all the pressure on the computer to handle the load and computational tasks, use it to control everything.
Cloud GPUs allow designers to iterate and render their designs more quickly. When a task that used to take hours or days to complete now only takes minutes, you can save a ton of time. As a result, your staff will be far more productive, freeing up time for you to spend on innovation rather than rendering or calculating.
11 Top Best Cloud GPU Platforms List for AI
Following are the Top Cloud GPU Platforms for AI.
1) Azure N series
The first on the list of Cloud GPU Platforms is Azure N series. GPU support is available for Azure Virtual Machines (VMs) in the Azure N series. For graphics- and compute-intensive tasks, GPUs are perfect. They enable users to accelerate creativity in a variety of contexts, including deep learning, predictive analytics, and remote visualization.
There are distinct N series options for different workloads.
- High-performance machine learning and computing workloads are the main topics of the NC series. NVIDIA’s Tesla V100 GPU is featured in the most recent version, NCsv3.
- The ND series is primarily concerned with training scenarios and inference for deep learning. NVIDIA Tesla P40 GPUs are used in it. NDv2, the most recent version, is compatible with NVIDIA Tesla V100 GPUs.
- The NVIDIA Tesla M60 GPU powers the NV series, which focuses on workloads involving remote visualization and other demanding applications.
- The InfiniBand connectivity that the NC, NCsv3, NDs, and NCsv2 virtual machines provide allows for scale-up performance. This place offers advantages such as deep learning, gaming, video editing, and graphics rendering.
2) IBM Cloud
You can have a lot of GPU options, power, and flexibility with IBM Cloud. Since GPU provides the additional intelligence that a CPU lacks, IBM Cloud gives you direct access to a more accessible server option for smooth interaction with the architecture, apps, and APIs of the cloud, as well as a dispersed worldwide network of data centers.
- Options for the bare metal server GPU include an NVIDIA T4 graphics card, an Intel Xeon 4210 processor with 20 cores, 32 GB of RAM, 2.20 GHz, and 20 TB of bandwidth. In a similar vein, Intel Xeon 5218 and Intel Xeon 6248 are also available possibilities.
- You get an AC1.8×60 virtual server with one P100 GPU, 60 GB RAM, and eight virtual CPUs. Additionally, you can find the AC2.8×60 and AC2.8×60 alternatives here.
Avail the virtual server GPU for $1.95/hour and the bare metal server GPU for $819/month, respectively.
3) AWS and NVIDIA
Together, AWS and NVIDIA have been able to consistently provide GPU-based solutions that are affordable, adaptable, and strong. It includes services like AWS IoT Greengrass, which deploys with NVIDIA Jetson Nano modules and GPU-powered Amazon EC2 instances.
For high-performance computing, machine learning (ML), IoT services, and virtual workstations, users utilize AWS and NVIDIA. Amazon EC2 instances, which NVIDIA GPUs power, are responsible for delivering scalable performance. Additionally, to expand AWS cloud services to NVIDIA-based edge devices, utilize AWS IoT Greengrass.
The NVIDIA A100 Tensor Core GPUs power Amazon EC2 P4d instances to deliver industry-leading low latency networking and high throughput. Comparably, for certain use cases, there are a plethora of additional examples accessible, such as Amazon EC2 P3, Amazon EC2 G4, and so on.
Sign up for a FREE trial to explore the GPU’s cutting-edge cloud capability.
OVHcloud provides cloud servers designed to process large workloads. Several GPUs are integrated with NVIDIA Tesla V100 graphics processors to support machine learning and deep learning applications.
They help speed up computers in the graphic computing industry as well as artificial intelligence. OVH partners with NVIDIA to offer the best GPU-accelerated platform for high-performance computing, AI, and deep learning.
Use a comprehensive catalog to find the easiest way to install and manage GPU-accelerated containers. It bypasses the virtualization layer and sends one of the four cards directly to the instances via PCI passthrough, allowing you to harness the full power.
The infrastructure and services provided by OVHcloud are certified to ISO/IEC 27017, 27001, 27701, and 27018. The certifications demonstrate that OVHcloud has an Information Security Management System (ISMS) in place to manage risks, enforce business continuity and manage risk. Privacy Information Management System (PIMS).
Additionally, the NVIDIA Tesla V100 includes key features such as PCIe 32 GB/s, 16 GB HBM2 capacity, 900 GB/s bandwidth, double precision-7 teraFLOPs, single precision-14 teraFLOPs, and deep learning-112 esters.
5) Lambda GPU
Train deep learning, ML, and AI models with Lambda GPU Cloud and scale from a machine to a total number of VMs in a matter of clicks. Get the latest version of the Lambda stack, including the deep learning framework and CUDA drivers, which are pre-installed with the main framework.
Instantly access a dedicated Jupyter Notebook development environment for each machine from the dashboard. For direct access, use SSH directly with one of the SSH keys or establish a connection through the web terminal in the cloud dashboard.
A maximum of 10 Gbps inter-node bandwidth is supported by each instance, allowing for distributed training using frameworks such as Horwood. In single or many instances, scaling the number of GPUs can help save time when optimizing models.
You can even reduce cloud TCO and computing costs by up to 50% with Lambda GPU Cloud, and there are never any multi-year commitments. For just $1.25 per hour, use an RTX 6000 GPU with six VCPUs, 46 GB of RAM, and 658 GB of temporary storage. To get on-demand pricing for your usage, choose from different instances based on your needs.
6) Genesis Cloud
Buy an efficient cloud GPU platform at a remarkably low price from Genesis Cloud. They are working with multiple efficient data centers located all over the world to deliver a wide array of applications.
All services are robust, scalable, automated, and safe. For use in massive data processing, visual effects, machine learning, transcoding or storage, and many more applications, Genesis Cloud offers infinite GPU computing power.
Many extensive capabilities, such as storage volumes for large datasets, security groups for network traffic, preset images, FastAI, PyTorch, and a public TensorFlow API, are all available for free with Genesis Cloud.
It has a variety of NVIDIA and AMD GPUs. Moreover, you can use GPU computational power to create animated movies or train neural networks. In order to minimize carbon emissions from geothermal sources, its data centers operate entirely on renewable energy.
Compared to other providers, their prices are 85% lower because you only pay for increased minute levels. Additionally, long-term and precautionary discounts can help you save even more.
For workloads that require parallel processing, including AI, machine learning, scientific computing, video processing, and more, Linode provides GPUs on demand. It offers GPU-optimized virtual machines (VMs) powered by Tensor, RT cores, and NVIDIA Quadro RTX 6000, and it uses CUDA capabilities to run complex processing, deep learning, and ray tracing tasks.
Transform your capital costs into operational costs by harnessing the GPU power of Linux GPUs and reap the rewards of the cloud’s true value proposition. Furthermore, Linode relieves you of hardware concerns so you may concentrate on your primary competencies.
For sophisticated use cases like AI, machine learning, and video streaming, the barrier to using GPUs has been removed. Additionally, the number of cards you receive for each instance will vary based on the processing power required for the expected workload.
With 4,608 CUDA cores, 576 Tensor cores, 72 RT cores, 24 GB GPU memory dedicated to GDDR6, 84T RTX-OPS, 10 Giga Rays/sec Rays Cast, and 16.3 TFLOPs of FP32 performance, the Quadro is a powerful G6PU00X.
The Dedicated + RTX6000 GPU plan costs $1.50 per hour.
Latitude.sh is a revolutionary cloud GPU platform built to optimize AI and machine learning workloads. Latitude.sh’s infrastructure, powered by NVIDIA’s H100 GPUs, provides twice as fast model training as competing GPUs such as the A100.
By using Latitude.sh, you can independently install high-performance dedicated servers in more than 18 countries, guaranteeing optimal performance and low latency.
With deep learning technologies like TensorFlow, PyTorch, and Jupyter, each instance is tailored for AI workloads. Eliminate messing with complex configurations; Just start and move on.
Latitude.sh’s API-first approach streamlines automation and facilitates seamless integration with tools like Terraform. The intuitive interface of Latitude.sh allows you to add resources, manage projects, and create feedback with only a few clicks, enabling you to complete more tasks.
Superior examples from Latitude. It has eight NVIDIA H100 80GB NVLink GPUs, two AMD 9354 processors, 64 cores at 3.25 GHz, and fifteen hundred and thirty-six GB of RAM for performance enthusiasts. In order to save money, on-demand pricing begins at $17.6 per hour.
You can make the most of your AI and ML applications by utilizing Latitude.sh, the most effective and scalable cloud GPU platform there is.
9) Paperspace CORE
Paperspace CORE’s cutting-edge, high-speed computing infrastructure allows for easy onboarding, collaboration features, and desktop Mac, Linux, and Windows applications to help you expedite organizational workflow. Use its unlimited processing power to run apps with high demand.
CORE offers a full programmatic access API, lightning-fast networks, faster rendering, and support for 3D apps. Get a comprehensive picture of your infrastructure in one place with an easy-to-use and intuitive GUI. Additionally, you can filter, configure, connect, or configure machines, networks, and users using the powerful capabilities in the CORE administration interface, giving you exceptional control.
The robust management panel in CORE accomplishes tasks such as quickly enabling VPN and Active Directory connectivity. Additionally, you can handle complex network configurations quickly and easily with just a few clicks.
In addition, there are many optional connectors that will be useful for you in your work. With this cloud GPU platform, you can get shared storage, enhanced security features and more. Take advantage of affordable GPUs by getting billing alerts, schooling discounts, one-time billing, and more.
Streamline the process and make it faster for as little as $0.07 per hour.
10) Google Cloud GPUs
With Google Cloud GPUs, you can get powerful GPUs for machine learning, 3D visualization, and scientific computing. Machine customization’s and variable pricing can help accelerate HPC, allow you to choose from different GPUs to meet price points and performance, and reduce your workload.
Many GPUs, including the NVIDIA K80, P4, V100, A100, T4, and P100, are also available from them. Additionally, Google Cloud GPUs balance memory, processor, and high-performance disk, plus up to 8 GPUs per instance for each workload.
You also have access to the best networking, data analytics, and storage available in the business. Some regions only have specific zones where GPU devices are available. The region, the GPU you choose, and the type of system you buy will all affect the cost. By entering your requirements into the Google Cloud Pricing Calculator, you can determine your price.
Alternatively, consider these options:
11) Elastic GPU Service
The last on the list of Cloud GPU Platforms is, Elastic GPU Service (EGS) leverages GPU technology to offer robust and parallel processing capabilities. For many applications, including deep learning, scientific computing, visualization, and video processing, it is excellent. Several GPUs are used by EGS, including AMD FirePro S7150, NVIDIA Tesla M40, NVIDIA Tesla V100, NVIDIA Tesla P4, and NVIDIA Tesla P100.
Benefits include source film repair, 4K/8K HD live, video conferencing, image and voice recognition, content recognition, HD media coding, and online deep learning evaluation services and training.
Additionally, you can choose from electives in the fields of computational finance, engineering design, genetic engineering, non-linear editing, climate prediction, collision simulation, and video rendering.
- Up to four AMD FirePro S7150 GPUs, 160 GB of RAM, and up to 56 vCPUs are available on the GA1 instance. It has 32 GB of GPU memory and 8192 cores, which work in parallel to create 15 single-precision TFLOPS and one double-precision TFLOPS.
- Up to two NVIDIA Tesla M40 GPUs, 96 GB of RAM, and 56 vCPUs are available with a GN4 instance. With its 24 GB GPU memory and 6000 cores, it can achieve 14 TFLOPS of single-precision performance. In a similar vein, several examples can be found, including the GN5, GN5i, and GN6.
- Within the EGS, network bandwidths of up to 2,000,000 PPS and 25 Gbit/s are supported internally to give computing nodes the best possible network performance. It has a fast local cache connected via Ultra Cloud or SSD drives.
- High-performance NVMe drives have a read bandwidth of 1900 Mbit/s and a write bandwidth of 1100 Mbit/s and can handle 230,000 IOPS with an I/O latency of 200 𝝻s.
Depending on what you need, you can choose from a variety of purchasing alternatives to get resources and pay only for them.
That’s it for the list of Cloud GPU Platforms.
The performance, speed, scalability, space and simplicity provided by Cloud GPU Platforms are remarkable. So, consider adopting your favorite cloud GPU platform with out-of-the-box features to accelerate your deep learning models and efficiently handle AI workloads.