A Deep-Dive into H100 Cloud GPUs for CXOs and Leaders

December 20, 2023

Introduction

AI/ML and HPC are two of the most powerful and transformative technologies of our time. They can unlock new possibilities and opportunities for businesses across industries and domains. However, to harness the full potential of AI and HPC, you need the right hardware and software infrastructure that can handle the massive scale and complexity of these workloads. Achieving this requires a powerhouse cloud GPU server that has been recently launched in India by E2E Cloud - the H100 GPU and the AI Supercomputer HGX 8xH100 GPUs. 

H100 Cloud GPUs are the ultimate AI GPUs, designed to deliver an order-of-magnitude performance leap for large-scale AI, HPC and LLM (Large Language Model) applications. Whether you want to deploy conversational AI, train large language models (LLMs), enable exascale computing, fine-tune image synthesis or audio generation AI, or solve any other challenging problem, H100 Cloud GPUs can help you achieve your goals faster and more efficiently.

Unleashing the Potential of H100 Cloud GPUs

Imagine a 30X speed boost that catapults your AI applications to the next level. That's what H100 Cloud GPUs can do for you, unlocking the power of cutting-edge conversational AI applications – such as chatbots, recommendation engines, and natural language understanding – which can handle trillion-parameter language models with ease. With a dedicated Transformer Engine and a high-speed NVLink Switch System, H100 Cloud GPUs set a new benchmark for large language models.

Faster Training, Reduced Costs

Time is money in AI development, and H100 Cloud GPUs can save you both. H100 Cloud GPUs can train foundational AI models up to 4X times faster than the previous generation, slashing the costs of creating state-of-the-art AI solutions. H100 Cloud GPUs can also leverage the Mixture of Experts (MoE) technique, which distributes the model parameters across multiple GPUs, to train even larger models with up to 395 billion parameters. The result? Pushing the frontiers of AI development without breaking the bank. The MoE technique has recently been used highly effectively by Mistral AI in their latest model - Mixtral 8x7B. 

Unmatched Inference Performance

H100 Cloud GPUs shine with an incredible 12X higher inference performance for massive LLM models, compared to the previous generation. This means low latency and high power efficiency while handling generative AI workloads such as image synthesis, video generation, and text-to-speech. H100 Cloud GPUs also bring a high level of quality and diversity to your generative AI projects, setting a new standard in the AI landscape.

Pioneering Exascale Computing

H100 Cloud GPUs are the driving force behind exascale computing, the next frontier of scientific discovery and innovation. Cluster of H100 Cloud GPUs, such as HGX 8xH100, is a supercomputer that harnesses H100 GPU, which itself can perform more than a quintillion calculations per second, enabling breakthroughs in fields such as climate modeling, drug discovery, astrophysics, and quantum computing. H100 GPUs are not just promising the future; they are delivering it, reshaping the landscape of scientific innovation.

Why CXOs Should Leverage HGX 8xH100 and H100 Cloud GPUs

CXOs and leaders should care about the NVIDIA HGX 8xH100 and H100 GPUs for several reasons. These GPUs offer powerful capabilities for AI and HPC (High-Performance Computing) applications, making them crucial for organizations at the forefront of technological innovation. 

The HGX 8xH100 server, which includes eight H100 Tensor Core GPUs and four third-generation NVSwitches, provides a staggering 900 gigabytes per second NVLink. The H100 Tensor Core GPU is designed to securely accelerate workloads from enterprise to exascale HPC and trillion-parameter AI. The E2E Cloud’s HGX 8xH100 platform, which combines H100 Tensor Core GPUs with high-speed interconnects, is one of the world's most powerful servers for AI and HPC.

The scale of computing power that H100 and HGX 8xH100 unlock for businesses can allow them to build advanced AI / ML solutions that would be otherwise impossible. Since E2E Cloud pioneered this in India, several startups and enterprises have started piloting their AI solutions, deploying extremely powerful AI at scale. Some of them are: 

Building Indic-Language LLMs: Building foundational AI models require access to highly efficient and advanced cloud GPUs that can take on massive numbers of parameters, humongous datasets, and enable training on large-scale deep learning neural networks. This is what the H100 cluster enables in a cost-effective way. 

Text-to-Image and Text-to-Video AI: Historically, media and entertainment industries have grappled with challenges around producing stock images, ‘b-roll’ and stock footage required for everyday media production workflows. This was not only expensive, but completely inaccessible to many due to sheer lack of content in some domains. With the emergence of open-source AI models like Stable Diffusion, it is now possible to train and fine-tune image and video generation models, and create systems that help fix content gaps that existed. 

Audio Synthesis and Voice-Over Generation: Audio production was another domain that was plagued with high sunk costs, and required tremendous amounts of effort during post-production of media and entertainment content. Open source AI tools have now emerged that enterprises can deploy and train on their GPU cloud servers, and provide a streamlined audio and text-to-speech generation workflow for media professionals, solving a major pain-point that plagued post-production workflows.

Industry-Specific AI Solutions: Another big leverage that powerhouse GPUs like H100 and HGX 8xH100 provide is the ability to create AI solutions for specific industries like healthcare, finance and education. This could range from training LLMs for conversational AI models, or building instruction-tuned AI that could be integrated into existing application stacks. Several of E2E Networks’ clients are now training large-scale LLMs designed for specific domains, and planning to transform user-experience of their products through that. 

The use-cases are numerous, and we are just beginning to understand the capabilities that advanced AI can unlock. 

Simplifying AI Adoption with TIR and E2E Cloud GPU Servers for Enterprises

For leaders navigating the complexities of AI adoption, simplicity is the guiding principle. Creating a private on-premises GPU infrastructure set-up is expensive, and the most effective way to harness GPUs is through cloud platforms. However, until recently, the cloud GPU servers that were available to Indian customers were hardly capable of meeting the compute demands of large-scale AI models. This is exactly what prompted us to pioneer HGX 8xH100 on E2E Cloud, enabling startups and enterprises with instant access to the most cutting-edge AI GPU server available currently.

H100 Cloud GPUs work smoothly with the TIR AI platform on E2E Cloud, offering not just a technological solution but the ability to build AI models and deploy AI endpoints at scale without the need for a large ops or IT team. 

Our integration simplifies AI adoption, enabling leaders to create data science teams who can focus on innovation without the hassle of complex implementations.

Seamless Integration with Frameworks and SDKs

One of the key user-friendly aspects of H100 Cloud GPUs is their seamless integration with popular development tools and frameworks. These GPUs are designed to work seamlessly with software development kits (SDKs) and libraries commonly used by developers, such as CUDA and TensorRT. This integration allows developers to easily leverage the power of H100 Cloud GPUs without having to make significant changes to their existing workflows. But wait, there is more:

  • Intuitive Software Development Kits (SDKs)

H100 Cloud GPUs come equipped with intuitive SDKs that streamline the development process. These kits offer a comprehensive set of tools, APIs, and libraries, empowering developers to leverage the full potential of these GPUs without grappling with unnecessary complexities. From model training to deployment, the SDKs provide a cohesive environment for developers to work within.

  • Robust Application Programming Interfaces (APIs)

The strength of any GPU lies in its APIs, and H100 doesn’t disappoint. Developers can tap into well-documented APIs that facilitate smooth integration with their applications. This not only accelerates development but also ensures compatibility with a wide range of software frameworks, enabling flexibility and choice in the development ecosystem.

  • Debugging and Profiling Tools

Developing complex AI models demands robust debugging and profiling tools, and H100 Cloud GPUs deliver on this front. Developers can efficiently identify and resolve issues in their code, optimize performance, and fine-tune their models for maximum efficiency. These tools contribute to a more iterative and productive development cycle.

  • Comprehensive Documentation

Understanding the ins and outs of a new GPU can be a daunting task, but H100 GPUs come with comprehensive documentation. Developers have access to detailed guides, tutorials, and use-case examples. This wealth of information ensures that developers, whether seasoned or new to the technology, can quickly get up to speed and make the most of H100’s capabilities.

Summary of Key Strengths of H100 Cloud GPUs

What sets the H100 apart is not just its power but its finesse in handling a myriad of generative AI workloads. Beyond its raw performance and accelerated training, the H100 unlocks a lot of possibilities with its dynamic adaptability.

  • Raw Performance

H100 Cloud GPUs deliver a staggering 30X speed increase for large language models (LLMs), setting a benchmark for raw performance. The combination of an advanced Transformer Engine and NVLink Switch System propels these GPUs into a league of their own.

  • Training Speed and Cost Efficiency

Accelerating development timelines, H100 GPUs offer up to 4X faster training for GPT-3 models. The incorporation of the Mixture of Experts (MoE) technique not only enhances training efficiency but also contributes to cost savings.

  • Inference Performance and Workload Handling

H100 GPUs shine with up to 12X higher inference performance, maintaining low latency and power efficiency. Their versatility extends to handling diverse generative AI workloads such as image synthesis, video generation, and text-to-speech.

  • Form Factor and Data Center Compatibility

The adaptability of H100 GPUs is evident in their various form factors, ranging from PCIe cards to NVLink modules and DGX systems. This flexibility ensures seamless integration with diverse data center configurations, offering scalability and efficient communication.

  • Software Ecosystem and Developer Support

H100 GPUs seamlessly integrate with the NVIDIA AI Enterprise software suite, simplifying AI adoption for businesses. The comprehensive documentation, SDKs, and developer tools contribute to an enriched user experience, making it easier for developers to leverage the full potential of these GPUs.

Start Your AI Journey Today

The current pace of technological advancement in the AI domain is relentless, and we are progressively bringing the best AI resources for Indian developers and researchers on our cloud platform. 

If you want to dive in deeper, talk to our team to understand the capabilities that H100 or HGX 8xH100 bring. Elevate your machine learning, data analytics, and high-performance computing with our GPU Dedicated Compute solutions. The H100’s Transformer Engine propels language models forward, while the HBM3 memory subsystem, Tensor Cores, MIG technology, and NVLink redefine performance benchmarks. Tailored variants offer flexibility, from singular powerhouses to multi-GPU configurations. With pricing starting at ₹412/hr, discover a synergy of innovation and affordability. 

Contact us today at sales@e2enetworks.com to learn more. 

Latest Blogs
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.

Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

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

Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

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.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

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.

Reference Links

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/ 

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html

Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure