Enterprises are always looking for ways to use their resources efficiently. The advantages cloud computing delivers in this regard are no secret anymore.
Physically maintaining on-premise computation and storage requires a professional team for maintaining, scaling, and orchestrating various IT systems. All these increase the overall maintenance budget for the enterprise. Instead, the enterprise can pay half the price to the cloud provider to manage these. Thus, liberated from IT management responsibilities, enterprises can shift their focus to rendering data-intensive tasks more efficiently – after all, data is the new oil!
In this Artificial Intelligence (AI) & data-driven business era, adopting robust computation through high-end processors has become crucial. This is where GPU-accelerated computing can be beneficial. Enterprises use Cloud GPUs in game development, Machine Learning (ML), Big Data Analysis, and web applications rendering, among other applications.
According to the Industry Arc research report, GPU-accelerated analytics market will project a compound annual growth rate (CAGR) of 15.6% during the 2022-27 period and reach USD 303 million by 2027. Within this market, Cloud GPU deployment will grow at a CAGR of 16.2%, thanks to the operational flexibility and ease of real-time deployment that it offers to enterprises.
This article will give you a comprehensive understanding of how Cloud GPU delivers various advantages for businesses and increase productivity.
Table of Contents
What are GPUs?
Graphical Processing Units are electronic devices having multiple semiconductor chips and circuits. Traditionally, these helped CPUs render 2D- and 3D images and animations in videos, games, 3D applications, etc. But now, their use range is no longer limited to high-end graphics rendering but has stretched over multiple domains. They are being deployed across industries to undertake computational processing through rapid mathematical calculations. A Statista report predicts that the global GPU market will balloon to USD 246.51 billion by 2028 (from USD 25.41 billion in 2020).
Whereas CPUs are the fundamental part of the computer and manage all the different applications that run within the system, GPUs do not come into action in every process. A CPU may run antivirus scans in the background while playing a movie on the screen. But a GPU only helps perform tasks involving a significant amount of computation in less time.
At the point where a CPU’s processing starts flickering, the GPU comes to the rescue. Today, high-end parallel processing, 4K graphics rendering with high refresh rate, training machine learning (ML) and deep learning (DL) models, analyzing large datasets, real-time cloud gaming, cryptocurrency development, and mining require GPU processing power.
Experience Lightning-fast Computing Power with Cloud-based GPU Resources
What is Cloud GPU?
Cloud GPU(s) or Cloud-based Graphical Processing Units are high-performance GPU clusters that reside in the cloud. They provide hardware acceleration for different applications without needing GPU to be physically deployed in the local device.
Enterprises and individuals now prefer using Cloud GPUs rather than purchasing hardware because it is cost-effective and scalable according to the current requirements. Let’s deep dive into various other advantages Cloud GPUs can provide to an enterprise –
Rapid deployment and scale-up
Procuring and establishing on-prem GPU system to render data-intensive applications can involve months of intensive efforts and headache,and impose unbearable time delays if not preceded by visionary planning and foresight.
Cloud GPUs simply bypass the entire setting-up and maintaining phase! The cloud provider will enable a dashboard for the client, and the enterprise can configure it from anywhere. It saves time.
Cloud is known for its cost-effectiveness because it can cater to multiple tenants simultaneously. Also, single setup and maintenance by a Cloud Service Provider (CSP) can serve numerous clients, even if these clients need dedicated services.
The Cloud GPU service is no different. The pay-as-you-go model makes it extremely cheap for enterprise customers and individuals to rent Cloud GPU services, especially when they require GPU resources for limited purpose or duration.
Renting a Cloud GPU not only eliminates enormous capital expenditure and wastage of hardware and physical storage space, but is also extremely environment-friendly in terms of power consumption savings. Furthermore, enterprises need not recruit and train a dedicated team of professionals to manage on-prem GPU and associated IT resources.
Computational workload acceleration
Often businesses and individuals need to compute large mathematical models, perform massive statistical analytics, and analyze extensive datasets. For making the computation seamless and time-saving, enterprises prefer leveraging Cloud GPUs.
Again, it makes corporate sense to deploy multiple GPUs rather than multiple CPUs because even slightest delay in the modeling can precipitate subsequent delays in the time-to-market the product/ application.
For the gaming industry as well, flickering or lagging in games can lead to the deterioration of customers’/ gamers’ experience, or cause grievances in online tournaments.
Time, undoubtedly, plays a significant role, and deploying a GPU can help save your day.
Whether scaling vertically or horizontally, adding more GPUs to the existing infrastructure entails astronomical financial costs and significant manpower resources.
Flawless scalability is the most alluring advantage of deploying Cloud GPUs – the CSP simply provides each client a dashboard from where they can easily add more GPUs on command as and when required. Downsizing deployed GPU resources is equally effortless.
This opens up numerous avenues for businesses and customers to grab larger contracts without learning much about GPUs and infrastructure upgrades.
Free local resources
Training an enormous ML model or visual rendering genome sequences on local hardware systems can cause the latter to slow down, become unusable or terminate abruptly if they cannot scale on demand.
Outsourcing computation and high-end processing to Cloud GPUs enables enterprises/ individuals to perform their heavy-duty work with ease, while simultaneously extricating the limited local resources from undertaking these unbearable workloads.
Time-efficient algorithm modeling
Parallel processing is an essential concept that streamlines computational workflow. To train robust ML/DL algorithms, enterprises rent Cloud GPUs. With multiple cores performing parallel processing, Cloud GPUs can efficiently accomplish ML and DL modeling in a fraction of time vis-a-vis months taken by an array of tens or even hundreds of CPUs!
Developing an AI-enabled traffic management system for provincial governments entails different levels of computation (traffic streams, speed limits, directionality, weather systems, etc.) when compared to developing a cryptocurrency that requires each transaction to be validated by billions of mathematical calculations across a blockchain-based decentralized, globally distributed ledger.
Depending on workload specifications and various performance metrics, end customers can choose the GPU instance (say, Nvidia A100 or A2) as well as configure the RAM and dedicated storage resources required.
And they can accomplish this at a click of the mouse through the Cloud GPU admin panel without necessarily learning hardcore programming skills. A supremely versatile model = measurably better customer experience.
Cloud GPU applications
Enterprises that leverage Cloud GPUs mostly run extensive applications and algorithms that regular CPUs cannot manage on local systems or take excessively long to accomplish.
Some research-intensive and data-driven applications for which enterprises and individuals prefer using Cloud GPUs include –
- Pattern recognition – A core operation underlying many research-intensive applications such as cloud-hosted cybersecurity systems (IAM & Biometrics), real-time data analytics, image matching, object identification, facial recognition, weather forecasting, agricultural improvement, disease control, crowd management, etc.
- Genetic sequencing – Like technology and finance sectors, healthcare industry has also started leveraging data-driven research. Genome sequencing, protein structure, drug behavior simulation, and other healthcare analyses ingest massive amounts of medical and genomic data. Genetic sequencing of specific organisms has come to occupy a central role in disease prevention and control, as witnessed during SARS, Ebola and Covid-19 pandemics. Such capital intensive and time-sensitive workloads involving remarkably massive data sets can be efficiently undertaken by deploying Cloud GPUs.
- Training ML models with large datasets – All comprehensive ML and inference systems are founded on the bedrock of extensive datasets which need to be collated, sieved, rearranged and referenced. Graph Neural Networks (GNNs) often come into play where millions of entities and billions of relationships between them have to be cross-referenced. Cloud GPUs not only impart coherence and dynamic scalability during ML/ DL training, but also facilitate effortless scaling-up when humongous datasets from multiple disparate sources need to be aggregated into a single high-performing training model.
- Visualization workload enhancement – Powerful enterprise applications often require high processing and can be very graphically-demanding to run. Cloud GPUs accelerate these streaming, rendering, video encoding, and Computer-Aided Design (CAD) applications.
In today’s fast-growing and agile market, to stay ahead of the cut-throat competition, you must leverage flexible and cost-effective solutions that can impart seamless processing power without catastrophically shooting your budget to unreachable heights.
The breakneck growth of AI/ML, emergence of Metaverse, Cloud gaming, remote employment models, BYOD workspaces, the meteoric rise of IoT-enabled infrastructure management, etc., have pushed enterprises to evolve and innovate and competitively leap into the embrace of Cloud GPUs.
On-demand access to computing resources & high-performance IaaS/ PaaS functionalities to customers are among other notable reasons that modern companies are switching towards Cloud GPUs.
Ace Cloud Hosting is a top-notch Cloud GPU Provider with 14+ years’ experience in this niche market. Ace’s expertise in providing cutting-edge technologies and all-around pleasant customer experience was recognized recently with the “2022 Most Innovative Cloud Solutions Provider – India” award.
You may also like:
- Cloud GPUs: The Cornerstone of Modern AI
- The Evolution of the GPU: How It Became the Heart of AI and ML
- NVIDIA A30: The Workhorse of AI and HPC in the Data Center
- Harness the Power of GPUs to Accelerate Analytics Processing
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