What is a Ray Tracing GPU?

March 17, 2021

What is a Ray Tracing GPU?

Ray tracing is a popular rendering technique in 3D computer graphics. It is used to trace ray paths to enhance the precision and effects of light, shadow, and reflection. A few years ago, ray tracing was thought to be best for computer images and television VFX due to its high rendering time. However, due to GPU acceleration through hardware improvement, real-time ray tracing has become the new normal. This has significantly increased ray tracing application areas.

What is a Ray Tracing GPU?

A ray tracing GPU is the graphics card that has the hardware to support the rendering technique. Ray tracing brings realistic-looking shadows, lightning, caustics, reflections, and immersion into the virtual world. However, getting all this into games and other applications is a complicated task. Hence, only specific GPUs with high hardware capabilities can support it. These GPUs are what we call ray tracing GPUs.

The ray tracing GPUs introduce new workloads, such as denoising image and ray/triangle intersection, of the hardware. Ray tracing GPUs are capable of rendering various visual effects. Some of these optical effects include reflection, scattering, dispersion, and refraction.

You can use E2E Networks’ cloud servers to get the best out of the ray tracing GPUs. Our NVIDIA GPU cloud servers come at affordable rates to help you efficiently manage large volumes of data and complex programs. Using our NVIDIA GPU cloud servers allows you to seamlessly explore various ray tracing techniques, including reflections, shadows, illumination, and caustics.

What are the Different Ray Tracing Techniques?

You can use ray tracing in multiple ways ranging from reflections to shadows to illumination to caustics and ambient occlusion. Below-mentioned are some of the standard ray tracing techniques popularly used in the gaming domain and other fields.

  • Ray traced reflections

Ray tracing casts rays on various reflective surfaces, including water and glass. It then captures the reflections and projects it on the screen. To better understand it, we can take the example of Battlefield V, the first game that used real-time ray tracing. It specified surfaces, such as glass and water, that reflect light rays. Ray tracing then, casts rays on the reflective surfaces and projects the screen’s output for precise reflection effects.

  • Ray traced shadows

The ray tracing technique casts rays on characters and objects that can block light to provide realistic-looking shadows. There can be multiple light sources coming from different angles, which can make shadowing difficult. Ray tracing allows you to segregate light traces from various sources and curves, making it easier to manage each ray individually.

  • Ray traced illuminations

With their ray tracing capabilities, NVIDIA GPUs allow you to precisely model the effect of light and how it bounces from objects. This lets you quickly implement global illumination systems into games, engineering, and several other application areas.

  • Ray traced caustics

Caustic is a phenomenon of focusing and projecting the light rays reflected by curved or imperfect surfaces. While many attempts have been made, especially in the gaming domain, they were not accurate enough to create realistic caustics. Ray tracing allows you to render life-like caustics.

Which GPUs Support Ray Tracing?

The latest NVIDIA graphics cards from the RTX edition support real-time ray tracing. Some of the names to be familiar include the following:

  • Nvidia GeForce RTX 2060
  • Nvidia GeForce RTX 2060 Super
  • Nvidia GeForce RTX 2070
  • Nvidia GeForce RTX 2070 Super
  • Nvidia GeForce RTX 2080
  • Nvidia GeForce RTX 2080 Super
  • Nvidia GeForce RTX 2080 Ti
  • Nvidia Titan RTX

Applications of Ray Tracing GPUs

Ray tracing has been well-known for its applications in the media and entertainment field. It is used to capture shadows and reflections in a frame precisely for a more realistic look. However, ray tracing applications do not limit to media and entertainment, many other fields can leverage ray tracing too.

  • Developing games

Ray tracing has become the buzzword in the gaming world. Gaming developers thrive on making their games more realistic, and what better technique than ray tracing? With ray tracing, developers can introduce practical reflections and life-like shadows into games.

There are shadows and reflections in games without ray tracing support too. But the significant difference between the shadows and reflections is that they are static or not dynamic. The shadows and reflections without ray tracing are just a package of animations bunched together, which will always be the same. On the other hand, ray tracing enabled reflections are more dynamic and realistic. Some of the majorly popular games using ray tracing are:

  • Cyberpunk 2077
  • Call of Duty: Black Ops Cold War
  • Battlefield V
  • Fortnite
  • Minecraft: Bedrock Edition
  • Dirt 5
  • Watch Dogs: Legion
  • Spider-Man: Miles Morales

Similar to rays, ray tracing can also track sound waves. Hence, developers can also use it for more immersive sound and echo in video games. Although there is not much exploration done in this ray tracing application, hardware advancements will undoubtedly bring this into effect shortly.

  • Training AI models for image recognition

Training complex AI models for image recognition requires GPU acceleration. Not directly, but ray tracing can help accelerate the training procedure. NVIDIA has some great deep learning GPUs, majorly the ones that are recently launched. However, ray tracing can further accelerate it with the help of precise shadow and reflection capturing.

Deep learning models are trained with data and images in the case of image recognition. Due to the lack of precision in each image, developers have to use millions of pictures to train a single deep learning model. Ray tracing can help increase the accuracy, thereby reducing the time required to prepare the models. However, the developers may still have to train the models with numerous images; the results will be far better when trained with ray tracing frames.

Its sound waves tracing capabilities bring in the possibility of becoming a useful application in the voice recognition area. It can help denoise sound waves so that developers can train deep learning models faster.

  • Product design and virtual prototyping

Ray tracing is useful in all the stages of product designing and prototyping, right from the designing and evaluation to creating images for sales. Engineers and designers use them to create a clear and realistic looking design for proposing. The manufacturers then use it to build and test the product virtually using graphics.

When all the evaluation is complete and the product comes into the market, the sales and marketing team can also use ray tracing. The marketing team can create amazing images of the product for promotion. They can also use it to display the usage of the product realistically.

  • Designing architecture

Similar to product designers, architects can use ray tracing and its precision to design a blueprint of any structure. Unfortunately, hand-made designs cannot produce realistic illuminations. Several computer programs that can design model objects cannot model shadow and light precisely.

Computer-aided design (CAD) programs allow architects to introduce light by providing location, distribution, orientation, and color. But still, they do not come anywhere close to ray tracing. Backward ray tracing can precisely render architectural design by creating realistic images.

  • Engineering

Ray tracing is fully capable of rendering global illumination models. Global illumination is a term used to describe a system model that can consider all of the light, including the direct and indirect light (shadow and reflection) in an environment. The global illumination model helps engineers simulate light behavior in real life and build a model accordingly.

  • Animation

Ray tracing has paved the way for several new possibilities in the animation world. Traditionally, the process of creating animations was very complicated and required a lot of time. The introduction of ray tracing in animation has eliminated the need to work separately on the shadows and reflections, as it can all be done in a single frame now.

Animation artists can use ray tracing to add fancy shadowing effects for a more realistic feel. We can see some of the recent uses of ray tracing in popular animations like Beauty and the Beast, Aladdin, and Toy Story.

To put it simply, any industry that requires the use of computer-generated imagery or rendering can benefit from real-time ray tracing. The best way to leverage ray tracing is with NVIDIA servers on E2E Networks’ cloud. NVIDIA offers some of the best GPUs, and with our Windows cloud servers, the benefits of NVIDIA GPUs are further enhanced. Hardware advancements in GPUs can further increase the potential ray tracing application areas.

Why Use Ray Tracing GPUs on E2E Networks’ Cloud Server?

Ray tracing has proven to be useful in multiple areas. By using ray tracing GPUs on E2E Networks’ cloud servers, you can further enhance the former’s benefits. Let’s consider an example of a deep learning neural network. AI and deep learning have become some of the most significant technological advancements in today’s world.

Training deep learning neural networks require great quality GPUs to train complex models with optimal accuracy. The same is valid for medical and scientific research, where you need to process big data for the best results. We provide the best GPU for deep learning. Our Windows cloud server allows you to easily set up NVIDIA GPU with CUDA, Anaconda, Jupyter, Keras, and TF. Here are all the GPU cloud servers we offer.

Based on one of the best Tesla GPUs, the TU104 NVIDIA GPU is well known for its fast-paced, precise performance. Tesla T4 is well suited for AI projects to pace up ML and deep learning training processes.

Based on NVIDIA Volta architecture, Tesla V100 has 640 Tensor Cores and provides 900GB/s bandwidth.

RTX8000 is one of the latest NVIDIA graphics cards with 576 Tensor cores, 72 RT cores, and 100G/s bidirectional bandwidth.

By providing extraordinary acceleration to AI projects, NVIDIA A100 becomes the best GPU for machine learning. Powered by the ampere architecture, it provides a bandwidth of 1.6TB/s.

By running the vGPU on E2E Networks’ Windows cloud, you can deliver a full Quadro experience with optimal security. 

Apart from just the GPU servers available, there are many more reasons to choose our cloud services for ray tracing GPUs. Some of the notable benefits you get are:

  • We provide world-class infrastructure for Windows cloud servers for perfect shadow, reflection, and illumination capturing.
  • You can choose from numerous pricing plans according to your needs.
  • With E2E clouds’ top Windows cloud servers get a fast-paced solution for real-time ray tracing.
  • Gain faster access to resources with reduced latency and higher bandwidths.
  • With our 99.9% uptime, you can solely focus on your application without worrying about the cloud computing.
  • We deploy multiple network security protocols to ensure the complete safety of your data and applications.
  • No long-term commitments required; you can pay on an hourly basis.

While these features and benefits are tempting enough, there’s much more that you can get by running your ray tracing applications on E2E Networks’ GPU cloud servers. With multiple pricing options and pay on the go features, we allow you to buy Windows cloud servers at a very affordable price for all your ray tracing applications.

ConclusionRay tracing is one of the most significant leaps in 3D Computer Graphics, and with NVIDIA GPU Cloud server providing real-time ray tracing support, you can include the real-world feel to your games and other applications. You can choose E2E Networks’ NVIDIA GPU cloudservers for all your needs. You can refer to our page for getting the NVIDIA GPU cloud pricing.

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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. 

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This is a decorative image for Top 7 AI & ML start-ups in Telecom Industry in India
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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