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Massive amounts of user-generated data are reformulating what’s needed for hyperscale data centres. Most cloud applications exploit invaluable data in order to deliver smarter, real-time experiences using image processing, modern video and also deep learning techniques. These particular applications can benefit significantly from GPU acceleration within the data centre.
NVIDIA Tesla M4 GPU Overview
This is the world’s first accelerator that targets hyperscale servers, allowing customers to always keep up with the constantly growing amount of data within their systems. It is designed to accelerate application output in a compact, low-power design, cutting data centre costs by over a half and also offers up to seven times more processing power as compared to CPU’s for deep learning at about 20 images per second, video workloads, machine learning prediction, inference and demanding web applications.
The Telsa M4 GPU compute card has 4GB GGR5 memory, 88 Gbps bandwidth and is fitted with about 1,024 NVIDIA Cuda cores. It needs only 50 to 75 watts of power to function, which is aided by a user-selectable power options profile as opposed to the M40 GPU compute card which requires up to 250 watts. The low-power consumption feature makes it perfect for neural networks at hyperscale data centres. It also has a small form factor that conforms to the requirements of hyperscale data centres.
"Machine learning is certainly one of the most crucial developments achieved in computing today, on the scale of cloud computing, the Internet, and PCs. " Claims Jen-Hsun Huang, the CEO and co-founder of NVIDIA. "Various industries ranging from healthcare, automotive and consumer cloud services are being revolutionized at the moment."
He further adds that machine learning is currently the biggest computational challenge of the future generation, and the Tesla GPU accelerator is built to give deep learning a 10X boost.
These new software and hardware products are designed precisely to accelerate the large number of web applications and services that are in a hurry to integrate Artificial Intelligence capabilities. Revolutionary advances in deep learning have made it possible to implement Artificial Intelligence techniques to develop smarter, cutting-edge applications.
Machine learning is a powerful process that has made many things possible that previously were not such as automatic object and scene recognition in photos or videos with the capability to tag for future search. It makes facial recognition in photos and videos possible, even if the face being captured is not that clear. It also powers services that are conscious of an individual’s interests and tastes, responds accurately to voice commands, organize schedules in an orderly fashion, and also deliver relevant stories and news.
The magic can be achieved by machine learning. The main challenge is acquiring the ominous amount of supercomputing amount of supercomputing power required to train and innovate the increasing number of neural networks, and the processing to respond to numerous queries from users instantly using the service. The NVIDIA accelerator line such as the NVIDIA Tesla M4 was designed to accelerate these workloads efficiently and substantially increase the throughput of applications.
The combination of NVIDIA hyperscale suite and the Tesla accelerated platform offers an end-to-end solution to design and deploy modern hyperscale services and applications.
GPU TESLA: M4
CUDA Cores: 1024
Memory Size: 4GB GDDR5X
Memory Interface: 128-bit
Memory Bandwidth: 88 GB/Sec
Bus Type:PCI-Express 3.0 x16
Thermic Solution: Passive