E2E networks offer a very high-performance cloud infrastructure and are a world-class cloud in India.
Their GPU Cloud is apt for a wide variety of applications comprising Computer Vision, AI, Scientific Research, Computational Finance, and Big Data. Taking the extensive benefit of GPU cloud computing, E2E networks announced its GPU power-driven cloud provision in association with NVIDIA.
In a world where cloud computing is the future and deep neural networks are being used extensively in the business, data scientists, scholars, and engineers can stop worrying about their precious time, money and memory, and concentrate on the next big AI innovation. E2E networks always intend on evolving and providing profitable cloud solutions for businesses. This article is about how E2E is offering powerful Nvidia A100 Tensor Core GPU Services and how important it is for all types of consumers extending from startups to big industries, from ML experts to CXOs of tech companies, and even tech enthusiasts.
Why do we need GPU Services?
When any data scientist wants to achieve high performance while training enormous datasets or deep learning (DL) models, most of the time, they take hours or even weeks without a powerful GPU.
While DL models require a lot of computational power to run, Machine Learning (ML) and recognition algorithms require great accuracy. Even in our digital world, high-res images and videos consume a lot of computational cost and storage. GPU driven clouds solve all such problems.
NVIDIA GPU Cloud (NGC)
NGC can achieve optimal solutions for all existing problems while executing scientific computing and deep learning models and is very technologically advanced. NGC involves an extensive directory of GPU – powered packages essential for ML, DL, and High-Performance Computing (HPC).
Source: HPC in NVIDIA
NGC containers deliver an easy and powerful stage for deploying software systems efficiently to achieve faster and more efficient solutions. NGC allows operators to emphasize on developing slender models, congregating quicker intuitions, and creating optimum resolutions.
The key factors on which they rely are: –
- Staying up to date
- Faster innovation
- Running anywhere
The benefits of using NGC are: –
- It provides models that are already trained and help deliver streamlined SDKs, thus enabling endwise AI explications.
- It has dedicated Augmented DL Frameworks, which make deep learning gear easily accessible.
- It has renowned DL Stack Containers such as TensorFlow (Powerful library for data flow and mathematical computations), MXNET (framework for deep neural networks), and framework customizations.
Nvidia A100 Tensor Core GPUs in E2E
Source: E2E Networks
The NVIDIA A100 is capable of carrying out unparalleled acceleration. It can boost performance in big data, data analytics, artificial intelligence, and high-performance computing (HPC), and confront the most difficult computations easily. The A100 is capable of scaling up effectively to thousands of GPUs or being segregated into 7 GPU instances to boost jobs of every dimension using NVIDIA Multi-Instance GPU (MIG) technology.
The A100 is targeting to achieve a big milestone with 3,456 FP64 CUDA cores, 6,912 FP32 CUDA cores, and 422 3rd – Gen Tensor cores. There is no alteration in code due to the combination of the exactness of FP16 and the range of FP32 during model training.
The NVIDIA A100 GPU is powered by the latest technologies like: –
- Fine-grained Designed Thinness: 2X the computational output for deep neural networks.
- 54 Billion Transistors (Xtors) on top of an 826 mm^2 size of a die: A 7 – nm processor, almost 3 times the speed of RTX 2080 Ti.
- 3rd Gen NVLink: Delivers connection-level fault finding and packet rerun mechanism.
- 3rd – Gen Tensor Cores
- Multi-instance GPU: It allows the partition of the A100 into seven distinct GPU occurrences.
The A100 GPU familiarizes revolutionary and innovative features designed to enhance implication loads. It offers extraordinary adaptability, stability, and performance. It can train huge Artificial Intelligence programs like BERT (Language Model) on a cluster of 210 A100s in only 37 minutes. It can also fast-track an entire range of accuracies, ranging from (floating point) FP32 to FP16 to INT8 and way down to INT4.
Looking at the HPC Performance Curve, we can see how it has delivered 9X more performance since 2016: –
Speaking of Cloud Gaming, latency, or the delay in time between input and output, is a crucial part. E2E GPU cloud improves the gaming experience by delivering an ultra-low latency system to “Cloud Hunt” Users.
Thus, E2E offering NVIDIA A100 Tensor Core GPU services are capable of giving a boost in: –
- Cloud data centers
- Single and Multi-GPU Workstations
- Servers and clusters, and
- Edge computing systems.
We are building a strong appreciation from the end-users because of our trustworthiness, scalability, affordability, and improved privacy features.