Cloud GPUs - When to and when not to use it
For organisations dealing with heavy computing technologies such as Artificial Intelligence(AI), Machine Learning (ML), and 3D visualisation, Graphics Units (GPU) computing plays a major role. Modern GPUs are designed to overcome the issues faced in the deep learning models in the organisations such as longer times, hefty costs, storage issues, and lesser productivity by offering high efficiency for computations and training AI models faster.
The recent Cloud computing advancements have originated the cloud GPUs which are transforming the data world and other emerging technologies.
In this article, we will discuss the instances when a cloud GPU must be used and when it should not. Before that, let us give you a brief look at what a cloud GPU is.
What Is A Cloud GPU?
While the GPUs refer to special electronic circuits for manipulating memory to create graphics or images efficiently because of their parallel structure, cloud GPUs are heavier applications-oriented. Cloud Graphics Units (GPUs) are computer instances with robust hardware for running applications to handle massive AI and deep learning workloads in the cloud. A cloud GPU does not demand deploying a GPU on your device.
Here are some of the common examples where a cloud GPU is used:
- Analytics, Deep learning, and Mathematical modelling
- CAD applications such as video encoding, rendering, and streaming
- Embedded systems
- Gaming consoles
- Cloud Gaming
- Mobile phones
- Personal Computers (PCs)
- Image recognition
- 3D computer graphics Calculations
- Texture mapping
- Geometric calculations
- Video decoding, encoding, and streaming
- Graphic designing
- Content creation
Now that we know the use cases of a cloud GPU are huge, in the next section we will discuss when is the favourable time to use a cloud GPU in an organisation.
When Should You Use a Cloud GPU?
Using a cloud GPU depends on the typical applications in an industry where heavy computing is required. Cost being one of the prominent factors among the organisations, it is advisable to use this system for those applications where parallelized computing is required such as data processing and a lot more.
Although some GPUs can be equally priced as CPUs, currently there are some reasons which make GPUs more cost-intensive:
- Global chip shortage
- COVID-19 Pandemic
- Heavy tariffs on GPU imports
- For better performance and specifications
The crypto miners have snatched up the high-end cards with 4 GB RAM or more, so those who still prefer using older graphics cards having 4GB or lesser VRAM have some operations to choose from.
When you should not use a Cloud GPU?
For applications where sequential computing is sufficient, cloud GPU is not needed as it will also save intensive costs up to 100 times.
One such instance is SIMD or Single Instruction/Multiple Data, which is a computing method with which multiple data is processed with a single instruction that can be handled with a CPU and a cloud GPU is not required.
Typically, the 3D graphics and processing audio and video in multimedia applications are one such use case where a single CPU is sufficient.
Another reason why CPUs are still preferred is that GPUs are parallel processors which do limited operations on an independent dataset and divide that among the processors for faster execution and are performed easily on multiple processors such as in graphic computing.
CPUs are useful for applications that are not heavily parallelized. The speed in this case is because of the hardware offering solutions for specific applications. For minor Excel calculations, using GPUs is not necessary as even the slowest CPUs can outperform it as that work is not easily split and managed.
In this article, we have tried to give you a brief introduction of the cloud GPUs and in what ways do they differ from CPUs. Both have a different set of applications to be performed. While GPUs can run a limited but intensive set of computing problems, CPUs are optimised for a broader set of problems and follow more of a 'jack-of-all-trades and master of none' approach but are still prevalent for many industrial applications.
GPUs aren’t very expensive as the cost of a high-end Nvidia Tesla goes just over 10k, but the cost of a huge Xeon CPU can be a lot higher. For some particular tasks mentioned above, GPUs prove to be more cost-effective compared to the number of CPU cores needed to do the same task as a CPU core is quite powerful than a GPU core but a lot of them are needed.A GPU cannot boot a PC or any other computing device as no OSes exist for them. Also, GPUs cannot perform many functions required to operate as general-purpose CPUs. GPUs are the accelerators but they cannot be the replacements for the CPUs.