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Comparing On-Premise & Cloud: Which Is Better?

Comparing On-Premise & Cloud: Which Is Better?

Comparing On-Premise & Cloud: Which Is Better?

 

On-premise GPU continues to progress, more and more organizations are also starting to invest in the implementation of machine learning operations. They do this to speed up their processes, especially for those organizations that work with deep learning (DL) processes, which are said to be incredibly complicated & time-consuming. If you’re one of these organizations, there is a way in which you can speed up this process even further—by using graphical processing units (GPU).

 

What is a GPU?

It’s basically a powerful tool you can harness to expedite the processing of large data pipelines with a deep neural network. It excels in parallel processing computations, which means it can run multiple calculations at the same time. 

Simply put, GPUs are microprocessors that perform specific tasks. They have a huge number of cores, better memory bandwidth than CPUs, and overall flexibility.

 

Choosing Your Deep Learning Infrastructure

You have two options here—an on-premises system and a GPU cloud platform. Each of these choices offers certain advantages and disadvantages. 

  • On-Premise

The foremost advantage of an on-premise GPU system is that it gives developers greater flexibility. You will pay more upfront, but you will get more use out of your hardware. You can also have complete control over your data, configurations, and security

  • Cloud GPU

Cloud-based GPUs are a good option for startups, individuals, or organizations who are looking to save on costs. Cloud resources are known for lowering the financial obstacles commonly associated with developing DL infrastructure. They also offer scalability features and provider support. Using a cloud GPU for long periods of time may result in rising costs.

 

Which is better?

Choosing between an on-premise system and a GPU cloud is a lot like choosing to buy or rent a property. A cloud-based GPU service is a lot like renting an apartment or residence. You’ll pay a lot less in terms of capital, and you can quickly scale up or down according to your needs or financial capacity. You can also stay around for as long as the contract dictates.

On the other hand, investing in an on-premise GPU is comparable to buying your own property. Because this is a one-time fee, you can keep the asset for as long as you desire.

 

Conclusion

If you ask us, we say there’s no decisive way to determine which is the better choice among the two as it would have to depend on the specific situation. Each platform brings with it pros and cons that you’d need to assess carefully in line with the needs of your organization. 

 

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