Cloud GPUs Vs On-Premise GPUs: Which is better for your use case?

Building a secure on-prem infrastructure requires dealing not just with space and power overhead but also with complex environmental controls and security protocols. For SMBs and enterprises alike, such undertakings can easily become cost-prohibitive.

Technology is developing at an exponential speed. These technological developments are changing every aspect of our lives.?Artificial intelligence?is the chief factor that is driving technological developments. Almost every business organization in the world is embracing artificial intelligence. The benefits reaped from AI in business processes are ground-breaking. AI solutions are changing traditional business processes and are driving them towards digital transformation. AI has helped companies amplify their growth, reach more audiences and work with agility.

With the growing technological developments, the cost of implementing them will also fluctuate. So the question is, how much does artificial intelligence model training cost? Well, the answer is, it depends. The cost of artificial intelligence model training depends on a few crucial factors. To determine the?AI?costs, we have to understand the factors leading to the current pricing.

GPU systems are incredible calculating machines. Choosing between GPU cloud servers and on-premises GPU servers is an eminent part of decision-making for your business. On-premise set-up is close to mountain climbing and you cannot put all your time according to the prevailing scenarios. It can increase your budget and load parallel to your dedicated work.?Cloud GPUs are the best choice to go with and every company wants to follow this?trend. Lets elaborate on how we can achieve this?

GPUs-?Graphics processing units (GPUs), originally developed for accelerating graphics processing, can dramatically speed up computational processes for deep learning. They are an essential part of a modern?artificial intelligence infrastructure, and new GPUs have been developed and optimized specifically for deep learning. Deep learning applications require powerful multi-GPU systems for development and operation, which can be very expensive to rent in the cloud for long-term operations.?

The question arises: Which infrastructure offers the best compromise between time-to-solution, cost-to-solution and availability of resources? To understand this, Let’s dive deeper into the Difference between the cost of?On-Premise GPU servers and Cloud GPUs-

If your organization is associated with training deep neural networks or multi-layer neural networks, GPU servers are essential.?

?Following are the verticals on which we can say Cloud GPUs are better than On-premise GPU servers-

• Business Agility: Use of cloud servers eminently reduces the hardware cost to zero. Subscription-based cloud offerings follow a pay as you go model which makes business strategies much easier to gain profits. Apart from infra, reduced operational costs are achievable with cloud servers.

• Wide selection:?With a number of options available, one can easily select the required cloud server, as it comes with hassle-free superfast deployment which again requires no guidance.?

• Data centralization:?It brings all the data of remote locations to a centralized cloud which gives hand on accessibility to business-critical applications and different projects. With a couple of clicks, data can be stored and retrieved via any device.

• Huge cut off in traditional IT and maintenance:?Server, network, power, insurance and maintenance is no longer a worry as such things are taken care of by Cloud service providers.

• Scalability: One of the demanding features of cloud computing which allows customers to scale up and scale down the cloud infrastructure according to the need in running projects.

Considering the above table, the optimal choice you can make is always a cloud platform as you can see 50% cost saving in the long run and freedom to move from one GPU card to another . What else do you need when E2E Cloud- The fastest growing accelerated cloud computing is available at 50% lower costs than hyperscalers ?

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