GPU systems are blazing fast calculating machines. A cloud server fitted with a GPU is known as a GPU cloud server. In an organisation, a GPU cloud server will deliver optimum results during high workloads. But choosing between a GPU cloud server and an on-premise GPU server is often confusing. Here we will discuss everything about GPU cloud servers and on-premise GPU servers in detail.
What is a GPU?
Graphics Processing Unit or simply GPU is an exponentially evolving technology with a wide range of benefits. It is a type of processor that was specifically designed for the use of graphic cards. GPU is an electronic circuit that offers faster computing and increased efficiency as compared to traditional CPUs. It is most commonly used for handling 3D graphics, data analysis, machine learning, deep learning, and GPU cloud. GPU is a powerful parallel processing system that enables graphic programmers to design and make dynamic visuals.
Difference between GPU and CPU
The basic difference between a CPU and GPU is the number of cores on each chip. A GPU server can perform several basic computing tasks at once. However, a CPU might not give optimum results under bulk load. A GPU is designed to rapidly render high-quality images and videos simultaneously. However, a CPU is designed to handle a wide range of tasks faster but it is limited in the simultaneous running tasks.
Choosing between a GPU cloud server and an on-premise GPU server is like choosing between renting and buying a house. A GPU-powered data centre and on-premise GPU server deliver fantastic performances as compared to a traditional cloud server. Moreover, such systems need fewer servers and have high computation, storage & processing abilities. If your organisation is associated with training deep neural networks or multi-layer neural networks then a GPU server can be highly beneficial. Now based on the cost, performance & operations, we will deeply compare both servers.
Developing, training AI and artificial neural models with large data sets have high operating costs. During each iteration of model training, the developers need to be attentive. These situations create less space for the developers and programmers to experiment and tune. However, an on-premise GPU server will offer a wide range of iteration capabilities and testing time to the developers at a fixed cost.
An on-premise GPU will not count the number of hours an employee is working on the systems. In contrast, a GPU cloud serverwill keep the count of the working hours and execution. It happens because GPU cloud servers stay at different locations and CSP offers a pay-as-you-go pricing model.
Working on ML and DL training models and algorithms or rendering high-resolution videos can be done effectively on an on-premise GPU server. It offers a wide range of features and facilities as compared to a GPU cloud server. A GPU-powered data centre is suitable for basic level modelling and training. A few GPUs provide double-precision while some provide single-precision value. However, it depends on your requirement whether you need that additional value to calculate the model with high precision.
Organisations find it hard to manage on-premise servers. They might not have an in-house team of IT professionals to configure the GPU-based cloud infrastructure for optimised performance. Such organisations will go for GPU cloud over GPU on-premise servers. Furthermore, GPU cloud servers will offer balanced processing speed with precise memory and high disk performance.
Operation is the only area where the GPU cloud server will overtake the on-premise GPU servers. An on-premise GPU server comes with additional operational problems. Problems like network issues, equipment failure, power out, low or no drive space, driver installation, and other common issues.
GPU cloud servers are more stable and do not face such issues. CSPs manage cloud servers thus they will take care of all the operational issues. CSPs will never make their consumer realise any downtime or service failure. A cloud-based GPU decreases the headache of fixing the issues that comes with an on-premise GPU server.
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