An Introduction to NVIDIA L40S

September 13, 2023


The rapid advancements in technology have led to an ever-increasing demand for powerful Graphics Processing Units (GPUs). Whether it's for running complex Artificial Intelligence (AI) algorithms, rendering high-quality 3D graphics, or accelerating various types of computational workloads, GPUs have become an indispensable part of modern computing. Enter the NVIDIA L40S—the most powerful universal GPU designed for the data center. While its predecessor, the L40, was released on 13 October 2022, the much-anticipated L40S is expected to hit the market by the end of 2023. This blog post aims to provide an in-depth look at what makes the NVIDIA L40S a game-changer in the realm of data center GPUs.

The Evolution from L40 to L40S

When NVIDIA released the L40 in October 2022, it set a new standard for data center GPUs. However, technology never stands still, and NVIDIA has taken it a step further with the upcoming L40S. Dubbed as the most powerful universal GPU, the L40S is not just an incremental update; it's a significant leap forward in terms of performance, features, and capabilities.

The L40S builds upon the strong foundation laid by the L40 but takes it to new heights. With an increase in Thermal Design Power (TDP), the L40S is designed to handle even the most demanding workloads. It's perfect for small to medium-scale operations and is particularly well-suited for AI training, Large Language Models (LLMs), and multi-workload environments.

The transition from L40 to L40S is not merely about numbers; it's about delivering a more robust, versatile, and efficient GPU that can meet the ever-evolving needs of modern data centers.

A green and white logoDescription automatically generated

Architectural Overview

One of the most compelling aspects of the NVIDIA L40S is its underlying architecture. Powered by the NVIDIA Ada Lovelace Architecture, the L40S is engineered to deliver unparalleled performance and efficiency. Ada Lovelace is NVIDIA's latest architectural innovation, designed to push the boundaries of what GPUs can achieve.

The Ada Lovelace Architecture brings several key advancements that set the L40S apart from its predecessors and competitors. It incorporates a more efficient instruction set, optimized data paths, and enhanced memory hierarchies, all aimed at maximizing throughput and minimizing latency. These architectural improvements are not just incremental; they represent a significant leap in GPU design philosophy.

The architecture plays a crucial role in enabling the L40S to deliver its impressive range of capabilities. From its massive number of CUDA cores to its advanced Tensor and RT cores, every component is optimized to work in harmony. This synergy provides a seamless and powerful computing experience, whether you're running AI algorithms or rendering complex 3D models.

Furthermore, the Ada Lovelace Architecture is highly adaptable, making it ideal for a wide range of applications. Whether you're dealing with AI acceleration, 3D rendering, or complex scientific simulations, the architecture ensures that the L40S is up to the task. It's this versatility that makes the L40S not just a powerful GPU, but a universal one, capable of adapting to the ever-changing demands of modern data centers.

Key Specifications

When it comes to raw power and capabilities, the specifications of the NVIDIA L40S speak volumes. Below is a detailed breakdown of its key technical specifications presented in a tabular form:

With 48GB of GDDR6 memory and a bandwidth of 864GB/s, the L40S is well-equipped to handle data-intensive tasks, making it ideal for AI and machine learning applications. The high number of CUDA cores (18,176) signifies the GPU's capability for parallel computing, which is crucial for tasks like scientific simulations and 3D rendering. The inclusion of advanced Tensor and RT cores allows for accelerated machine learning and real-time ray tracing, respectively. These specialized cores make the L40S versatile and capable of handling specialized workloads. While the L40S is a powerhouse, it has a max power consumption of 350W, which is something to consider for data center energy management. Features like secure boot with root of trust and NEBS Level 3 readiness, add an extra layer of security and reliability, making the L40S suitable for enterprise-level applications.

These specifications not only make the L40S a powerhouse in terms of performance but also offer a range of features that cater to various needs. Whether it's the massive 48GB of GDDR6 memory with ECC for data integrity or the high number of CUDA cores for parallel computing, the L40S is built to handle all types of applications.

Performance Metrics

The NVIDIA L40S isn't just about impressive specifications; it's about delivering unparalleled performance where it matters most. Here's a closer look at some of the key performance metrics:

  • FP32 Performance: With a staggering 91.6 teraFLOPS, the L40S excels in single-precision floating-point calculations, making it ideal for a wide range of scientific and engineering applications.
  • Tensor Core Performance: The L40S boasts a peak Tensor performance of 1,466 TFLOPS (with sparsity), making it a formidable choice for AI and machine learning tasks.
  • RT Core Performance: At 212 Teraflops, the RT Core performance is optimized for real-time ray tracing, enhancing visual rendering capabilities.
  • Power Efficiency: Despite its high performance, the L40S has a max power consumption of 350W, showcasing its efficiency.
  • Sparsity Support: The L40S supports sparsity, a feature that allows the Tensor cores to process zero values in matrices more efficiently. This results in a significant boost in performance, especially in AI and machine learning applications where sparse data sets are common.

These performance metrics confirm that the L40S is not just a jack-of-all-trades; it's a master of them. Whether you're running complex AI algorithms, rendering intricate 3D models, or simulating scientific phenomena, the L40S has the performance capabilities to handle it all.

Specialized Features

The NVIDIA L40S is not just a powerhouse in terms of raw specifications and performance metrics; it's a marvel of engineering that comes packed with specialized features designed to meet the diverse needs of modern data centers. Let's delve deeper into some of these standout features:

Fourth-Generation Tensor Cores

The fourth-generation Tensor Cores are a significant advancement in NVIDIA's GPU technology. They offer hardware support for structural sparsity and come with an optimized TF32 format. This not only results in immediate performance gains for AI and data science model training but also opens up new possibilities for AI-enhanced graphics. For instance, the DLSS technology leverages these Tensor Cores to upscale resolution in real-time, providing better performance in select applications without compromising on quality.

Third-Generation RT Cores

The third-generation RT Cores are designed to revolutionize visual computing. With enhanced throughput and the ability to handle concurrent ray-tracing and shading, these cores significantly improve ray-tracing performance. This is particularly beneficial for industries like product design, architecture, and engineering, where high-quality renders are crucial. The hardware-accelerated motion blur and real-time animations add another layer of realism, making designs come to life like never before.

Transformer Engine

The Transformer Engine is a groundbreaking feature that dramatically accelerates AI performance. It works in tandem with the Ada Lovelace fourth-generation Tensor Cores to scan the layers of transformer architecture neural networks intelligently. The engine can automatically recast between FP8 and FP16 precisions, optimizing memory utilization and delivering faster AI performance across both training and inference tasks.

Efficiency and Security

The L40S is built with enterprise-level efficiency and security in mind. It is optimized for 24/7 data center operations and undergoes rigorous testing to ensure maximum performance, durability, and uptime. Additionally, it meets the latest data center standards and is NEBS Level 3 ready. The secure boot with root of trust technology adds an extra layer of security, making the L40S a reliable choice for sensitive, high-stakes environments.


DLSS 3 is another feather in the cap of the L40S. This advanced frame-generation technology leverages deep learning and the latest hardware innovations within the Ada Lovelace architecture. It significantly boosts rendering performance, delivers higher frames per second (FPS), and improves latency. This is particularly useful for real-time 3D rendering and gaming applications, where smooth performance is key.

Versatility Across Workloads

One of the most compelling aspects of the L40S is its versatility. Whether it's AI training, Large Language Models (LLMs), 3D rendering, or multi-workload environments, the specialized features of the L40S make it a one-size-fits-all solution for a wide array of computing needs.

These specialized features, combined with its robust architecture and powerful performance metrics, make the NVIDIA L40S a versatile and formidable GPU, capable of meeting the diverse and ever-evolving needs of modern data centers.

Use Cases

The NVIDIA L40S is a versatile powerhouse designed to excel in a multitude of applications. Below are some key use cases where the L40S truly shines:

AI and Machine Learning

The L40S is a game-changer in the realm of AI and machine learning. Its fourth-generation Tensor Cores, coupled with the Transformer Engine, provide unparalleled performance for both training and inference tasks. Whether it's natural language processing, computer vision, or predictive analytics, the L40S offers the computational power and efficiency to tackle complex algorithms and large datasets with ease.

3D Graphics and Rendering

When it comes to 3D graphics and rendering, the L40S is in a league of its own. Its third-generation RT Cores and a high number of CUDA cores enable it to deliver stunning visual quality at high speeds. This makes it an ideal choice for industries like architectural visualization, animation, and game development, where visual fidelity and performance are critical.

Video Applications

The L40S is a robust solution for video encoding and decoding tasks, thanks to its NVENC and NVDEC capabilities. It can handle 4K video streams with low latency, making it a perfect fit for video streaming services, post-production tasks, and real-time video analytics. Its support for AV1 encoding and decoding further expands its utility in modern video applications.

Scientific Simulations

The L40S excels in scientific simulations, thanks to its high FP32 performance and massive memory bandwidth. Whether you're working on computational fluid dynamics, molecular modeling, or climate simulations, the L40S offers the computational power to handle complex calculations and large datasets, delivering results with high accuracy and in less time.

Multi-Workload Environments

The L40S is a true multi-tasker, capable of handling multiple workloads efficiently. Its versatility makes it an ideal choice for data centers that require a multi-purpose GPU. From running virtual machines and containerized applications to big data analytics and real-time monitoring, the L40S can manage a wide array of tasks without breaking a sweat.

Security-Centric Applications

Security is a paramount concern in today's digital landscape, and the L40S is well-equipped to meet these challenges. With features like secure boot and root of trust, it offers an added layer of security that makes it a reliable choice for enterprise-level operations where data integrity and security are non-negotiable.

Practical Advantages

The NVIDIA L40S is not just a high-performance GPU; it's a practical, versatile solution designed for seamless integration into modern data centers. Here's why the L40S stands out in offering practical advantages:

Plug-and-Play Architecture

The L40S features a plug-and-play architecture that simplifies the installation process. With its standard form factor and compatibility with existing power and cooling solutions, getting the L40S up and running is a straightforward affair.

Virtual GPU Support

The L40S comes with virtual GPU (vGPU) software support, allowing for the virtualization of GPU resources. This is particularly useful for organizations that want to maximize resource utilization across multiple tasks and users.

Comprehensive Software Ecosystem

NVIDIA provides a rich ecosystem of software and tools optimized for the L40S, including libraries for machine learning, data analytics, and more. This makes it easier for developers to leverage the full capabilities of the GPU without having to start from scratch.

Energy Efficiency

Despite its high performance, the L40S is energy-efficient with a maximum power consumption of 350W. This makes it a sustainable choice for data centers looking to optimize their energy usage without compromising on performance.

Security Features

With secure boot and root of trust technology, the L40S adds an extra layer of security, making it a reliable choice for data-sensitive environments. These features ensure that only authorized firmware and software run on the device, providing peace of mind for IT administrators.


The L40S is designed to handle a wide range of workloads, making it ideal for small to medium-scale operations that require a versatile, high-performance GPU. Whether it's AI training, 3D rendering, or multi-workload computing, the L40S can handle it all with ease.

By offering these practical advantages along with its powerful features and performance metrics, the NVIDIA L40S proves itself to be a well-rounded, practical solution for modern data centers.


The NVIDIA L40S is a groundbreaking GPU that promises to redefine the landscape of data center computing. With its state-of-the-art Ada Lovelace architecture, impressive performance metrics, and a host of specialized features, it sets a new standard for what a universal GPU can achieve. But what truly sets the L40S apart is its practical advantages. From its plug-and-play architecture and comprehensive software ecosystem to its robust security features and energy efficiency, the L40S is designed with real-world applications in mind. It offers a versatile, high-performance solution that is as easy to implement as it is powerful.

We're excited to announce that the NVIDIA L40S will soon be available on E2E Cloud. Given the long waitlist for access to this GPU, launching L40S node on E2E Cloud offers a convenient and immediate way to access its powerful capabilities without the wait or the upfront investment.

While the L40S is expected to be released by the end of 2023, its predecessor, the L40, has already demonstrated the potential of this line of GPUs since its release on 13 October 2022. The L40S aims to build on this legacy, offering end-to-end acceleration for the next generation of AI-enabled applications—from generative AI and model training and inference to 3D graphics, rendering, and video applications.

In summary, the NVIDIA L40S is not just an incremental upgrade; it's a leap forward in GPU technology. Whether you're involved in AI research, 3D rendering, or running a multi-workload data center, the L40S is poised to be a game-changer. And now, thanks to E2E Cloud, you can be among the first to experience its transformative power.


  1. Official NVIDIA website.
  2. NVIDIA Datasheet.
  3. PNY

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

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

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

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

Build on the most powerful infrastructure cloud

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