Supercharging Your AI/ML with NVIDIA A100 GPU on E2E Cloud

The field of Artificial Intelligence has seen a significant rise in the past decade. There are two reasons for this. The first reason is the increased popularity and readaptation of deep learning architectures due to their tremendous promise in speed and accuracy. The second reason is the availability of high computational power. With the introduction of the GPU, the field of AI has never been so attractive. GPUs made it possible for researchers and developers to create complex, scalable solutions for their organizations. GPUs are the accelerators that drive the architectures to train in a couple of hours rather than a couple of weeks. 

But when it comes to GPU, there is one problem. More computational power means more money. When it comes to small-scale businesses, it is difficult for them to have a local computational machine for complex calculations like running deep AI/ML architectures. Although there is a possibility of using cloud computing services, sometimes we don’t feel the money’s worth with the services provided. Here is when E2E Cloud provides the best accelerator at the best price introducing the Nvidia A100 GPU on the cloud. 

Let us look at some of the ways the Nvidia A100 GPU can help in accelerating your AI/ML solutions.

• Versatility: Many people think that GPU is only useful for huge architectures and analytics. But the A100 GPU can also help in accelerating small-scale jobs. With the highly versatile acceleration configurability, A100 can make the most out of the power for you. It provides the ability to utilize the maximum power to manage every single business need of yours. 
• The NVLink: With the next-generation NVLink, the A100 GPU can provide twice the computational than the previous generation. It makes it possible by connecting over multiple cores to gain a higher computational power to provide the highest computational performance on a single server. 
• The MIG: Although we talked about the single server instance performance, A100 GPU is not just limited to that. The Multi-instance GPU (MIG) provides the ability to slice the GPU into as many as seven fully-isolated instances with their caches. With the individual core functionality, developers can accelerate all types of application performances. 
• Tensor cores: Compared to Nvidia Volta GPUs, the A100 GPU provides 20 times tensor FLOPS in deep learning training as well as inference. The A100 clocks in at over 312 TeraFLOPS. 
• Structural sparsity: There are many such instances in deep learning when the overall computational matrix can go sparse. Instances like this can hurt the computational resource as many of the computations would go blank. But with A100 GPU tensor cores, developers can have twice the performance on sparse matrices. Not just increasing the inference but also model training. 
• High bandwidth memory (HBM2): With the increased HBM of over 40 GB, the A100 GPU takes the raw bandwidth performance to as much as 1.6TB/sec. It also increases the dynamic RAM efficiency to over 95 per cent, giving 1.7 times more efficient performance than the previous generation. 

Why E2E Cloud?

There are several reasons for choosing the A100 GPU, but why should someone choose E2E Cloud for using this accelerator? Here are some of the reasons that can help make a choice clear:

• E2E Cloud provides world-class cloud infrastructure, suitable for your every need. 
• With accelerators starting from just ?30/hour, the E2E network provides the most affordable solutions out there. 
• E2E Cloud never asks for a long-term commitment. You may use the services as you like, for as long as you wish. 
• The transparency of E2E Cloud makes it the cloud platform to go for with no hidden or additional charges anywhere. 
• The best thing about E2E Cloud is that every single instance runs on an Indian server. It reduces the latency and hence gives you faster access to the resources. 
• With E2E Cloud, you can focus on what you do best. We provide a 99.9% uptime, so you do not need to worry about anything other than your application.

Conclusion

GPUs are something that can decide how quickly you can develop and test any application of yours. As the GPU can take the time for training from a week to just an hour in certain cases, it can tremendously drop delivery time to your customers. With the accelerators like A100, the developers can be more confident about the best solutions, as faster training can test more.

We believe that hardware accelerators should not be the privilege only for big organizations. It should be there for those who need it, and with the price, they can afford. 

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