Posts in category: nVidia AI

Enterprises challenged with running accelerated workloads have an answer: NVIDIA-Certified Systems. Available from nearly 20 global computer makers, these servers have been validated for running a diverse range of accelerated workloads with optimum performance, reliability and scale.

Now NVIDIA-Certified Systems are expanding to the desktop with workstations that undergo the same testing to validate their ability to run GPU-accelerated applications well.

Certification ensures that these systems, available as desktop or laptop models, have a well-balanced design and the correct configurations to maximize performance. GPUs eligible for certification in the workstations include the newest NVIDIA RTX A6000, A5000 and A4000, as well as the RTX 8000 and 6000.

NVIDIA-Certified workstations will join a lineup of over 90 already available systems that range from the highest performance AI servers with the NVIDIA HGX A100 8-GPU, to enterprise-class servers with the NVIDIA A30 Tensor Core GPU for mainstream accelerated data centers, to low-profile, low-power systems designed for the edge with NVIDIA T4 GPUs.

Certified Systems to Accelerate Data Science on CDP

Cloudera Data Platform (CDP) v7.1.6, which went into general availability last week, now takes advantage of NVIDIA-Certified Systems. This latest version adds RAPIDS to accelerate data analytics, ETL and popular data science tools like Apache Spark with NVIDIA GPUs to churn through massive data operations.

Testing has shown that this version of CDP runs up to 10x faster on servers with NVIDIA GPUs vs. non-accelerated servers. To make it easy to get started, NVIDIA and Cloudera recommend two NVIDIA-Certified server configurations that customers can purchase from several vendors:

• CDP-Ready: For running Apache Spark, a CDP-Ready configuration of NVIDIA-Certified servers with two NVIDIA A30 GPUs per server offers over 5x the performance at less than 50 percent incremental cost relative to modern CPU-only alternatives.
• AI ready: For customers additionally running machine learning or other AI-related applications, the NVIDIA A100 GPU provides even more performance — as well as acceleration on machine learning and AI training.

Data scientists often develop and refine machine learning and deep learning models on workstations to augment data center resources or help minimize cloud-based compute costs. By using an NVIDIA-Certified workstation, they can transition their work to NVIDIA-Certified servers when it’s time for larger scale prototyping and eventually production, without having to port to a different tool or framework.

New White Paper Describes Value of Certification

When it comes to installing GPUs and SmartNICs in a system, choosing the right server or workstation model and correctly configuring the components and firmware are critical to getting the most out of the investment.

With NVIDIA-Certified Systems, NVIDIA and its partners have already done the work of validating that a particular system is capable of running accelerated workloads well, and they’ve figured out the most optimal hardware configuration.

Misconfiguration can lead to poor performance and even inability to function properly or complete tasks. The certification process ensures that issues such as these are surfaced and resolved for each tested system. We’ve described this and more in a new white paper, Accelerate Compute-Intensive Workloads with NVIDIA-Certified Systems.

Our system partners run a suite of more than 25 tests designed by NVIDIA based on our vast experience with compute, graphics and network acceleration. Each of the tests is chosen to exercise the hardware of the system in a unique and thorough manner, so as many potential configuration issues as possible can be exposed. Some of the tests focus on a single aspect of the hardware, while others stress multiple components, both simultaneously as well as in a multi-step workflow.

With NVIDIA-Certified Systems, enterprises can confidently choose performance-optimized hardware to power their accelerated computing workloads — from the desktop to the data center to the edge.

Learn more about NVIDIA-Certified Systems:

• Aug. 12 webinar: The Expanding Universe of NVIDIA-Certified Systems
• Find an NVIDIA-Certified System
• NVIDIA-Certified Systems Configuration Guide

Computer graphics and AI are cornerstones of NVIDIA. Combined, they’re bringing creators closer to the goal of cinema-quality 3D imagery rendered in real time.

At a series of graphics conferences this summer, NVIDIA Research is sharing groundbreaking work in real-time path tracing and content creation, much of it based on cutting-edge AI techniques. These projects are tackling the hardest unsolved problems in graphics with new tools that advance the state of the art in real-time rendering.

One goal is improving the realism of rendered light as it passes through complex materials like fur or fog. Another is helping artists more easily turn their creative visions into lifelike models and scenes.

Presented at this week’s SIGGRAPH 2021 — as well as the recent High-Performance Graphics conference and the Eurographics Symposium on Rendering — these research advancements highlight how NVIDIA RTX GPUs make it possible to further the frontiers of photorealistic real-time graphics.

Rendering photorealistic images in real time requires accurate simulation of light, modeling the same laws that govern light in the physical world. The most effective approach known so far, path tracing, requires massive computational resources but can deliver spectacular imagery.

The NVIDIA RTX platform, with dedicated ray-tracing hardware and high-performance Tensor Cores for efficient evaluation of AI models, is tailor made for this task. Yet there are still situations where creating high-fidelity rendered images remains challenging.

Consider, for one, a tiger prowling through the woods.

Seeing the Light: Real-Time Path Tracing

To make a scene completely realistic, creators must render complex lighting effects such as reflections, shadows and visible haze.

In a forest scene, dappled sunlight filters through the leaves on the trees and grows hazy among the water molecules suspended in the foggy air. Rendering realistic real-time imagery of clouds, dusty surfaces or mist like this was once out of reach. But NVIDIA researchers have developed techniques that often compute the visual effect of these phenomena 10x more efficiently.

The tiger itself is both illuminated by sunlight and shadowed by trees. As it strides through the woods, its reflection is visible in the pond below. Lighting these kinds of rich visuals with both direct and indirect reflections can require calculating thousands of paths for every pixel in the scene.

It’s a task far too resource-hungry to solve in real time. So our research team created a path-sampling algorithm that prioritizes the light paths and reflections most likely to contribute to the final image, rendering images over 100x more quickly than before.

AI of the Tiger: Neural Radiance Caching

Another group of NVIDIA researchers achieved a breakthrough in global illumination with a new technique named neural radiance caching. This method uses both NVIDIA RT Cores for ray tracing and Tensor Cores for AI acceleration to train a tiny neural network live while rendering a dynamic scene.

The neural network learns how light is distributed throughout the scene. It evaluates over a billion global illumination queries per second when running on an NVIDIA GeForce RTX 3090 GPU, depicting the tiger’s dense fur with rich lighting detail previously unattainable at interactive frame rates.

Seamless Creation of Tough Textures

As rendering algorithms have progressed, it’s crucial that the 3D content available keeps up with the complexity and richness that the algorithms are capable of.

NVIDIA researchers are diving into this area by developing a variety of techniques that support content creators in their efforts to model rich and realistic 3D environments. One area of focus is on materials with rich geometric complexity, which can be difficult to simulate using traditional techniques.

The weave of a polo shirt, the texture of a carpet, or blades of grass have features often much smaller than the size of a pixel, making it difficult to efficiently store and render representations of them. NVIDIA researchers are addressing this with NeRF-Tex, an approach that uses neural networks to represent these challenging materials and encode how they respond to lighting.

Seeing the Forest for the Trees

Complex geometric objects also vary in their appearance depending on how close they are to the viewer. A leafy tree is one example: Close up, there’s enormous detail in its branches, leaves and bark. From afar, it may appear to be little more than a green blob.

It would be a waste of time to render detailed bark and leaves on a tree that’s on the other end of the forest in a scene. But when zooming in for a close-up, the model should be as realistic as possible.

This is a classic problem in computer graphics known as level of detail. Artists have often been burdened with this challenge, manually modeling multiple versions of each 3D object to enable efficient rendering.

NVIDIA researchers have developed a new approach that generates simplified models automatically based on an inverse rendering method. With it, creators can generate simplified models that are optimized to appear indistinguishable from the originals, but with drastic reductions in their geometric complexity.

NVIDIA at SIGGRAPH 2021 

More than 200 scientists around the globe make up the NVIDIA Research team, focusing on AI, computer graphics, computer vision, self-driving cars, robotics and more. At SIGGRAPH, which runs from Aug. 9-13, our researchers are presenting the following papers:

• Real-Time Neural Radiance Caching for Path Tracing
• Neural Scene Graph Rendering
• An Unbiased Ray-Marching Transmittance Estimator
• StrokeStrip: Joint Parameterization and Fitting of Stroke Clusters

Don’t miss NVIDIA’s special address at SIGGRAPH on Aug. 10 at 8 a.m. Pacific, revealing our latest technology, demos and more. Catch our Real Time Live demo on Aug. 10 at 4:30 p.m. Pacific to see how NVIDIA Research creates AI-driven digital avatars.

We’re also discussing esports as a real-time graphics challenge in a panel on Aug. 11. An interactive esports demo is available on demand through the SIGGRAPH Emerging Technologies program.

For more, check out the full lineup of NVIDIA events at SIGGRAPH 2021.

Autonomous trucking startup Embark is planning for universal autonomy of commercial semi-trucks, developing one AI platform that fits all.

The company announced today that it will use NVIDIA DRIVE to develop its Embark Universal Interface (EUI), a manufacturer-agnostic platform that includes the compute and multimodal sensors necessary for autonomous trucks. This flexible approach, combined with the high performance of NVIDIA DRIVE, leads to an easily scalable solution for safer, more efficient delivery and logistics.

The EUI is purpose-built to run Embark Driver autonomous driving software for a comprehensive self-driving trucking system.

Most trucking carriers don’t just use one model of vehicle in their fleets. This variety can even extend to vehicles from different manufacturers to haul a wide range of cargo around the world.

The Embark platform will be capable of integrating into trucks from any of the four major truck manufacturers in the U.S. — PACCAR, Volvo, International and Freightliner. By developing a platform that can be retrofitted to such a wide range of vehicles, Embark is helping the trucking industry realize the benefits of AI-powered driving without having to wait for purpose-built vehicles.

And with NVIDIA DRIVE at its core, the platform leverages the best in high-performance AI compute for robust self-driving capabilities.

Scaling Safety

Autonomous vehicles are always learning, taking in vast amounts of data to navigate the unpredictability of the real world, from highways to crowded ports. This rapid processing requires centralized, high-performance AI compute.

The NVIDIA DRIVE platform is the first scalable AI hardware and software platform to enable the production of automated and self-driving vehicles. It combines deep learning, sensor fusion and surround vision for a safe driving experience.

This end-to-end open platform allows for one development investment across an entire fleet, from level 2+ systems all the way to level 5 fully autonomous vehicles. In addition to high-performance, scalable compute, the EUI will have all the necessary functional safety certification to operate without a driver on public roads.

“We need an enormous amount of compute horsepower in our trucks,” said Ajith Dasari, head of Hardware Platform at Embark. “NVIDIA DRIVE meets this need head-on, and allows us to outfit our partners and customers with the best self-driving hardware and software currently on the market.”

A Growing Ecosystem

Embark is already working with leading trucking companies and plans to continue to extend its software and hardware technology.

In April, the company unveiled partnerships with Werner Enterprises, Mesilla Valley Transportation and Bison Transport. It’s also working with shippers including Anheuser Busch InBev and HP, Inc.

Embark plans to list on the public market, announcing a SPAC, or special purpose acquisition company, agreement in June, as well as a partnership with Knight-Swift Transportation. The autonomous trucking company will join the ranks of NVIDIA DRIVE ecosystem members who have collectively raised more than $8 billion via public listings.

And just like the trucks running on its Embark Universal Interface, the company is tapping the power of NVIDIA DRIVE to keep traveling further and more intelligently.

Computer vision and edge AI are looking beyond the pasture.

Plainsight, a San Francisco-based startup and NVIDIA Metropolis partner, is helping the meat processing industry improve its operations — from farms to forks. By pairing Plainsight’s vision AI platform and NVIDIA GPUs to develop video analytics applications, the company’s system performs precision livestock counting and health monitoring.

With animals such as cows that look so similar, frequently shoulder-to-shoulder and moving quickly, inaccuracies in livestock counts are common in the cattle industry, and often costly.

On average, the cost of a cow in the U.S. is between $980 and $1,200, and facilities process anywhere between 1,000 to 5,000 cows per day. At this scale, even a small percentage of inaccurate counts equates to hundreds of millions of dollars in financial losses, nationally.

“By applying computer vision powered by edge AI and NVIDIA Metropolis, we’re able to automate what has traditionally been a very manual process and remove the uncertainty that comes with human counting,” said Carlos Anchia, CEO of Plainsight. “Organizations begin to optimize existing revenue streams when accuracy can be operationally efficient.”

Plainsight is working with JBS USA, one of the world’s largest food companies, to integrate vision AI into its operational processes. Vision AI-powered cattle counting was among the first solutions to be implemented.

At JBS, cows are counted by fixed-position cameras, connected via a secured private network to Plainsight’s vision AI edge application, which detects, tracks and counts the cows as they move past.

Plainsight’s models count livestock with over 99.5 percent accuracy — about two percentage points better than manual livestock counting by humans in the same conditions, according to the company.

For a vision AI solution to be widely adopted by an organization, the accuracy must be higher than humans performing the same activity. By monitoring and tracking each individual animal, the models simplify an otherwise complex process.

Highly robust and accurate computer vision models are only a portion of the cattle counting solution. Through continued collaboration with JBS’s operations and innovation teams, Plainsight provided a path to the digital transformation required to more accurately provide accountability when receiving livestock at scale and thus ensure that the payment for livestock received is as accurate as possible.

Higher Accuracy with GPMoos

For JBS, the initial proof of value involved building models and deploying on an NVIDIA Jetson AGX Xavier Developer Kit.

After quickly achieving nearly 100 percent accuracy levels with their models, the teams moved into a full pilot phase. To augment the model to handle new and often challenging environmental conditions, Plainsight’s AI platform was used to quickly and easily annotate, build and deploy model improvements in preparation for a nationwide rollout.

As a member partner of NVIDIA Metropolis, an application framework that brings visual data and AI together, Plainsight continues to develop and improve models and AI pipelines to enable a national rollout with the U.S. division of JBS.

There, Plainsight uses a technology stack built on the NVIDIA EGX platform, incorporating NVIDIA-Certified Systems with NVIDIA T4 GPUs. Plainsight’s application processes multiple video streams per GPU in real time to count and monitor livestock as part of managing the accounting of livestock when received.

“Innovation is fundamental to the JBS culture, and the application of AI technology to improve efficiencies for daily work routines is important,” said Frederico Scarin do Amaral, Senior Manager Business Solutions of JBS USA. “Our partnership with Plainsight enhances the work of our team members and ensures greater accuracy of livestock count, improving our operations and efficiency, as well as allowing for continual improvements of animal welfare.”

Milking It

Inaccurate counting is only part of the problem the industry faces, however. Visual inspection of livestock is arduous and error-prone, causing late detection of diseases and increasing health risks to other animals.

The same symptoms humans can identify by looking at an animal, such as gait and abnormal behavior, can be approximated by computer vision models built, trained and managed through Plainsight’s vision AI platform.

The models identify symptoms of particular diseases, based on the gait and anomalies in how livestock behave when exiting transport vehicles, in a pen or in feeding areas.

“The cameras are an unblinking source of truth that can be very useful in identifying and alerting to problems otherwise gone unnoticed,” said Anchia. “The combination of vision AI, cameras and Plainsight’s AI platform can help enhance the vigilance of all participants in the cattle supply chain so they can focus more on their business operations and animal welfare improvements as opposed to error-prone manual counting.”

Legen-Dairy

In addition to a variety of other smart agriculture applications, Plainsight is using its vision AI platform to monitor and track cattle on the blockchain as digital assets.

The company is engaged in a first-of-its-kind co-innovation project with CattlePass, a platform that generates a secure and unique digital record of individual livestock, also known as a non-fungible token, or NFT.

Plainsight is applying its advanced vision AI models and platform for monitoring cattle health. The suite of advanced technologies, including genomics, health and proof-of-life records, will provide 100 percent verifiable proof of ownership and transparency into a complete living record of the animal throughout its lifecycle.

Cattle ranchers will be able to store the NFTs in a private digital wallet while collecting and adding metadata: feed, heartbeat, health, etc. This data can then be shared with permissioned viewers such as inspectors, buyers, vets and processors.

The data will remain with each animal throughout its life through harvest, and data will be provided with a unique QR code printed on the beef packaging. This will allow for visibility into the proof of life and quality of each cow, giving consumers unprecedented knowledge about its origin.

Archaeologist Daria Dabal is bringing the past to life, with an assist from NVIDIA technology.

Dabal works on various archaeological sites in the U.K., conducting field and post-excavation work. Over the last five years, photogrammetry — the use of photographs to create fully textured 3D models — has become increasingly popular in archaeology. Dabal has been expanding her skills in this area to create and render high-quality models of artifacts and sites.

With the help of her partner, Simon Kotowicz, Dabal is taking photogrammetry scans to the next level with a pair of NVIDIA graphics technologies.

Using NVIDIA RTX GPUs, Dabal can accelerate workflows to create and interact with 3D models, from recreating missing elements to adding animations or dropping them into VR. And with the NVIDIA Omniverse real-time collaboration and simulation platform, she can build stunning scenes around her projects using the platform’s library of existing assets.

All for the (Photo)Gram

Dabal quickly learned that good photogrammetry requires a good dataset. It’s important to take photos in the right pattern, so that every area of the site, monument or artifact is covered.

Once she has all the images she needs, Dabal builds the 3D models using Agisoft Metashape, a tool for photogrammetry pipelines. Dabal loads all her photos into the application, and the software turns that data into a point cloud, which is a rough collection of dots that represent the 3D model.

Recently, Dabal and Kotowicz were approached by The Flow State XR, a company that delivers immersive experiences and applications. They were tasked with a new photogrammetry project that rocked their world: creating a 3D model of The Crobar, an iconic, heavy-metal bar located in the heart of Soho in London.

The Flow State XR sent images of The Crobar to the duo, who used the photos to model the bar from scratch, then created texture maps using Adobe Photoshop, Illustrator and Substance Painter. Dabal and Kotowicz are currently finishing the 3D model, but once the project is complete, The Flow State XR plans to use it as an interactive mobile app and a VR hangout for music fans.

RTX Shapes 3D Modeling Workflows

Dabal uses an NVIDIA Quadro RTX 4000 GPU to significantly speed up her 3D modeling and rendering workflows. Processing a sparse cloud model with her older generation GPU would take two days, she said. With the upgraded RTX card, a similar point cloud takes only 10 hours.

With NVIDIA RTX, the millions of points on a model can be rotated and zoomed much more easily. The team can also use their 4K monitor to view the models, which they couldn’t do previously because it was difficult to navigate around the point clouds.

Dabal and Kotowicz have also experienced faster performance in creative apps like Autodesk  3ds Max. They can iterate quicker and see textures in graphics without needing to render as often.

“The NVIDIA RTX card has helped us achieve the model we need much faster,” said Dabal. “We’re spending less time in front of the workstation, and we’re getting to the rendering stage a lot quicker now.”

Omniverse Makes Space for Sharing Assets, Building Scenes 

Dabal got the chance to use the advanced features of NVIDIA Omniverse when she entered the first “Create With Marbles” design competition. After exploring the platform, Dabal sees great potential in how it will transform traditional workflows and images in archaeology.

Currently, there isn’t a tool that enables archaeologists to quickly upload assets in one place, and share or collaborate with others on the same projects.

With an open platform like Omniverse, archaeologists have a virtual space where they can easily upload photogrammetry artifacts and share assets with one another, no matter what location they’re working from. Or they could place models in Omniverse and create a stunning scene by adding extra elements, like trees or farm fields.

“Right now, most archaeological 3D models look fake, floating in their black backgrounds. It would take too much time to add the extras, but it would be so easy in Omniverse,” said Dabal. “When I was in Omniverse, I really enjoyed taking premade objects and moving them around to create a scene. It was super easy.”

Dabal says that if archeologists had access to a library of extra assets, as well as all their own photogrammetry scans, “it would be game-changing.”

With Omniverse, archaeologists can share their projects with others around the world as well as simulate fire or weather conditions to bring their 3D models and sites to life.

Explore more of Dabal’s work, and learn more about NVIDIA RTX and NVIDIA Omniverse.

And join NVIDIA at SIGGRAPH, where we’ll showcase the technologies driving the future of graphics. We’ll announce the winners of the latest “Create With Marbles: Marvelous Machines” contest winner, and premiere a documentary highlighting how Omniverse was used to create the NVIDIA GTC 2021 keynote.

Your Amazon Prime delivery just got smarter.

Autonomous trucking company Plus recently signed a deal with Amazon to provide at least 1,000 self-driving systems to retrofit on the e-commerce giant’s delivery fleet. These systems are powered by NVIDIA DRIVE Xavier for high-performance, energy-efficient and centralized AI compute.

The agreement follows Plus’ announcement of going public via SPAC, or special acquisition company.

Amazon — which leads the U.S. e-tail market, counting $386 billion in net revenue in 2020 — has been investing heavily in autonomous and electric vehicle technology. Last year, it acquired robotaxi company and NVIDIA DRIVE ecosystem member Zoox for $1.3 billion.

These deals signal the transition to autonomous systems in both personal and commercial transportation at a massive scale.

An A-Plus Platform

The current generation PlusDrive autonomous trucking platform was developed for level 4 autonomous driving with a human driver still at the wheel, using NVIDIA DRIVE Xavier system-on-a-chip at its core.

Xavier is the first-ever production, automotive-grade SoC for autonomous capabilities. It incorporates six different types of processors, including a CPU, GPU, deep learning accelerator, programmable vision accelerator, image signal processor and stereo/optical flow accelerator.

Architected for safety, Xavier incorporates the redundancy and diversity necessary for safe autonomous operation.

This high-performance compute enables the PlusDrive system to perform surround perception with an array of radar, lidar and camera sensors, running a variety of deep neural networks simultaneously and in real time.

Trucking Ahead

Plus’s deal with Amazon is just the beginning of the march toward widespread autonomous delivery.

The self-driving company has already announced plans to transition to the next generation of AI compute, NVIDIA DRIVE Orin, beginning in 2022. Plus has received more than 7,000 orders and pre-orders for this upcoming system.

Additionally, Amazon has been granted a warrant to buy a 20 percent stake in Plus after they spend more than $150 million, opening up the possibility for even deeper integration of the company’s technology with the e-retailer’s delivery fleet.

And with NVIDIA DRIVE at their core, these autonomous systems will be able to handle the AI processing necessary to deliver safe, efficient and continuously improving trucks at scale.

It’s a new month for GFN Thursday, which means a new month full of games on GeForce NOW.

August brings a wealth of great new PC game launches to the cloud gaming service, including King’s Bounty II, Humankind and NARAKA: BLADEPOINT.

In total, 13 titles are available to stream this week. They’re just a portion of the 34 new games coming to the service this month.

Members will also get to stream upcoming content updates for popular free-to-play titles like Apex Legends and Tom Clancy’s Rainbow Six Siege as soon as they release.

Fit for a King

It’s time to save a kingdom. Members will be able to stream King’s Bounty II (Steam) when it releases for PC later this month on GeForce NOW.

Darkness has descended over the world of Nostria in this exciting RPG sequel. Gamers will be able to play as one of three heroes, rescuing and building a personal army in a journey of leadership, survival and sacrifice. Fight for the future and outsmart enemies in turn-based combat. Every action has profound and lasting consequences in the fight to bring peace and order to the land.

Be the kingdom’s last hope and live out an adventure August 24. Preorder on Steam to get some exclusive bonuses.

More Fun for Free This Month

This month also comes with new content for some of the most popular free-to-play titles streaming on GeForce NOW. Members can look forward to experiencing the latest in Apex Legends and Tom Clancy’s Rainbow Six Siege.

“Apex Legends: Emergence,” the latest season of the wildly popular free-to-play game from Respawn and EA, launched on August 3 and brought in a new Legend, weapon and Battle Pass as well as some awesome map updates.

The newest legend, Seer, is here and ready to spot opportunities that others may miss. Players can also enjoy a new midrange weapon, the Rampage LMG, a slower but more powerful variation of the Spitfire. To top it all off, the newest map updates reveal a familiar landscape torn at the seams. Decimated World’s Edge is available now on Apex Legends and streaming on GeForce NOW.

The latest event in Tom Clancy’s Rainbow Six Siege features a new time-limited gameplay mode, a challenge to unlock a free Nomad Hive Mind set, and more. Year 6 Season 2 kicked off with a special “Rainbow Six Siege: Containment” event that sets players in the Consulate map overrun by the Chimera parasite in a new game mode called Nest Destruction. Members will be able to stream the Containment event from August 3 to August 24.

Here This Week

Starting off the month, members can look for the following titles available to stream this GFN Thursday:

• A Plague Tale: Innocence (Free on Epic Games Store, August 5)
• Death Trash (day-and-date release on Steam, August 5)
• Starmancer (day-and-date release on Steam, August 5) 
• CyberTaxi (Steam)
• Eldest Souls (Steam)
• Elex (Epic Games Store)
• The Flame in the Flood (Steam)
• GRIME (Steam)
• Hakuoki: Kyoto Winds (Steam)
• MetaMorph: Dungeon Creatures (Steam)
• Super Animal Royale (Steam)
• Tales of the Neon Sea (Steam)
• Zero Hour (Steam)

August’s Newest Additions

This month is packed with more games coming to GeForce NOW over the course of August, including nine new titles:

• Lawn Mowing Simulator (day-and-date release on Steam, August 10)
• NARAKA: BLADEPOINT (day-and-date release on Steam and Epic Games Store, August 12)
• Voidtrain (day-and-date release on Epic Games Store, August 12)
• Greak: Memories of Azur (day-and-date release on Steam, August 17)
• Humankind (day-and-date release on Steam and Epic Games Store, August 17)
• RiMS Racing (day-and-date release on Steam and Epic Games Store, August 19)
• King’s Bounty II (day-and-date release on Steam and Epic Games Store, August 24)
• Arid (Steam)
• Check vs Mate (Steam, North America) / Battle vs Chess (Steam, Europe)
• Before We Leave (Epic Games Store)
• Blood of Steel (Steam)
• Deepest Chamber (Steam and Epic Games Store)
• Hard Truck Apocalypse / Ex Machina (Steam)
• Hello Neighbor (Steam)
• Hello Neighbor: Hide and Seek (Steam)
• Ironclad Tactics (Steam)
• Old World (Epic Games Store)
• Shadow Man Remastered (Steam)
• The Architect Paris (Epic Games Store)
• TIS-100 (Steam)
• Wargame: European Escalation (Steam)

More from July

On top of the 36 games that were announced and released in July, an extra 24 titles joined the GeForce NOW library over the month:

• Alan Wake (Epic Games Store)
• Alan Wake’s American Nightmare (Epic Games Store)
• Call to Arms – Gates of Hell: Ostfront (Steam)
• Crowfall (Native Launcher)
• Death’s Door (Steam)
• Edge of Eternity (Epic Games Store)
• Escape from Naraka (Steam)
• Ironcast (Epic Games Store)
• Lost at Sea (Steam)
• Lumberjack’s Dynasty (Epic Games Store)
• Moonlighter (Epic Games Store)
• Obduction (Epic Games Store)
• Pathfinder: Kingmaker – Enhanced Plus Edition (Epic Games Store)
• Rayman: Raving Rabbids (Steam and Ubisoft Connect)
• Siege Survival: Gloria Victis (Epic Games Store)
• Space Colony: Steam Edition (Steam)
• The Immortal Mayor (Steam)
• Vesper (Steam)
• Warframe (Digital Extremes)
• Wildermyth (Steam)
• World of Warplanes (Steam)
• Wushu Chronicles (Steam)
• X3: Albion Prelude (Steam)
• X3: Farnham’s Legacy (Steam)

Finally, here’s a special question from our friends on the GeForce NOW Twitter feed:

𝙒𝙖𝙣𝙩𝙚𝙙: 𝘼 𝙨𝙩𝙧𝙖𝙩𝙚𝙜𝙞𝙘 𝙘𝙝𝙖𝙡𝙡𝙚𝙣𝙜𝙚.

Drop your favorite strategy games below.

— NVIDIA GeForce NOW (@NVIDIAGFN) August 4, 2021

Sustainability isn’t just a choice for consumers to make. It’s an opportunity for companies to lead.

AI startup Heartdub seams together fashion and technology to let designers and creators display physical fabrics and garments virtually — and help clothing companies achieve zero-waste manufacturing.

The company’s software digitizes textiles and then simulates how clothes look on the human body. This virtual design verification lessens the amount of physical samples needed, reducing waste from excess fabric, and helps minimize unsold inventory.

Based in Beijing, Heartdub is a member of NVIDIA Inception, a program designed to nurture startups revolutionizing industries with advancements in AI and data sciences. NVIDIA Inception helps startups during critical stages of product development, prototyping and deployment by providing free benefits such as go-to-market support and access to technology expertise.

“As a member of NVIDIA Inception, Heartdub hopes to leverage the resources, support and platform provided by NVIDIA to accelerate the implementation and extension of design verification and virtual showrooms,” said Li Ruohao, chief technology officer at Heartdub.

Zipping Up a Solution to Fabric Waste

Sampling fabrics and other materials is a costly, involved process for brand owners. It can slow the distribution of fashion information throughout the supply chain. And finished products may not look or perform the way consumers want, or may arrive too late to meet the latest trends.

Heartdub offers its customers Heartdub Materials, a physics engine comprising a large set of laboratory-grade textiles data which replicates the physical properties of materials. Inside the application, digitized material behaves as it would in the real world by accounting for texture, weight and movement.

The engine can reduce R&D costs by half, marketing costs by 70 percent and lead times by 90 percent for fabric manufacturers and brand owners, according to Li.

By verifying designs at near zero cost, Heartdub Materials can produce digital, ready-to-wear garments based on clothing patterns. Designers are able to select the texture, pattern and design online, allowing fabric manufacturers to complete preorder presentations at no cost.

These pieces can be showcased and purchased directly through virtual demos and fashion shows hosted by Heartdub One, the company’s database of clothing and avatars.

With Heartdub One, consumers can see how clothes fit on their specific size and shape by building their own digital human based on their particular measurements.

Fashionably Early 

Powered by NVIDIA HDR InfiniBand networking, the Heartdub Materials physics engine boasts a speed of 200 Gbps, improving data transmission efficiency nearly 100-fold.

Taking advantage of NVIDIA Quadro RTX 8000 GPUs and high-speed interconnect technology, Heartdub Materials can easily simulate complex virtual world environments and efficiently process complex ray-tracing and visual computing workloads.

This enables collaboration across the industry, making it possible to complete the process from fabric selection, ready-to-wear design and review to pattern making and production, all virtually.

“AI will revolutionize the fashion industry, and Heartdub Materials is just the beginning,” said Li. “NVIDIA solutions have solved the technical challenges in graphics, allowing us to continue to provide new experiences and create more application scenarios for the fashion industry and its customers.”

Apply to join NVIDIA Inception. 

Modern workloads such as AI and machine learning are putting tremendous pressure on traditional IT infrastructure.

Enterprises that want to stay ahead of these changes can now register to participate in an early access of Project Monterey, an initiative to dramatically improve the performance, manageability and security of their environments.

VMware, Dell Technologies and NVIDIA are collaborating on this project to evolve the architecture for the data center, cloud and edge to one that is software-defined and hardware-accelerated to address the changing application requirements.

AI and other compute-intensive workloads require real-time data streaming analysis, which, along with growing security threats, puts a heavy load on server CPUs. The increased load significantly increases the percentage of processing power required to run tasks that aren’t an integral part of application workloads. This reduces data center efficiency and can prevent IT from meeting its service-level agreements.

Project Monterey is leading the shift to advanced hybrid-cloud data center architectures, which benefit from hypervisor and accelerated software-defined networking, security and storage.

With access to Project Monterey’s preconfigured clusters, enterprises can explore the evolution of VMware Cloud Foundation and take advantage of the disruptive hardware capabilities of the Dell EMC PowerEdge R750 server equipped with NVIDIA BlueField-2 DPU (data processing unit).

Selected functions that used to run on the core CPU are offloaded, isolated and accelerated on the DPU to support new possibilities, including:

• Improved performance for application and infrastructure services
• Enhanced visibility, application security and observability
• Offloaded firewall capabilities
• Improved data center efficiency and cost for enterprise, edge and cloud.

Interested organizations can register for the NVIDIA Project Monterey early access program. Learn more about NVIDIA and VMware’s collaboration to modernize the data center.

If AI, data science, graphics or robotics is your jam, stream the NVIDIA AI Essentials Learning Series this summer.

These intro-level courses provide foundational knowledge to students and early-career developers looking to broaden their areas of expertise. The free series includes over a dozen sessions — each less than an hour long — on topics including conversational AI, ray tracing and robotics fundamentals.

For a deeper dive, register for daylong hands-on courses from the NVIDIA Deep Learning Institute and earn a certificate of competency to boost your resume. To date, DLI has trained more than 300,000 developers through an extensive catalog of self-paced and instructor-led courses and workshops.

Besides picking up new skills to boost your career journey, there are summer sweepstakes at stake: For every 30 minutes a participant watches the learning series, they earn an entry to win a DLI book and free registration codes for self-paced courses.

For those who register for DLI courses on deep learning, accelerated data science and AI on the NVIDIA Jetson Nano, we’re upping the game. Upon successful completion of one or more of these courses, participants will be entered to win an NVIDIA GeForce RTX 3090 GPU in addition to three DLI registration codes and the book.

Here’s a taste of what students, educators and early-career technologists will find in the learning series.

Deep Learning Demystified

This talk covers what deep learning is, what it’s good for and why it’s such a powerful technology. Will Ramey, senior director and global head of developer programs at NVIDIA, talks through the different types of neural networks and explains how they’re trained, optimized and deployed in industries like healthcare and energy. He also discusses some of the challenges organizations face when adopting deep learning.

Ray Tracing in One Weekend

Ray tracing brings stunning, realistic visuals to the movie and gaming industries — but how does it work? In this session, NVIDIA researcher Pete Shirley guides viewers through the process of writing code to generate a ray-traced image of a 3D scene. As he goes, Shirley explains key concepts that form the foundation of ray tracing.

Conversational AI Demystified 

Building a conversational AI model requires developers to achieve two key features: high accuracy and low latency. This session provides an overview of conversionational AI models for automatic speech recognition, natural language processing and text-to-speech. Meriem Bendris, a solution architect at NVIDIA, shares how to train and fine-tune these models using NVIDIA NeMo, the Transfer Learning Toolkit and the NVIDIA Riva application framework.

Jetson Nano: From Zero to Hero in 20 Minutes

Learn how to get started from scratch with the NVIDIA Jetson Nano 2GB developer kit — even if you’re not a developer. This crash course, led by NVIDIA’s Asier Arranz Jiminez, walks through the process from installation to inference with an AI model that detects vehicles, roads and pedestrians.

To get started, visit the NVIDIA AI Essentials Learning Series homepage. Thousands more talks are available to stream free through NVIDIA On-Demand.