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Cloud GPUs are in great demand as new technologies such as deep learning, A.I., and machine learning emerge.
GPUs (Graphics Processing Units) are specialized processors made to handle the enormous data sets and intricate computations needed for activities like computer graphics and gaming. They are now crucial for the field of artificial intelligence (A.I.) as well, though, as of late.
Traditional CPUs (Central Processing Units) are unable to meet the demand since A.I. models require a significant amount of computing power to train and run.
On the other hand, since they are built to support parallel processing, GPUs are more effective at managing the massive volumes of data and intricate computations needed for A.I. operations.
If you are in tech, you must have heard about cloud GPU services. For those, who won’t follow along.
A cloud-based service that gives users online access to potent GPU resources is known as a cloud GPU service for AI. Instead of needing to buy and maintain their own GPU hardware, these services enable customers to rent GPU resources as needed.
This makes it feasible for people and organizations to use the GPUs’ processing capacity without having to make a sizable initial investment or pay continuing maintenance fees.
The majority of cloud GPU services for AI provide a broad variety of GPU options, including several models and configurations and the flexibility to scale up or down as necessary. You can now select the ideal GPU resources for their unique requirements and projects thanks to this service.
Additionally, the services frequently come with other capabilities including networking, storage, and software tools to aid customers in their AI projects. In this post, we will look at the top cloud GPU services for AI.
Linode offers GPUs on demand for workloads like video processing, scientific computing, machine learning, artificial intelligence, and others.
It provides GPU-optimized virtual machines (VMs) that run on NVIDIA Quadro RTX 6000, Tensor, and RT cores and uses CUDA capability to do deep learning, ray tracing workloads, and complex calculations.
By turning your capital expenditure into an operating expense, you can take use of Linode GPU’s GPU capability and the real value of the cloud.
Additionally, Linode frees you from having to worry about the hardware so you can concentrate on your strongest skills.
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 can receive up to 4 cards for each instance.
Pricing starts at $1.5/hour for the dedicated RTX6000 GPU plan. Get started with Linode for free.
2. Vast AI
Vast AI is a global marketplace where users can rent inexpensive GPUs for use in high-performance computing.
They reduce the cost of computationally heavy tasks by enabling hosts to lease out their GPU hardware, enabling users to utilize their online search tool to locate the cheapest prices for computing in accordance with their needs and execute commands or launch SSH connections.
They provide SSH instances, Jupyter instances with the Jupyter GUI, or command-only instances, and feature a straightforward user interface.
A deep learning performance function (DLPerf), which forecasts an estimated deep learning task performance, is also provided.
The systems used by Vast AI are Ubuntu-based and do not offer remote desktops. They also operate instances that are available on demand for a specific fee established by the host.
Pricing starts at $0.80/hour for 4x RTX A6000.
3. AWS & NVIDIA
Together, AWS and NVIDIA are consistently delivering GPU-based solutions that are affordable, adaptable, and powerful.
It includes NVIDIA GPU-powered Amazon EC2 instances and services like AWS IoT Greengrass that installs with 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, utilize 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 other different instances available for certain circumstances, such as Amazon EC2 P3, Amazon EC2 G4, etc.
You can apply for a free trial and please contact the vendor for its pricing.
A CORE is a fully managed cloud GPU platform created by Paperspace that provides straightforward, cost-effective, and accelerated computing for a variety of applications.
Utilize the cutting-edge accelerated computing infrastructure provided by Paperspace CORE to speed up organizational processes. For quick onboarding, collaborative tools, and desktop applications for Mac, Linux, and Windows, it offers a user-friendly and uncomplicated interface.
Use it to execute demanding programs on an infinite amount of processing power. CORE offers a super-fast network, quick provisioning, support for 3D apps, and a complete API for programmatic access.
With an easy-to-use and intuitive GUI, get a comprehensive picture of your infrastructure in one location.
Additionally, gain excellent control thanks to the CORE’s administration interface, which offers powerful tools and enables you to filter, sort, connect, or create machines, networks, and users.
Pricing starts at $0.45/hour for the dedicated M4000 GPU.
Alibaba Elastic GPU Service (EGS) leverages GPU technology to deliver parallel and powerful processing capabilities. Many applications, including video processing, visualization, scientific computing, and deep learning, are well suited for it.
EGS makes use of a number of GPUs, including AMD FirePro S7150, NVIDIA Tesla M40, NVIDIA Tesla V100, NVIDIA Tesla P4, and NVIDIA Tesla P100.
You will have access to advantages including online deep learning inference services and training, content identification, picture and voice recognition, HD media coding, video conferencing, source film restoration, and 4K/8K HD live.
Get alternatives like video rendering, computational finance, climate prediction, collision simulation, genetic engineering, non-linear editing, distant education applications, and engineering design, as well.
It offers a free trial and pay-as-you-go subscription plan, which means you only what instances or GPUs you use.
Genesis Cloud offers a highly cost-effective cloud GPU platform. They are working in partnership with several effective data centers throughout the world to provide a wide variety of applications.
Genesis Cloud provides a wide range of useful features at no additional cost, including snapshots for backing up your work, security groups for network traffic, storage volumes for large data sets, FastAI, PyTorch, preset pictures, and a public TensorFlow API.
They speed computation with the help of NVIDIA GeForce RTX 3090, RTX 3080, RTX 3060 Ti, and GTX 1080 Ti technologies in their cloud GPU instances.
You can utilize the power of GPU computing to produce animated movies or train neural networks.
Pricing starts at $1.30/hour for NVIDIA® GeForce™ RTX 3090.
Google Cloud Platform (GCP) offers a number of GPU instances that can be used to speed processes like machine learning, deep learning, and high-performance computing (HPC).
Through these GPU instances, which are supported by NVIDIA, AMD, and Intel GPUs, it is possible to access the CUDA and cuDNN libraries, which are frequently used for GPU-accelerated computing.
Because of machine modifications, flexible pricing, and a large range of GPU possibilities, HPC could be more productive.
Additionally, it could help you lighten your workload. Among the GPUs they provide are the NVIDIA K80, P4, V100, A100, T4, and P100. Only a small number of zones dispersed across different places have access to the GPUs.
The cost will vary based on the region, the GPU you select, and the machine type. For the NVIDIA A100, prices start at $3.93 per GPU.
The NVIDIA GPU Cloud (NGC) is a cloud-based platform that gives users access to a variety of GPU-accelerated software, such as visualization tools, HPC programs, and deep learning frameworks.
Developers, data scientists, and researchers can now easily use the computing power of NVIDIA GPUs in the cloud without having to worry about the infrastructure or upkeep needed to operate their apps thanks to NGC.
A library of pre-configured software containers is included with NGC and can be quickly deployed on a number of cloud computing infrastructures, including Amazon Web Services, Google Cloud Platform, and Microsoft Azure.
Access to NGC also gives users access to NVIDIA’s deep learning platform, which comes with a number of programs and resources for creating and implementing deep learning models, including TensorRT, cuDNN, and CUDA-X AI.
Depending on the specific services and resources you use, the NVIDIA GPU Cloud’s (NGC) price changes. While certain NGC services, like the deep learning software stack, are free to use, others, such as the HPC application containers, may charge a price. Please refer to the NGC catalog.
Lambda provides cloud GPU instances for deep learning model training and scalability from a single physical computer to many virtual machines.
From the dashboard, immediately access each machine’s specialized Jupyter Notebook programming environment. For direct access, use SSH directly with one of the SSH keys or connect via the Web Terminal on the cloud dashboard.
A maximum of 10 Gbps of inter-node connectivity is supported by each instance, allowing for distributed training using frameworks like Horovod.
Scaling up the number of GPUs on one or several instances can help speed up model optimization. The platform supports GPU instances from NVIDIA RTX 6000, Quadro RTX 6000, and Tesla V100s.
Pricing starts at $1.10/hour for NVIDIA A100.
10. IBM Cloud GPU
The IBM Cloud GPU utilizes a globally dispersed network of data centers to offer flexible server selection procedures and smooth connectivity with the IBM cloud architecture, APIs, and applications.
With Intel Xeon 4210, Xeon 5218, and Xeon 6248 GPU instances, it offers the bare-metal Server GPU option.
You can execute high-performance, latency-sensitive, specialized, and conventional workloads directly on server hardware using bare-metal instances, just like they could with on-premise GPUs.
For their bare-metal server option, they also provide instances with NVIDIA T4 GPUs and Intel Xeon processors with up to 40 cores, as well as instances for their virtual server alternatives with NVIDIA V100 and P100 models.
Pricing starts at $514/month for Intel Xeon4110.
Finally, the finest cloud GPU services for A.I. are those that offer strong GPU resources, adaptable price choices, and simple scaling.
All of the companies that provide GPU instances for A.I. workloads—AWS, NVIDIA, IBM, and GCP—have excellent product lines in terms of performance and functionality.
The best option is ultimately determined by your unique requirements, financial constraints, infrastructure already in place, and vendor preferences.