Benefits of Using a vGPU

February 1, 2021

Traditionally, Desktop or PC have limited CPU computation level to run 3D applications. But with virtualization, this trend has changed remarkably in the last few years. You can now easily add more vGPUs units to any system to enhance its performance and get things done efficiently. This change offers a smart way for businesses to deliver performance for their end-user whenever required.

Understanding Virtual GPU (Graphics Processing Units)

There has been an unprecedented rise in the graphic requirements from Windows 10 up at 32 percent from the earlier version of Windows 7. (source) 

Today, all latest software with their utility and productivity apps require extra CPU resources to carry out their regular functions such as Chrome, Microsoft Office, Skype, Word, gaming, and all other applications. There has been a constant need to upgrade the software to improve its user experience that directly depends on the GPU or CPU usage.

Especially for commercial needs, businesses further need that extra virtual GPU to match their specific requirements. Most businesses today need additional virtual machine capabilities to improve their performance and compete within the industry. Thus can avoid extra costs by using virtual GPUs depending on their workplace requirements rather than buying them for higher costs. These virtual GPU cloud servers can easily integrate with your current infrastructure and deliver exceptional results with an instant performance boost for matching your business requirements.

And with E2E Networks, you get a one-click GPU Cloud to run a powerful application such as Tensorflow to manage Machine learning or any other complex architecture. We offer the world premium NVIDIA T4, NVIDIA Tesla V100, and NVIDIA RTX 8000 machines to bring forth the best possible resources for businesses to match all kinds of workload requirements.

vGPUs can easily integrate additional capabilities into the system and help deliver exceptional results with ease. 

Here are the essential benefits of using a Virtual GPU (VGPU) that everyone should know in this modern age.

1. Performance Enhancement

The first and the most significant benefit from vGPUs is the performance enhancement from desktop and virtual machines. Businesses today can opt for these virtual GPUs to get that extra leverage to match with high data processing ability. 

For instance, CannonDesign was able to bring a newer virtual workstation for every 10 minutes with higher user density and twice the performance. 

Cornerstone Home Lending integrated has more than 100 branches and 1000 users within a single virtual environment from its desktop workload to deliver smooth and uniform performance across every user.

Several activities today need additional GPU acceleration to perform, such as Image processing, video processing, 3D, 4D, or 5D modeling, and rendering, image segmentation, facial recognition, data processing and analyzing, medical imaging, real-time recognition, etc. And with every data or user increase in the organization, these resources can put extra pressure on the system to deliver results. Here virtual GPUs can be a tremendous relief as they can bring in more resources at the time of requirement for the system to process and deliver a smooth user experience. E2E offers NVIDIA T4 instances for special deep learning solutions for businesses to enhance image processing and segmentation solutions in running real-time applications. These have worked in parallel with various organizations in developing computer vision technology for generating capabilities similar to human vision.

2. GPU Memory Usage

Virtual GPUs can also be used to add more memory for the system to increase their ability to perform higher ability tasks. Different slabs for the NVIDIA GPU cloud are available. Businesses can use the power of 16GB, 32GB, 40GB, 48GB, 64GB, 80GB, 96 GB, and up to 128GB to maximize their system performance to handle large data applications.

NVIDIA RTX server has excellent capacities to run powerful software such as Autodesk Maya., etc., to give end-users impeccable solutions. With a virtual machine, you can create, build, and deliver spectacular animations for businesses to work at par excellence. You can also add special effects for lighting and rendering along with all best practices under the GPU virtualization with high performance as always.

NVIDIA vGPUs are among the only technology that supports accelerated live migration of platforms using their virtual machines. And when the system needs additional resources to distribute, enhanced uptime, data optimization, and avoid troubleshooting, these Virtual GPUs are the ideal solution today. The whole system infrastructure can help manage and control workloads across multiple machines as per the user requirements to match the demands accordingly.

3. Improved Driver Support 

Systems need further driver support to run and manage large data frameworks to process and precisely understand the information. Virtual space can help to distribute ability across premises to add more capabilities for working with multimedia and powerful creative work applications in the virtual setups.

NVIDIA GPU Server can provide high density for monitoring systems and adding more tracking information for central management. Businesses also use them to enhance server-side graphic abilities and administrative proficiency and get more results from current IT architecture.

With the work from home environment getting more prowess in this Pandemic year, Businesses are also using virtual GPU servers to track virtual workflow procedures. This can add multiple tasks tracking abilities within a system such as image rendering, video rendering both at the same time.

4. Video Experience

With the continuous enhancement, today, videos are common among the users. Videos require additional resources from the backend to give a live presentation for users. Technology has improved to show 3D, 4D, or even 5D capabilities that offer businesses change their infrastructure rapidly. But with virtual GPUs, these businesses can leverage the latest NVIDIA GPU cloud to get sophisticated technology at hourly or monthly prices rather than buying them for a specific use. Thus delivering a smooth user experience for users with a virtual enhancement of their hardware resources.

5. Gaming Experience 

Gaming is another essential dimension that has improved significantly in the last decade. More and more businesses are turning to virtual GPU cloud servers to gain additional resources for their system. Newer Games use more advanced technology with AR/VR (Augmented Reality and Virtual Reality) games. These modern games offer rich features and functionality for users to enjoy requiring ultra-high resources to deliver a smooth experience. While Mobile Edge computing (MEC) even requires higher frame rates to give users the right gaming experience. And with Networks communicating at a 5G rate and streamlined data processing, systems today work on virtual GPUs to give users an incredible experience throughout the game.

NVIDIA vGPU solutions are highly sophisticated these days and offer up to 160 PC games for running them at one point. Especially for games, latency is a crucial factor for Virtual GPUs. And E2E Networks now offer several opportunities for gaming enthusiasts to get their specific GPU solutions in maximizing their profits.

Gaming is a really big industry today in several countries, with more interest coming from the younger generation. More and more complex resource requirements also put a lot of pressure on the system that only matches the modern cloud GPU solutions. The manufacturers and gaming organizations use these cloud GPU solutions to get more and more users to enjoy a smooth experience throughout. 

6. Cost Affordability 

Yes, one of the main benefits you can achieve with a Cloud GPU server is its affordability. At a specific cost, you can get the power of the world's best system performance to any virtual machine or desktop application.

E2E Networks can seamlessly connect with any business vertical to give you enhanced performance for any AI/ML workload and deliver data processing. And businesses will pay for what they use and still get customized solutions without affecting their extra spending.

You can check the NVIDIA tesla price to match with a customized graphic processor, GPU memory, disk space, dedicated RAM, and get hourly or monthly prices, to deliver desired results for your business at any requirements. You can create and build even a custom virtual machine to compare prices and then find the exact solutions for your workload for performing at specific requirements.

You can also opt for NVIDIA vGPUs trial to understand the whole user experience and then buy the services to get more customized solutions for matching your specific business requirements.

7. Working with Private Cloud/ Customization / Multi GPUs

E2E Networks also offer private GPUs that experts can customize as per any requirements with a dedicated server and get the desired performance. There are also hundreds of pre-packed offers that businesses can easily access with one-click to integrate with their current system for enhancing performance.

This can boost the current IT architecture to match with any higher resource requirement. So, bridging the gap for businesses to work with high-data processing needs without having a significant impact on their infrastructure costs. E2E Networks have several options for businesses to get customized solutions and bring forthright solutions for their clients as per the requirement rather than investing heavily in the hardware resources.

These modern virtual machines are highly compatible as well, and you can connect almost any workstation to enhance their performance. 


Today VGPUs have become a phenomenon across industries, with businesses using them to leverage their current architecture based on their requirements. E2E Networks is a reliable and trusted organization by more than 10000+ clients nationally and internationally. Almost every industry is now using GPU Cloud server ability to work and collaborate with real-time users, such as Creative Cloud (Adobe), Bentley Microstation, Maya Autodesk, Dassault Systemes SOLIDWORKS, PACS (Picture Archiving and Communication System), Reuters, Bloomberg, Eikon, Eclipse Medical imaging, and also major electronic trading platforms have been using these cloud GPU servers to manage their workloads precisely. 

  1. GPU cloud has become a prominent feature in working with top industry applications such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). These are the futuristic technologies that can work at a rapid pace for training data models for predicting and decision making in algorithms. These can take data from any industry and then understand their pattern to drive their strategies and planning for the future.
  1. Computer Vision has become elemental technology, using deep learning technology for facial recognition, video analysis, medical imaging, etc.
  1. Computational Finance today needs extra security and technology to protect information from the cyber world and real-time challenging scenarios.
  1. Modern Scientific Research today needs that extra powerful GPU power to manage big data processing ability for delivering results in the latest fields of fluid dynamics, molecular modeling, and many more. Scientists enjoy working on these powerful systems for delivering more results and bringing more futuristic solutions. 
  1. The Big Data industry has become the primary revolution for next-generation success. Data has grown profoundly in the last few years as more and more industries are using this information to gain an extra advantage over their competitors. Now the system needs that GPU power to handle this voluminous data at ease.

Graphical Processing Units (GPUs) today are the fundamental blocks for managing big data processing technology. And with virtual GPU (vGPU), businesses enhance the system's ability and performance whenever required to match high user requirements. Thus enabling a traditional CPU to act as a powerful virtual machine to handle 3D motions and graphics, image, and video capabilities to experience smoothly. E2E Networks offer world premium NVIDIA virtual GPU technology to match with high data processing requirements.

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June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

Reference Links:,product%20on%20the%20right%20platform.

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June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.


The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.


In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.


Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:


Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 


As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online


As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 


Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.



Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 


An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.


In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.




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June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

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