Data Scientists, engineers and researchers are always looking for new inventions with the aid of deep learning technology. Deep learning technology requires large GPU powered computational power. In-house infrastructure management is quite expensive in case of construction and components purchasing. The best option is to use GPU powered Cloud services. GPU is well-known for speeding up the data, pipelining in neural networks, and other data components.
E2E’s GPU Cloud is appropriate for an extensive range of applications:
- Artificial Intelligence: Here, you need GPU to train complex models at greater accuracy and speed to achieve predictions and decisions in algorithms. Greater the training samples, more accuracy is the detection.
- Computer Vision: When it comes to computer-aided imaging, the quality of the model cannot be compromised. So a high GPU is required for deep-learning and better-quality analysis, medical imaging, and many other computer visions.
- Computational Finance: As data handling by businesses is growing so is the requirement of precise financial calculation of complex financial data. Real-time calculation algorithms require large computation power, which GPU provides.
- Scientific Research: In this year of pandemic, many scientists are making use of computational power for medicine detection. Here key components are time and greater bandwidth, to run deep search algorithms for real-time results, which is possible with the computational power of GPU powered cloud service.
- Big Data: By making use of GPUs, large sets of data can be easily ingested into the system, enabling users to make powerful queries and real-time visuals, among billions of data sets.
Two immediate yields from choosing GPU than CPU powered are; DNN interfaces run 3-4 times faster and load management, and GPU powered network will aid to use higher load. To train and implement deep learning networks, you would require hundreds of data points. These data points need to be powered with significant resources, which include memory, storage, and processing power. Well-known benefits of GPUs on deep learning are:
- A Higher Number of Cores: GPUs include a large number of cores, which can easily be clustered combined with CPUs, thus increasing the processing power.
- Higher Memory: Larger bandwidth(up to 750GB/s vs 50GB/s) than CPUs, which aids the network to handle higher amounts of data in deep learning.
- Task Distribution: Parallel processing power provides a platform for easy load distribution by constructing clusters. Or each GPU can be trained to power each algorithm.
Choosing the right GPU powered cloud, based on requirement is essential. All of the GPUs offered by E2E cloud are powered by Nvidia GPUs. The best reason to choose NVIDIA is because of the libraries that they provide, known as CUDA toolkits. This consists of libraries that enable easy processing in Deep learning based on strong machine learning. In addition to the GPU power, libraries are well-developed by the large community at NVIDIA and Frameworks like PyTorch, Café 2 etc. are offered.
E2E public cloud service provides a huge range of Cloud GPU Plans. Based on the combination of these 5 GPU offerings:
- Tesla T4: T4 is based on TU104 NVIDIA graphics processing unit (GPU). Well-known for its AI projects supports all required AI frameworks, and network components consist of a universal deep learning accelerator perfect for distributed computing environments. While T4 provides innovative multi-precision performance to fast-track machine learning training and deep learning.
- Tesla V100: V100 is one of the advanced data centre GPUs built to fast-track AI, Graphics and HPC. This is constructed and powered based on NVIDIA Volta architecture. A single V100 Tensor Core GPU believes in delivering the power of 32 CPU. It is armed with 640 Tensor Cores and known for higher efficiency with lower power consumption providing raw bandwidth of 900GB/s.
- RTX8000: It is one of the most powerful graphics powered by NVIDIA Turing architecture and NVIDIA RTX. It is focused on power advanced graphical power during visualized based deep learning to extract complex models and scenes using physically accurate shadows, refractions and reflections to allow models with the instantaneous insight of imaging. It consists of 576 Tensor cores and 72 RT Cores providing 96 GB of GDDR6 memory and provides a bandwidth of 100 GB/s (bidirectional).
- A100: It is one of the advanced data centres of GPUs. This Tensor Core GPU brings extraordinary acceleration required for AI, analytics, and HPC. It is powered by NVIDIA ampere architecture and delivers 312 teraflops (TFLOPS) of deep learning performance and raw bandwidth of 1.6TB/sec. Every deep learning network consists of Caffe2, MXnet, TensorFlow, Theano, Pytorch frameworks powering 700+ GPU accelerated applications.
- NVIDIA virtual GPU (vGPU): As companies are using hypervisor-based server virtualization about 80% server workload is run on virtual machines (VM). vGPU is licenced software which enables to power your AI, ML and HPC workloads. vGPU provides the platform to power multiple VMs. All the other top GPUs are supported by vGPU supporting max of 16.
Conclusion
To advance rapidly, machine learning capabilities require greater processing capabilities. As divergent to CPUs, GPUs deliver higher processing power, larger memory bandwidth, and platform for parallelism. On-premise possessions typically come with a high upfront overhead, with the use of E2E cloud GPU you can,
- Finish First: Stay ahead of the competitors by making use of the advanced computational power, ready state libraries and frameworks leveraging business throughputs.
- Solve: Let your business problem be solved with precise measurement. Tensor core provides that best platform.
- Save: Save that time and project budget by choosing the E2E GPU cloud service. By making use of ROI across each node.
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