Initially designed to enhance computer graphics, the graphics processing unit (GPU) has now become a key component in AI research, 3D modeling, video game development, design, construction, and more.
What is GPU Cloud?
A GPU cloud is a public cloud (IaaS) that provides computing resources such as RAM, various types of storage, and both processing and graphics cores. The inclusion of graphics cores helps address tasks that require enhanced computing power.
GPU stands for Graphics Processing Unit or accelerator, which can be installed on a server. This simplifies the work of companies dealing with graphic applications and content. Data processing using a GPU cloud is faster because it allows for the simultaneous, parallel processing of large volumes of diverse data across multiple threads. A graphics core can perform 10 to 14 times more operations than processing cores (CPU). Additionally, data processing costs are reduced. To perform the same amount of calculations, more processing cores are needed and paid for compared to graphics cores.
This solution is suitable for various business segments, including:
- IT companies;
- Companies engaged in design and modeling;
- Enterprises using VDI;
- Agricultural companies utilizing IoT;
- Retail;
- Mobile operators;
- Research institutes.
Tasks where a cloud with GPUs is a must-have
If any of the points listed below characterize your company’s area of activity, it might be beneficial to consider implementing this solution.
- 3D Modeling
Renting a GPU server allows you to quickly launch complex graphic applications and carry out comprehensive development.
- Design, architecture, and construction
Cloud computing enables you to work on graphic design from any device, securely store and transfer files, and remotely access the necessary applications.
- Deep learning and AI
Deep learning is the backbone of artificial intelligence. It’s an advanced ML approach focusing on representational learning through artificial neural networks (ANNs). A deep learning model is used to process large datasets or highly computational processes.
In 2018, scientists from the US, France, and Germany trained neural networks to recognize images for diagnosing early stages of skin cancer. The machine was shown over 100,000 images of ordinary moles and dangerous melanomas. These photos were then reviewed by dermatologists. As a result, the neural networks outperformed specialists, correctly identifying malignant formations in 95% of cases, while human experts achieved only 86%.
- Working with ML
If your company analyzes large data sets, such as equipment telemetry, IoT sensor data, object movements, purchases, phone calls, and weather data, and uses machine learning, these computations will be faster with graphics cores.
For example, the technology platform Valossa AI extracts information from videos using advanced search and audiovisual content recognition, relying on NVIDIA GPUs on AWS. The largest oil company Schlumberger accelerates HPC modeling with Google Cloud’s graphics cores to map underground oil reservoirs, saving time and costs.
- Video games development and streaming, video rendering online
If your company develops games or creates video content, your employees need powerful computers. These can be replaced with remote workstations on a dedicated server with GPUs.
- Scientific research and experiments
This activity involves collecting large data sets that need to be processed quickly. A powerful GPU cloud server, which can simultaneously perform numerous calculations, can handle this.
Benefits of using this solution for business
Building your own IT infrastructure with graphics cores involves the need to maintain qualified IT personnel to service it and requires significant investments in equipment purchases for future growth, as traditional infrastructure can only be scaled by adding hardware components.
Migration to a cloud with graphics cores comes with several advantages:
- Unlimited computational power on demand. The cloud infrastructure is available 24/7 from anywhere in the world and can be scaled up or down as needed.
- Cost reduction. The TCO of the cloud is lower than owning a personal workstation with a graphics card. On top of that, maintenance costs, expendables, electricity, etc., should be considered.
- Work acceleration. If computations or rendering used to take hours, with graphics cards this time will be reduced to minutes.
- Simplified remote work. 3D modelers, business analysts, and video content creators can work from any location, on any device, and at any time. Companies can attract employees from different countries.
- Data control. Data is stored in a centralized manner, under the supervision of an administrator, rather than on a local machine.
