Which GPU You Should Buy in 2022?

May 17, 2022

First things first, it's critical to understand why we need a GPU and there are a lot of additional factors that affect the consumer choice for purchasing the GPU. 

In this blog, we've compiled a list of GPU recommendations for training or building deep learning models. Not all GPUs are appropriate for deep learning applications. Those constructed expressly for this use case, have the computational capability needed to sustain these networks. They've also been tweaked to reduce memory latency, which is vital when it comes to training these models.

Our Top Picks for Deep Learning GPUs

You must choose GPUs that can serve your operation in the long term and can scale through integration and clustering. This involves choosing consumer GPUs for less complex tasks such as low-level testing and model planning or production-grade/data centre GPUs for high-level testing and model execution. 

Deep Learning GPUs for the General operations

There are many GPUs for low-level operations but the Titan RTX and the Titan V, in particular, have demonstrated performance comparable to datacenter-grade GPUs. 

  1. Titan RTX

Titan RTX serves as an entry point for researchers, developers, and artists. It is powered by TuringTM architecture, offering 130 Tensor TFLOPs of performance, 576 tensor cores, and ultra-fast GDDR6 memory of 24 GB. TITAN RTX can train complex models such as ResNet-50 and GNMT up to four times quicker. TITAN RTX, which is built with multi-precision Turing Tensor Cores, provides revolutionary performance, allowing for quicker neural network training.

  1. Titan V

When it comes to Word RNNs, the Titan V has been demonstrated to perform similarly to datacenter-grade GPUs. Furthermore, its performance for CNNs is only somewhat inferior to that of higher-tier choices. The NVIDIA TITAN V comes with the groundbreaking capability of 12 GB HBM2 memory and 640 Tensor Cores, offering the performance of 110 TeraFLOPS. For optimal performance, it also has NVIDIA CUDA.

  1. NVIDIA Tesla K80

To improve performance, this GPU combines two graphics processors. The NVIDIA Tesla K80 is a dual-slot card powered by an 18-pin power socket. This GPU can reduce energy in data centers while increasing throughput in real-world applications. This feature indicates that the GPU will perform better. The core features a dual-GPU design, 24GB of GDDR5 storage, 480 GB/s collective memory bandwidth, ECC protection for greater dependability, and server optimization. 

Best Deep Learning GPUs for Large-Scale Projects

1. Nvidia H100

It has to be on the top of the list as it was recently released by Nvidia with a lot of innovations. H100 is a ninth-generation data center GPU with 80 billion transistors. It is ideal for large-scale AI and HPC models as it is based on the Hopper architecture and is believed to be the world's largest and most powerful accelerator


  • Most Advanced Chip in the World
  • Speeds up network speed  to 6x
  • Secure Computing 
  • 2nd-Generation Secure Multi-Instance GPU with MIG capabilities that are 7 times more powerful than the prior version
  • NVIDIA NVLink 4th Generation connects up to 256 H100 GPUs at a bandwidth greater than 9 times.
  • Can accelerate dynamic programming up to 40x faster than CPUs and 7x faster than previous-generation GPUs.

2. Nvidia A100

The NVIDIA A100 Tensor Core GPU was the world's most powerful GPU for AI, data analytics, and high-performance computing. The Ampere design outperforms its predecessor by up to 20X, with the capacity to divide into seven GPUs and dynamically react to changing needs. The A100 GPU supports multi-instance GPU (MIG) virtualization and GPU partitioning, making it ideal for cloud service providers (CSPs). 


  • AI Inference Performance Up to 249X Faster than CPUs 
  • On the largest models, AI training can be up to three times more effective. 
  • The A100 80GB introduces the world's fastest memory bandwidth of more than 2 terabytes per second (TB/s), allowing it to execute the largest models and datasets. 
  • HPC Applications can benefit from up to 1.8X faster performance. 
  • On the Big Data Analytics Benchmark, GPUs outperform CPUs by up to 83X. With Multi-Instance GPU, Inference Throughput is increased by 7X. (MIG)

3. Nvidia V100

The NVIDIA V100 is a GPU with Tensor Cores that was built for machine learning, deep learning, and high-speed computing (HPC). It is driven by NVIDIA Volta technology, which supports tensor core technology, and is specialized for accelerating typical deep learning tensor operations. Each Tesla V100 has 149 teraflops of capability, up to 32GB of memory, and a memory bus of 4,096 bits.


  • Training Throughput is 32X faster than a CPU. 
  • A CPU Server has a 24X higher inference throughput. 
  • A single V100 server node can replace up to 135 CPU-only server nodes. 
  • It is designed to maximize performance in current hyperscale server racks. With AI at its core, the V100 GPU outperforms a CPU server in inference performance by 47X

4. Nvidia P100

The Tesla P100 has been redesigned from silicon to software, with innovation at every level. Each game-changing breakthrough provides a significant boost in performance, inspiring the development of the world's fastest compute node. The Tesla P100 is a GPU built for machine learning and HPC that is based on the NVIDIA Pascal architecture. Each P100 has a performance of up to 21 teraflops and 16GB of memory.


  • Pascal Architecture provides Exponentially Improved Performance.
  • It can scale applications over many GPUs and achieve 5X greater performance. 
  • Applications can now scale beyond the physical memory size of the GPU to potentially infinite quantities of memory. 
  • Customers can save up to 70% on total data center costs.

5. Nvidia T4

The NVIDIA T4 GPU speeds up a wide range of applications such as high-performance computing, deep learning inference and training,  data analytics, machine learning, and graphics. T4 is optimized for mainstream computing scenarios and contains multi-precision Turing Tensor Cores and new RT Cores. It is based on the new NVIDIA TuringTM architecture and built in an energy-efficient 70-watt, compact PCIe form factor. T4 delivers unprecedented performance at scale when combined with NGC's accelerated containerized software stacks.


  • T4 has up to 40X the performance of CPUs. 
  • T4 delivers up to 40X faster throughput, allowing more requests to be fulfilled in real-time. 
  • It provides breakthrough performance in FP16, INT8, and FP32 precisions.
  • T4 provides game-changing performance for AI multimedia applications, with specific hardware converting engines that deliver double the decoding performance of previous-generation GPUs.


Unfortunately, there is no universal solution for the GPUs requirement. The optimal GPU for your project will be determined by your individual requirements, the level of maturity of your AI operation, the size at which it operates, and the algorithms and models you use. 

The most important thing to remember, however, is that consumer-grade GPUs can only handle a limited set of parameters. As a result, if you want to grow efficiently and give a large number of parameters, data center GPUs on the E2E cloud are the way to go. You can run and deploy your deep learning models rapidly and affordably with the E2E cloud. The pay-as-you-go pricing approach ensures that you only pay for what you use and that you receive the most value for your money.

Learn more about this and more on E2E Cloud.

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This is a decorative image for: A Complete Guide To Customer Acquisition For Startups
October 18, 2022

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

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This is a decorative image for: Constructing 3D objects through Deep Learning
October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

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This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts
October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

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This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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