GPU Cloud Solutions: What to Know About Their Benefits?

GPU as a service market size to record a growth rate of more than 40% up to 2027.”  (Source) 

The demand for artificial intelligence is increasing day by day. The adoption of strong computing-intensive technology has become a need of the hour. GPU is a platform that enables computing data without deploying it on local devices. It accelerates computing, deep learning, and data analytical processes.  

Graphic processing units have been helping our PC gamers experience high-end performance for a few years. Apart from this, GPUs assist us with complex machine learning and advanced analytics to suit even video editors, developers, and graphic designers.  


Why Enterprises Prefer GPU As
A Service?

Today even SMEs prefer GPUs to increase speed of mundane workloads – from adding photo filters to trimming videos – GPU profoundly eases graphic-intensive operations. However, enterprises face a significant challenge in deploying and maintaining the Graphic Intensive Infrastructure. This is where a managed solution comes into play. ACE delivers GPU as a service to support enterprises meet challenges posed by advanced machine learning processes. 

While some call its GPU as a service, some tech professionals have also named it- GPU-DaaS. This means that the technology of GPU comes as a managed solution with fully managed desktop infrastructure. A graphics processing unit as a service is a managed GPU that eases the immediate IT complexities and delivers a high-definition experience with the help of remote servers to host the GPU with the help of a network connection. These remote servers store, manage, and process data without the need for the local graphics card and on-premise hardware. Earlier used for video rendering and developers, the on-cloud-GPU is becoming more popular as a fully managed service.  

Here are a few benefits it offers to the enterprises. 

  • Reduced costs, both front-head and infrastructure 
  • Pay-as-you-go for computing 
  • Higher level of scalability 
  • Full-time availability 
  • Increased device lifespan 
  • Ability to work on computing apps efficiently 
  • FAST Services (Feasible, Accessible, Secure and Trusted) 

GPU As a Service: Fostering the Future of Computing  

GPUs are the core unit when working in analytics and machine learning. With the tremendous growth of managed cloud services, GPUs are integrating well into the high computing world in AI. GPUs integrated with the cloud can transform the future of cloud computing. Moving GPU to the cloud enables the user (engineer or a designer) to have next-level flexibility and compute at their fingertips at a relatively economical cost. Second, GPU as a service also makes space for specialization by eliminating the need to maintain existing cloud infrastructure.  

Suggested reading: The New Wave of Cloud GPUs: Revolutionizing the Business Landscape

Make A Way to Hassle-Free Experience with GPU DaaS

Having managed on-the-go GPU solution frees the enterprises from installing massive servers and workstations for running GPU within office premises. Thus, GPU DaaS solution is here to help you out. It gives you the best performance by running on best-in-class infrastructure with NVIDIA RTX 8000 Graphics. When GPUs combine with the cloud, they operate on Advanced Solid-State Drives (SSDs) to deliver enhanced business agility, thereby, reducing overall TCO for maintaining GPUs.  

Also read: Desktop As A Service- Myths And Facts 

Long Term Cost Savings with Cloud GPU 

Having a traditional model of GPU necessitates substantial front head investment in purchasing concrete machines and server instances. However, by opting for GPU as a service model, you can substitute these instances with accelerated computing nodes that can get shared among up to 5 instances or customized as per your needs. The GPU cloud brings flexible pay per use pricing options along with scalability and guaranteed uptime. Therefore, it results in cost reduction in the long run. Moreover, GPU on the cloud reduces capital expenditure on end-points, delivering predictable performance even on low spec devices.  

Moving Towards Automated Provisioning Processes  

Traditional provisioning is a manual process; therefore, it can take days or even weeks to configure, whereas the GPU DaaS model can be achieved within a few hours with the help of virtual images. The firms looking to expand their workforce or having dynamic needs can have GPUs DaaS that are fully scalable to meet short peak durations.  

Mobility And Collaboration in IT Infrastructure  

An ideal scenario consists of secure remote access from any location —powered by the cloud. This further means that the organization’s challenge of mobility is resolved. And the workforce can seamlessly collaborate in the IT ecosystem with cloud-based GPUs. This implies that the firms can hire talents from across the globe who can perform on reliable GPU infrastructure. Moreover, the data is protected against Distributed Denial-of-Service attacks and more with channelized layers of security.  

Reducing Latency to Operate Computing Apps  

Latency restricts performance when a user is working on high-end computing apps. Business-critical processes such as voice modulation, and retail customer analytics demand real-time performance, that is achievable only with the lowest possible latency. To significantly reduce latency, enterprises leverage GPU as a service that enhances application response to improve performance.  

Major Benefit to the Developers

Coding for GPU processing is not everyone’s cup of tea. For that, you need experienced IT leaders to give a set of tools to manage GPU focused programming so that multiple users can leverage the power of GPUs. Thus, moving GPUs to the cloud brings managed GPUs to the table with an optimized platform to empower computing apps for the workforce.  

The Bottom Line: Switch to GPU as a Service Now 

In today’s agile development world, we need to make computing accelerated to cope with the massive digital transformation. Artificial intelligence has evolved enough to make firms anxious to make a leap to the cloud GPUs. Before you move to cloud GPU DaaS, contemplate the benefits you could get out of it. With the growth of remote working in IoT, having on-demand access to resources along with the application performance. The wave of GPU computing comes with lower costs and higher scalability when hosted on the cloud.  

Switch to ACE-managed GPU as a Service with Advanced Solid-State Drives (SSDs) for improved speed and performance. ACE is an experienced cloud service provider with 14+ years in the market that offers NVIDIA Quadro RTX 8000 GPUs for an unparalleled visual experience with multi-layered security.