Shoppers pondering a new hairstyle can now try styles before committing to curls or a new color. An AI app by Ulta Beauty, the largest specialty beauty retailer in the U.S., uses selfies to show near-instant, highly realistic previews of desired hairstyles.GLAMlab Hair Try On is a digital experience that lets users take a photo, upload a headshot or use a models picture to experiment with different hair colors and styles. Used by thousands of web and mobile app users daily, the experience is powered by the NVIDIA StyleGAN2 generative AI model.Hair color try-ons feature links to Ulta Beauty products so shoppers can achieve the look in real life. The company, which has more than 1,400 stores across the U.S., has found that people who use the virtual tool are more likely to purchase a product than those who dont.Shoppers need to try out hair and makeup styles before they purchase, said Juan Cardelino, director of the computer vision and digital innovation department at Ulta Beauty. As one of the first cosmetics companies to integrate makeup testers in stores, offering try-ons is part of Ulta Beautys DNA whether in physical or digital retail environments.https://blogs.nvidia.com/wp-content/uploads/2024/12/Hair-final-Nvidia-2024.12.18.mp4Adding Ulta Beautys Flair to StyleGAN2GLAMlab is Ulta Beautys first generative AI application, developed by its digital innovation team.To build its AI pipeline, the team turned to StyleGAN2, a style-based neural network architecture for generative adversarial networks, aka GANs. StyleGAN2, developed by NVIDIA Research, uses transfer learning to generate infinite images in a variety of styles.StyleGAN2 is one of the most well-regarded models in the tech community, and, since the source code was available for experimentation, it was the right choice for our application, Cardelino said. For our hairstyle try-on use case, we had to license the model for commercial use, retrain it and put guardrails around it to ensure the AI was only modifying pixels related to hair not distorting any feature of the users face.Available on the Ulta Beauty website and mobile app, the hair style and color try-ons rely on NVIDIA Tensor Core GPUs in the cloud to run AI inference, which takes around 5 seconds to compute the first style and about a second each for subsequent styles.The company next plans to incorporate virtual trials for additional hair categories like wigs and is exploring how the virtual hairstyle try-ons could be connected to in-store styling services.Stylists could use the tool to show our guests how certain hairstyles will look on them, giving them more confidence to try new looks, Cardelino said.Beyond giving customers a new way to interact with Ulta Beautys products, these AI-powered virtual try-ons give users a chance to be creative and explore new possibilities for their personal styles.Hair and makeup are playful categories, Cardelino said. Virtual try-ons are a way to explore options that may be out of a customers comfort zone without needing to commit to a physical change.See the latest work from NVIDIA Research, which has hundreds of scientists and engineers worldwide, with teams focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics.

Stuck in a gaming rut? Get out of the loop this GFN Thursday with four new games joining the GeForce NOW library of over 2,000 supported games.Dive into Square Enixs mind-bending action role-playing games (RPGs) NieR:Automata and NieR Replicant ver.1.22474487139, now streaming in the cloud. Plus, explore HoYoverses Zenless Zone Zero for an adrenaline-packed adventure, just in time for its 1.4 update.Check out GeForce Greats, which offers a look back at the biggest and best moments of PC gaming, from the launch of the GeForce 256 graphics card to the modern era. Follow the GeForce, GeForce NOW, NVIDIA Studio and NVIDIA AI PC channels on X, as well as #GeForceGreats, to join in on the nostalgic journey. Plus, participate in the GeForce LAN Missions from the cloud with GeForce NOW starting on Saturday, Jan. 4, for a chance to win in-game rewards, first come, first served.GeForce NOW members will also be able to launch a virtual stadium for a front-row seat to the CES opening keynote, to be delivered by NVIDIA founder and CEO Jensen Huang on Monday, Jan. 6. Stay tuned to GFN Thursday for more details.A Tale of Two NieRsNieR:Automata and NieR Replicant ver.1.22474487139 two captivating action RPGs from Square Enix delve into profound existential themes and are set in a distant, postapocalyptic future.Existence is futile, except in the cloud.Control androids 2B, 9S and A2 as they battle machine life-forms in a proxy war for human survival in NieR:Automata. The game explores complex philosophical concepts through its multiple endings and perspective shifts, blurring the lines between man and machine. It seamlessly mixes stylish and exhilarating combat with open-world exploration for a diverse gameplay experience.The heros journey leads to the cloud.NieR Replicant ver.1.22474487139, an updated version of the original NieR game, follows a young mans quest to save his sister from a mysterious illness called the Black Scrawl. Uncover dark secrets about their world while encountering a cast of unforgettable characters and making heart-wrenching decisions.Unravel the layers of the emotionally charged world of NieR with each playthrough on GeForce NOW. Experience rich storytelling and intense combat without high-end hardware. Carefully explore every possible loop with extended gaming sessions for Performance and Ultimate members.Find Zen in the CloudDive into the Hollows.Zenless Zone Zero, the free-to-play action role-playing game from HoYoverse, is set in the post-apocalyptic metropolis of New Eridu. Take on the role of a Proxy and guide others through dangerous alternate dimensions to confront an interdimensional threat. The game features a fast-paced, combo-oriented combat system and offers a mix of intense action, character-driven storytelling and exploration of a unique futuristic world.The title comes to the cloud in time for the version 1.4 update, A Storm of Falling Stars, bringing additions to the game for new and experienced players alike. Joining the roster of playable characters are Frost Anomaly agent Hoshimi Miyabi and Electric Attack agent Asaba Harumasa. Plus, the revamped Decibel system allows individual characters to collect and use Decibels instead of sharing across the squad, offering a new layer of strategy. Explore two new areas, Port Elpis and Reverb Arena, and try out the new Hollow Zero-Lost Void mode.Experience the adventure on GeForce NOW and dive deeper into New Eridu across devices with a Performance or Ultimate membership. Snag some in-game loot by following the GeForce NOW social channels (X, Facebook, Instagram, Threads) and be on the lookout for for a limited-quantity redemption code for a free reward package including 20,000 Dennies, three Official Investigator Logs and three W-Engine Power Supplies.Fresh ArrivalsLook for the following games available to stream in the cloud this week:NieR:Automata (Steam)NieR Replicant ver.1.22474487139 (Steam)Replikant Chat (Steam)Zenless Zone Zero v1.4 (HoYoverse)What are you planning to play this weekend? Let us know on X or in the comments below.

For more than two decades, the NVIDIA Graduate Fellowship Program has supported graduate students doing outstanding work relevant to NVIDIA technologies. Today, the program announced the latest awards of up to $60,000 each to 10 Ph.D. students involved in research that spans all areas of computing innovation.Selected from a highly competitive applicant pool, the awardees will participate in a summer internship preceding the fellowship year. Their work puts them at the forefront of accelerated computing tackling projects in autonomous systems, computer architecture, computer graphics, deep learning, programming systems, robotics and security.The NVIDIA Graduate Fellowship Program is open to applicants worldwide.The 2025-2026 fellowship recipients are:Anish Saxena, Georgia Institute of Technology Rethinking data movement across the stack spanning large language model architectures, system software and memory systems to improve the efficiency of LLM training and inference.Jiawei Yang, University of Southern California Creating scalable, generalizable foundation models for autonomous systems through self-supervised learning, leveraging neural reconstruction to capture detailed environmental geometry and dynamic scene behaviors, and enhancing adaptability in robotics, digital twin technologies and autonomous driving.Jiayi (Eris) Zhang, Stanford University Developing intelligent algorithms, models and tools for enhancing user creativity and productivity in design, animation and simulation.Ruisi Cai, University of Texas at Austin Working on efficient training and inference for large foundation models as well as AI security and privacy.Seul Lee, Korea Advanced Institute of Science and Technology Developing generative models for molecules and exploration strategies in chemical space for drug discovery applications.Sreyan Ghosh, University of Maryland, College Park Advancing audio processing and reasoning by designing resource-efficient models and training techniques, improving audio representation learning and enhancing audio perception for AI systems.Tairan He, Carnegie Mellon University Researching the development of humanoid robots, with a focus on advancing whole-body loco-manipulation through large-scale simulation-to-real learning.Xiaogeng Liu, University of WisconsinMadison Developing robust and trustworthy AI systems, with an emphasis on evaluating and enhancing machine learning models to ensure consistent performance and resilience against diverse attacks and unforeseen inputs.Yunze Man, University of Illinois Urbana-Champaign Developing vision-centric reasoning models for multimodal and embodied AI agents, with a focus on object-centric perception systems in dynamic scenes, vision foundation models for open-world scene understanding and generation, and large multimodal models for embodied reasoning and robotics planning.Zhiqiang Xie, Stanford University Building infrastructures to enable more efficient, scalable and complex compound AI systems while enhancing the observability and reliability of such systems.We also acknowledge the 2025-2026 fellowship finalists:Bo Zhao, University of California, San DiegoChenning Li, Massachusetts Institute of TechnologyDacheng Li, University of California, BerkeleyJiankai Sun, Stanford UniversityWenlong Huang, Stanford University

Editors note: This post is part of the AI On blog series, which explores the latest techniques and real-world applications of agentic AI, chatbots and copilots. The series will also highlight the NVIDIA software and hardware powering advanced AI agents, which form the foundation of AI query engines that gather insights and perform tasks to transform everyday experiences and reshape industries.To enhance productivity and upskill workers, organizations worldwide are seeking ways to provide consistent, around-the-clock customer service with greater speed, accuracy and scale.Intelligent AI agents offer one such solution. They deliver advanced problem-solving capabilities and integrate vast and disparate sources of data to understand and respond to natural language.Powered by generative AI and agentic AI, digital avatars are boosting efficiency across industries like healthcare, telecom, manufacturing, retail and more. According to Gartner, by 2028, 45% of organizations with more than 500 employees will use employee AI avatars to expand the capacity of human capital.1From educating prospects on policies to giving customers personalized solutions, AI is helping organizations optimize revenue streams and elevate employee knowledge and productivity.Where Context-Aware AI Avatars Are Most ImpactfulStaying ahead in a competitive, evolving market requires continuous learning and analysis. AI avatars also referred to as digital humans are addressing key concerns and enhancing operations across industries.One key benefit of agentic digital human technology is the ability to offer consistent, multilingual support and personalized guidance for a variety of use cases.For instance, a medical-based AI agent can provide 24/7 virtual intake and support telehealth services. Or, a virtual financial advisor can help enhance client security and financial literacy by alerting bank customers of potential fraud, or offering personalized offers and investment tips based on their unique portfolio.These digital humans boost efficiency, cut costs and enhance customer loyalty. Some key ways digital humans can be applied include:Personalized, On-Brand Customer Assistance: A digital human interface can provide a personal touch when educating new customers on a companys products and service portfolios. They can provide ongoing customer support, offering immediate responses and solving problems without the need for a live operator.Enhanced Employee Onboarding: Intelligent AI assistants can offer streamlined, adaptable, personalized employee onboarding, whether in hospitals or offices, by providing consistent access to updated institutional knowledge at scale. With pluggable, customizable retrieval-augmented generation (RAG), these assistants can deliver real-time answers to queries while maintaining a deep understanding of company-specific data.Seamless Communication Across Languages: In global enterprises, communication barriers can slow down operations. AI-powered avatars with natural language processing capabilities can communicate effortlessly across languages. This is especially useful in customer service or employee training environments where multilingual support is crucial.Learn more by listening to the NVIDIA AI Podcast episode with Kanjun Qiu, CEO of Imbue, who shares insights on how to build smarter AI agents.Interactive AI Agents With Text-to-Speech and Speech-to-TextWith text-to-speech and speech-to-text capabilities, AI agents can offer enhanced interactivity and engagement in customer service interactions.SoftServe, an IT consulting and digital services provider, has built several digital humans for a variety of use cases, highlighting the technologys potential to enhance user experiences.SoftServes Digital Concierge is accelerated by NVIDIA AI Blueprints and NVIDIA ACE technologies to rapidly deploy scalable, customizable digital humans across diverse infrastructures.GEN, SoftServes virtual customer service assistant and digital concierge, makes customer service more engaging by providing lifelike interactions, continuous availability, personalized responses and simultaneous access to all necessary knowledge bases.SoftServe also developed FINNA, an AI-powered virtual financial advisor that can provide financial guidance tailored to a clients profile and simplify complex financial terminology. It helps streamline onboarding and due diligence, supporting goal-oriented financial planning and risk assessment. AISHA is another AI-powered digital human developed by SoftServe with NVIDIA technology. Created for the UAE Ministry of Justice, the digital human significantly improves judicial processes by reducing case review times, enhancing the accuracy of rulings and providing rapid access to legal databases. It demonstrates how generative AI can bridge the gap between technology and meaningful user interaction to enhance customer service and operational efficiency in the judicial sector.How to Design AI Agents With Avatar and Speech FeaturesDesigning AI agents with avatar and speech features involves several key stepsDetermine the use case: Choose between 2D or 3D avatars based on the required level of immersion and interaction.Avatar development:For 3D avatars, use specialized software and technical expertise to create lifelike movements and photorealism.For 2D avatars, opt for quicker development suitable for web-embedded solutions.Integrate speech technologies: Use NVIDIA Riva for world-class automatic speech recognition, along with text-to-speech to enable verbal interactions.Rendering options: Use NVIDIA Omniverse RTX Renderer technology or Unreal Engine tools for 3D avatars to achieve high-quality output and compute efficiency.Deployment: Tap cloud-native deployment for real-time output and scalability, particularly for interactive web or mobile applications.For an overview on how to design interactive customer service tools, read the technical blogs on how to Build a Digital Human Interface for AI Apps With an NVIDIA AI Blueprint and Expanding AI Agent Interface Options With 2D and 3D Digital Human Avatars.NVIDIA AI Blueprint for Digital HumansThe latest release of the NVIDIA AI Blueprint for digital humans introduces several updates that enhance the interactivity and responsiveness of digital avatars, including dynamic switching between RAG models. Users can experience this directly in preview.The integration of the Audio2Face-2D microservice in the blueprint means developers can create 2D digital humans, which require significantly less processing power compared with 3D models, for web- and mobile-based applications.2D avatars are better suited for simpler interactions and platforms where photorealism isnt necessary. This makes them ideal for scenarios like telemedicine, where quick loading times with lower bandwidth requirements are crucial.Another significant update is the introduction of user attention detection through vision AI. This feature enables digital humans to detect when a user is present even if they are idle or on mute and initiate interaction, such as greeting the user. This capability is particularly beneficial in kiosk scenarios, where engaging users proactively can enhance the service experience.Getting StartedNVIDIA AI Blueprints make it easy to start building and setting up virtual assistants by offering ready-made workflows and tools to accelerate deployment. Whether for a simple AI-powered chatbot or a fully animated digital human interface, the blueprints offer resources to create AI assistants that are scalable, aligned with an organizations brand and deliver a responsive, efficient customer support experience.1. Gartner, Hype Cycle for the Future of Work, 2024, Tori Paulman, Emily Rose, etc., July 2024GARTNER is a registered trademark and service mark and Hype Cycle is a trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

Imagine a future in which everyone is empowered to build and use their own AI agents. That future may not be far off, as new software is infused with intelligence through collaborative AI systems that work alongside users rather than merely automating tasks.In this episode of the NVIDIA AI Podcast, Kanjun Qiu, CEO of Imbue, discusses the rise of AI agents, drawing parallels between the personal computer revolution of the late 1970s and 80s and todays AI agent transformation. She details Imbues approach to building reasoning capabilities into its products, the challenges of verifying the correctness of AI outputs and how Imbue is focusing on post-training and fine-tuning to improve verification capabilities.The AI Podcast Imbue CEO Kanjun Qiu on Transforming AI Agents Into Personal Collaborators Ep. 239Learn more about Imbue, and read more about AI agents, including how virtual assistants can enhance customer service experiences.And hear more about the future of AI and graphics by tuning in to the CES keynote, delivered by NVIDIA founder and CEO Jensen Huang live in Las Vegas on Monday, Jan. 6, at 6:30 p.m. PT.Time Stamps1:21 What are AI agents? And Imbues approach to them.9:00 Where are AI agents being used the most today?17:05 Why building a good user experience around agents requires invention.26:28 How reasoning and verification capabilities factor into Imbues products.You Might Also LikeZoom CTO Xuedong XD Huang on How AI Revolutionizes ProductivityZoom is now transforming into an AI-first platform. CTO Xuedong Huang discusses Zooms AI Companion 2.0 and the companys federated AI strategy, which aims to integrate multiple large language models to enhance productivity and collaboration.How Roblox Uses Generative AI to Enhance User ExperiencesRoblox is enhancing its colorful online platform with generative AI to improve user safety and inclusivity through features like automated chat filters and real-time text translation. Anupam Singh, VP of AI and growth engineering at Roblox, explores how AI coding assistants are helping creators focus more on creative expression.Rendered.ai CEO Nathan Kundtz on Using AI to Build Better AIData is crucial for training AI and machine learning systems, and synthetic data offers a solution to the challenges of compiling real-world data. Nathan Kundtz, founder and CEO of Rendered.ai, discusses how his companys platform generates synthetic data to enhance AI models.Subscribe to the AI PodcastGet the AI Podcast through Apple Podcasts, Google Podcasts, Google Play, Castbox, DoggCatcher, Overcast, PlayerFM, Pocket Casts, Podbay, PodBean, PodCruncher, PodKicker, Soundcloud, Spotify, Stitcher and TuneIn.

In enterprise AI, understanding and working across multiple languages is no longer optional its essential for meeting the needs of employees, customers and users worldwide.Multilingual information retrieval the ability to search, process and retrieve knowledge across languages plays a key role in enabling AI to deliver more accurate and globally relevant outputs.Enterprises can expand their generative AI efforts into accurate, multilingual systems using NVIDIA NeMo Retriever embedding and reranking NVIDIA NIM microservices, which are now available on the NVIDIA API catalog. These models can understand information across a wide range of languages and formats, such as documents, to deliver accurate, context-aware results at massive scale.With NeMo Retriever, businesses can now:Extract knowledge from large and diverse datasets for additional context to deliver more accurate responses.Seamlessly connect generative AI to enterprise data in most major global languages to expand user audiences.Deliver actionable intelligence at greater scale with 35x improved data storage efficiency through new techniques such as long context support and dynamic embedding sizing.New NeMo Retriever microservices reduce storage volume needs by 35x, enabling enterprises to process more information at once and fit large knowledge bases on a single server. This makes AI solutions more accessible, cost-effective and easier to scale across organizations.Leading NVIDIA partners like DataStax, Cohesity, Cloudera, Nutanix, SAP, VAST Data and WEKA are already adopting these microservices to help organizations across industries securely connect custom models to diverse and large data sources. By using retrieval-augmented generation (RAG) techniques, NeMo Retriever enables AI systems to access richer, more relevant information and effectively bridge linguistic and contextual divides.Wikidata Speeds Data Processing From 30 Days to Under Three DaysIn partnership with DataStax, Wikimedia has implemented NeMo Retriever to vector-embed the content of Wikipedia, serving billions of users. Vector embedding or vectorizing is a process that transforms data into a format that AI can process and understand to extract insights and drive intelligent decision-making.Wikimedia used the NeMo Retriever embedding and reranking NIM microservices to vectorize over 10 million Wikidata entries into AI-ready formats in under three days, a process that used to take 30 days. That 10x speedup enables scalable, multilingual access to one of the worlds largest open-source knowledge graphs.This groundbreaking project ensures real-time updates for hundreds of thousands of entries that are being edited daily by thousands of contributors, enhancing global accessibility for developers and users alike. With Astra DBs serverless model and NVIDIA AI technologies, the DataStax offering delivers near-zero latency and exceptional scalability to support the dynamic demands of the Wikimedia community.DataStax is using NVIDIA AI Blueprints and integrating the NVIDIA NeMo Customizer, Curator, Evaluator and Guardrails microservices into the LangFlow AI code builder to enable the developer ecosystem to optimize AI models and pipelines for their unique use cases and help enterprises scale their AI applications.Language-Inclusive AI Drives Global Business ImpactNeMo Retriever helps global enterprises overcome linguistic and contextual barriers and unlock the potential of their data. By deploying robust, AI solutions, businesses can achieve accurate, scalable and high-impact results.NVIDIAs platform and consulting partners play a critical role in ensuring enterprises can efficiently adopt and integrate generative AI capabilities, such as the new multilingual NeMo Retriever microservices. These partners help align AI solutions to an organizations unique needs and resources, making generative AI more accessible and effective. They include:Cloudera plans to expand the integration of NVIDIA AI in the Cloudera AI Inference Service. Currently embedded with NVIDIA NIM, Cloudera AI Inference will include NVIDIA NeMo Retriever to improve the speed and quality of insights for multilingual use cases.Cohesity introduced the industrys first generative AI-powered conversational search assistant that uses backup data to deliver insightful responses. It uses the NVIDIA NeMo Retriever reranking microservice to improve retrieval accuracy and significantly enhance the speed and quality of insights for various applications.SAP is using the grounding capabilities of NeMo Retriever to add context to its Joule copilot Q&A feature and information retrieved from custom documents.VAST Data is deploying NeMo Retriever microservices on the VAST Data InsightEngine with NVIDIA to make new data instantly available for analysis. This accelerates the identification of business insights by capturing and organizing real-time information for AI-powered decisions.WEKA is integrating its WEKA AI RAG Reference Platform (WARRP) architecture with NVIDIA NIM and NeMo Retriever into its low-latency data platform to deliver scalable, multimodal AI solutions, processing hundreds of thousands of tokens per second.Breaking Language Barriers With Multilingual Information RetrievalMultilingual information retrieval is vital for enterprise AI to meet real-world demands. NeMo Retriever supports efficient and accurate text retrieval across multiple languages and cross-lingual datasets. Its designed for enterprise use cases such as search, question-answering, summarization and recommendation systems.Additionally, it addresses a significant challenge in enterprise AI handling large volumes of large documents. With long-context support, the new microservices can process lengthy contracts or detailed medical records while maintaining accuracy and consistency over extended interactions.These capabilities help enterprises use their data more effectively, providing precise, reliable results for employees, customers and users while optimizing resources for scalability. Advanced multilingual retrieval tools like NeMo Retriever can make AI systems more adaptable, accessible and impactful in a globalized world.AvailabilityDevelopers can access the multilingual NeMo Retriever microservices, and other NIM microservices for information retrieval, through the NVIDIA API catalog, or a no-cost, 90-day NVIDIA AI Enterprise developer license.Learn more about the new NeMo Retriever microservices and how to use them to build efficient information retrieval systems.

NVIDIA is taking the wraps off a new compact generative AI supercomputer, offering increased performance at a lower price with a software upgrade.The new NVIDIA Jetson Orin Nano Super Developer Kit, which fits in the palm of a hand, provides everyone from commercial AI developers to hobbyists and students, gains in generative AI capabilities and performance. And the price is now $249, down from $499.Available today, it delivers as much as a 1.7x leap in generative AI inference performance, a 70% increase in performance to 67 INT8 TOPS, and a 50% increase in memory bandwidth to 102GB/s compared with its predecessor.Whether creating LLM chatbots based on retrieval-augmented generation, building a visual AI agent, or deploying AI-based robots, the Jetson Orin Nano Super is an ideal solution to fetch.The Gift That Keeps on GivingThe software updates available to the new Jetson Orin Nano Super will also boost generative AI performance for those who already own the Jetson Orin Nano Developer Kit.Jetson Orin Nano Super is suited for those interested in developing skills in generative AI, robotics or computer vision. As the AI world is moving from task-specific models into foundation models, it also provides an accessible platform to transform ideas into reality.Powerful Performance With Super for Generative AIThe enhanced performance of the Jetson Orin Nano Super delivers gains for all popular generative AI models and transformer-based computer vision.The developer kit consists of a Jetson Orin Nano 8GB system-on-module (SoM) and a reference carrier board, providing an ideal platform for prototyping edge AI applications.The SoM features an NVIDIA Ampere architecture GPU with tensor cores and a 6-core Arm CPU, facilitating multiple concurrent AI application pipelines and high-performance inference. It can support up to four cameras, offering higher resolution and frame rates than previous versions.Extensive Generative AI Software Ecosystem and CommunityGenerative AI is evolving quickly. The NVIDIA Jetson AI lab offers immediate support for those cutting-edge models from the open-source community and provides easy-to-use tutorials. Developers can also get extensive support from the broader Jetson community and inspiration from projects created by developers.Jetson runs NVIDIA AI software including NVIDIA Isaac for robotics, NVIDIA Metropolis for vision AI and NVIDIA Holoscan for sensor processing. Development time can be reduced with NVIDIA Omniverse Replicator for synthetic data generation and NVIDIA TAO Toolkit for fine-tuning pretrained AI models from the NGC catalog.Jetson ecosystem partners offer additional AI and system software, developer tools and custom software development. They can also help with cameras and other sensors, as well as carrier boards and design services for product solutions.Boosting Jetson Orin Performance for All With Super ModeThe software updates to boost 1.7X generative AI performance will also be available to the Jetson Orin NX and Orin Nano series of systems on modules.Existing Jetson Orin Nano Developer Kit owners can upgrade the JetPack SDK to unlock boosted performance today.Learn more about Jetson Orin Nano Super Developer Kit.See notice regarding software product information.

On Jan. 6 at 6:30 p.m. PT, NVIDIA founder and CEO Jensen Huang with his trademark leather jacket and an unwavering vision will step onto the CES 2025 stage.From humble beginnings as a busboy at a Dennys to founding NVIDIA, Huangs story embodies innovation and perseverance.Huang has been named the worlds best CEO by Fortune and The Economist, as well as one of TIME magazines 100 most influential people in the world.Today, NVIDIA is a driving force behind breakthroughs in AI and accelerated computing, technologies transforming industries ranging from healthcare, to automotive and entertainment.Across the globe, NVIDIAs innovations enable advanced chatbots, robots, software-defined vehicles, sprawling virtual worlds, hypersynchronized factory floors and much more.NVIDIAs accelerated computing and AI platforms power hundreds of millions of computers, available from major cloud providers and server manufacturers.They fuel 76% of the worlds fastest supercomputers on the TOP500 list and are supported by a thriving community of more than 5 million developers.For decades, Huang has led NVIDIA through revolutions that ripple across industries.GPUs redefined gaming as an art form, and NVIDIAs AI tools empower labs, factory floors and Hollywood sets. From self-driving cars to automated industrial processes, these tools are foundational to the next generation of technological breakthroughs.CES has long been the stage for the unveiling of technological advancements, and Huangs keynote is no exception.Since its inception in 1967, CES has unveiled iconic innovations, including transistor radios, VCRs and HDTVs.Over the decades, CES has launched numerous NVIDIA flagship innovations, from a first look at NVIDIA SHIELD to NVIDIA DRIVE for autonomous vehicles.NVIDIA at CES 2025The keynote is just the beginning.From Jan. 7-10, NVIDIA will host press, analysts, customers and partners at the Fontainebleau Resort Las Vegas.The space will feature hands-on demos showcasing innovations in AI, robotics and accelerated computing across NVIDIAs automotive, consumer, enterprise, Omniverse and robotics portfolios.Meanwhile, NVIDIAs technologies will take center stage on the CES show floor at the Las Vegas Convention Center, where partners will highlight AI-powered technologies, immersive gaming experiences and groundbreaking automotive advancements.Attendees can also participate in NVIDIAs Explore to Win program, an interactive scavenger hunt featuring missions, points and prizes.Curious about the future? Tune in live on NVIDIAs website or the companys YouTube channels to witness how NVIDIA is shaping the future of technology.

Editors note: This article, originally published on May 20, 2022, has been updated.Advances in extended reality have already changed the way we work, live and play, and its just getting started.Extended reality, or XR, is an umbrella category that covers a spectrum of newer, immersive technologies, including virtual reality, augmented reality and mixed reality.From gaming to virtual production to product design, XR has enabled people to create, collaborate and explore in computer-generated environments like never before.What Is Extended Reality?Virtual, augmented and mixed reality are all elements of XR technology.Virtual reality puts users inside a virtual environment. VR users typically wear a headset that transports them into a virtual world one moment theyre standing in a physical room, and the next theyre immersed in a simulated environment.The latest VR technologies push these boundaries, making these environments look and behave more like the real world. Theyre also adding support for additional senses, including touch, sound and smell.With VR, gamers can become fully immersed in a video game, designers and customers can review building projects to finalize details prior to construction, and retailers can test virtual displays before committing to a physical one.Augmented reality is when a rendered image is overlaid onto the real world. The mobile game Pokmon GO famously brought AR to the mainstream by showing computer-rendered monsters standing on lawns and sidewalks as players roam their neighborhoods.AR graphics are visible through cell phones, tablets and other devices, bringing a new kind of interactive experience to users. Navigating directions, for example, can be improved with AR. Rather than following a 2D map, a windshield can superimpose directions over ones view of the road, with simulated arrows directing the driver exactly where to turn.Mixed reality is a seamless integration of the real world and rendered graphics, which creates an environment in which users can directly interact with the digital and physical worlds together.With MR, real and virtual objects blend, and are presented together within a single display. Users can experience MR environments through a headset, phone or tablet, and can interact with digital objects by moving them around or placing them in the physical world.There are two types of MR:Mixing virtual objects into the real world for instance, where a user sees the real world through cameras in a VR headset with virtual objects seamlessly mixed into the view.Mixing real-world objects into virtual worlds for example, a camera view of a VR participant mixed into the virtual world, like watching a VR gamer playing in a virtual world.The History of XRTo understand how far XR has come, consider its origins in VR.VR began in the federal sector, where it was used to train people in flight simulators. The energy and automotive design industries were also early adopters. These simulation and visualization VR use cases required large supercomputers. It also needed dedicated spaces, including powerwalls, which are ultra-high-resolution displays, and VR CAVEs, which are empty rooms that have the VR environment projected on each surface, from the walls to the ceiling.For decades, VR remained unaffordable for most users, and the small VR ecosystem was mainly composed of large institutions and academic researchers.But early in the previous decade, several key component technologies reached a tipping point, which precipitated the launch of the HTC Vive and Oculus Rift head-mounted displays (HMDs), along with the SteamVR runtime.Individuals could now purchase personal HMDs to experience great immersive content. And they could drive those HMDs and experiences from an individual PC or workstation with a powerful GPU.Suddenly, VR was accessible to millions of individuals, and a large ecosystem quickly sprung up, filled with innovation and enthusiasm.In recent years, a new wave of VR innovation started with the launch of all-in-one (AIO) headsets. Previously, fully immersive VR experiences required a physical connection to a powerful PC. The HMD couldnt operate as a self-contained device, as it had no operating system and no ability to compute the image.But with AIO headsets, users gained access to a dedicated device with a simple setup that could deliver fully tracked VR anywhere, anytime. Coupled with the innovation of VR streaming technology, users could now experience powerful VR environments, even while on the go.Latest Trends in XRHigh-quality XR is becoming increasingly accessible. Consumers worldwide are purchasing AIOs to experience XR, from immersive gaming to remote learning to virtual training. Large enterprises are adding XR into their workflows and design processes. XR drastically improves design implementation with the inclusion of a digital twin.Image courtesy of Innoactive.And one of todays biggest trends is streaming XR experiences through 5G from the cloud. This removes the need to be tethered to workstations or limit experiences to a single space.By streaming over 5G from the cloud, people can use XR devices and get the computational power to run XR experiences from a data center, regardless of location and time. Advanced solutions like NVIDIA CloudXR are making immersive streaming more accessible, so more XR users can experience high-fidelity environments from anywhere.AR is also becoming more common. After Pokmon GO became a household name, AR emerged in a number of additional consumer-focused areas. Many social media platforms added filters that users could overlay on their faces. Organizations in retail incorporated AR to showcase photorealistic rendered 3D products, enabling customers to place these products in a room and visualize it in any space.https://developer.download.nvidia.com/cloudxr/videos/compressed/CloudXR_mclaren_002.mp4Plus, enterprises in various industries like architecture, manufacturing, healthcare and more are using the technology to vastly improve workflows and create unique, interactive experiences. For example, architects and design teams are integrating AR for construction project monitoring, so they can see onsite progress and compare it to digital designs.And though its still fairly new, MR is developing in the XR space. Trends are shown through the emergence of many new headsets built for MR, including the Varjo XR-3. With MR headsets, professionals in engineering, design, simulation and research can develop and interact with their 3D models in real life.Varjo XR-3 headset. Image courtesy of Varjo.The Future of XRAs XR technology advances, another technology is propelling users into a new era: artificial intelligence.AI will play a major role in the XR space, from virtual assistants helping designers in VR to intelligent AR overlays that can walk individuals through do-it-yourself projects.For example, imagine wearing a headset and telling the content what to do through natural speech and gestures. With hands-free and speech-driven virtual agents at the ready, even non-experts will be able to create amazing designs, complete exceedingly complex projects and harness the capabilities of powerful applications.Platforms like NVIDIA Omniverse have already changed how users create 3D simulations and virtual worlds. Omniverse allows users from across the globe to develop and operate digital twin simulations. The platform provides users with the flexibility to portal into the physically accurate, fully ray-traced virtual world through 2D monitors, or their preferred XR experience, so they can experience vast virtual worlds immersively.Entering the next evolution of XR, the possibilities are virtually limitless.What Is Spatial Computing?Unlike traditional digital experiences, which are confined to screens, spatial computing places virtual elements directly into the physical world, creating more natural and intuitive interactions. The technology combines sensors, cameras and AI-driven software to recognize and respond to real-world elements, so users can interact with digital objects as if they were tangible.Supported by platforms like NVIDIA Omniverse, spatial computing has broad applications, from industrial design and training to navigation and entertainment. For example, designers could use it to visualize 3D prototypes in real space, or field teams could harness the technology to receive AR guidance overlaid onto real equipment.As spatial computing evolves, its poised to reshape interactions with digital information, making it part of everyday physical environments and expanding the possibilities for XR in practical and creative ways.Learn more about how organizations can use NVIDIA XR technologies.

Editors note: This post is part of Into the Omniverse, a series focused on how developers, 3D practitioners, and enterprises can transform their workflows using the latest advances in OpenUSD and NVIDIA Omniverse.Scalable simulation technologies are driving the future of autonomous robotics by reducing development time and costs.Universal Scene Description (OpenUSD) provides a scalable and interoperable data framework for developing virtual worlds where robots can learn how to be robots. With SimReady OpenUSD-based simulations, developers can create limitless scenarios based on the physical world.And NVIDIA Isaac Sim is advancing perception AI-based robotics simulation. Isaac Sim is a reference application built on the NVIDIA Omniverse platform for developers to simulate and test AI-driven robots in physically based virtual environments.At AWS re:Invent, NVIDIA announced that Isaac Sim is now available on Amazon EC2 G6e instances powered by NVIDIA L40S GPUs. These powerful instances enhance the performance and accessibility of Isaac Sim, making high-quality robotics simulations more scalable and efficient.These advancements in Isaac Sim mark a significant leap for robotics development. By enabling realistic testing and AI model training in virtual environments, companies can reduce time to deployment and improve robot performance across a variety of use cases.Advancing Robotics Simulation With Synthetic Data GenerationRobotics companies like Cobot, Field AI and Vention are using Isaac Sim to simulate and validate robot performance while others, such as SoftServe and Tata Consultancy Services, use synthetic data to bootstrap AI models for diverse robotics applications.The evolution of robot learning has been deeply intertwined with simulation technology. Early experiments in robotics relied heavily on labor-intensive, resource-heavy trials. Simulation is a crucial tool for the creation of physically accurate environments where robots can learn through trial and error, refine algorithms and even train AI models using synthetic data.Physical AI describes AI models that can understand and interact with the physical world. It embodies the next wave of autonomous machines and robots, such as self-driving cars, industrial manipulators, mobile robots, humanoids and even robot-run infrastructure like factories and warehouses.Robotics simulation, which forms the second computer in the three computer solution, is a cornerstone of physical AI development that lets engineers and researchers design, test and refine systems in a controlled virtual environment.A simulation-first approach significantly reduces the cost and time associated with physical prototyping while enhancing safety by allowing robots to be tested in scenarios that might otherwise be impractical or hazardous in real life.With a new reference workflow, developers can accelerate the generation of synthetic 3D datasets with generative AI using OpenUSD NIM microservices. This integration streamlines the pipeline from scene creation to data augmentation, enabling faster and more accurate training of perception AI models.Synthetic data can help address the challenge of limited, restricted or unavailable data needed to train various types of AI models, especially in computer vision. Developing action recognition models is a common use case that can benefit from synthetic data generation.To learn how to create a human action recognition video dataset with Isaac Sim, check out the technical blog on Scaling Action Recognition Models With Synthetic Data. 3D simulations offer developers precise control over image generation, eliminating hallucinations.Robotic Simulation for HumanoidsHumanoid robots are the next wave of embodied AI, but they present a challenge at the intersection of mechatronics, control theory and AI. Simulation is crucial to solving this challenge by providing a safe, cost-effective and versatile platform for training and testing humanoids.With NVIDIA Isaac Lab, an open-source unified framework for robot learning built on top of Isaac Sim, developers can train humanoid robot policies at scale via simulations. Leading commercial robot makers are adopting Isaac Lab to handle increasingly complex movements and interactions.NVIDIA Project GR00T, an active research initiative to enable the humanoid robot ecosystem of builders, is pioneering workflows such as GR00T-Gen to generate robot tasks and simulation-ready environments in OpenUSD. These can be used for training generalist robots to perform manipulation, locomotion and navigation.Recently published research from Project GR00T also shows how advanced simulation can be used to train interactive humanoids. Using Isaac Sim, the researchers developed a single unified controller for physically simulated humanoids called MaskedMimic. The system is capable of generating a wide range of motions across diverse terrains from intuitive user-defined intents.Physics-Based Digital Twins Simplify AI TrainingPartners across industries are using Isaac Sim, Isaac Lab, Omniverse, and OpenUSD to design, simulate and deploy smarter, more capable autonomous machines:Agility uses Isaac Lab to create simulations that let simulated robot behaviors transfer directly to the robot, making it more intelligent, agile and robust when deployed in the real world.Cobot uses Isaac Sim with its AI-powered cobot, Proxie, to optimize logistics in warehouses, hospitals, manufacturing sites and more.Cohesive Robotics has integrated Isaac Sim into its software framework called Argus OS for developing and deploying robotic workcells used in high-mix manufacturing environments.Field AI, a builder of robot foundation models, uses Isaac Sim and Isaac Lab to evaluate the performance of its models in complex, unstructured environments across industries such as construction, manufacturing, oil and gas, mining, and more.Fourier uses NVIDIA Isaac Gym and Isaac Lab to train its GR-2 humanoid robot, using reinforcement learning and advanced simulations to accelerate development, enhance adaptability and improve real-world performance.Foxglove integrates Isaac Sim and Omniverse to enable efficient robot testing, training and sensor data analysis in realistic 3D environments.Galbot used Isaac Sim to verify the data generation of DexGraspNet, a large-scale dataset of 1.32 million ShadowHand grasps, advancing robotic hand functionality by enabling scalable validation of diverse object interactions across 5,355 objects and 133 categories.Standard Bots is simulating and validating the performance of its R01 robot used in manufacturing and machining setups.Wandelbots integrates its NOVA platform with Isaac Sim to create physics-based digital twins and intuitive training environments, simplifying robot interaction and enabling seamless testing, validation and deployment of robotic systems in real-world scenarios.Learn more about how Wandelbots is advancing robot learning with NVIDIA technology in this livestream recording:Get Plugged Into the World of OpenUSDNVIDIA experts and Omniverse Ambassadors are hosting livestream office hours and study groups to provide robotics developers with technical guidance and troubleshooting support for Isaac Sim and Isaac Lab. Learn how to get started simulating robots in Isaac Sim with this new, free course on NVIDIA Deep Learning Institute (DLI).For more on optimizing OpenUSD workflows, explore the new self-paced Learn OpenUSD training curriculum that includes free DLI courses for 3D practitioners and developers. For more resources on OpenUSD, explore the Alliance for OpenUSD forum and the AOUSD website.Dont miss the CES keynote delivered by NVIDIA founder and CEO Jensen Huang live in Las Vegas on Monday, Jan. 6, at 6:30 p.m. PT for more on the future of AI and graphics.Stay up to date by subscribing to NVIDIA news, joining the community, and following NVIDIA Omniverse on Instagram, LinkedIn, Medium and X.Featured image courtesy of Fourier.

From heart-pounding action games to remastered classics, theres something for everyone this GFN Thursday.Six new titles join the cloud this week, starting with The Thing: Remastered. Face the horrors of the Antarctic as the game oozes onto GeForce NOW. Nightdive Studios revival of the cult-classic 2002 survival-horror game came to the cloud as a surprise at the PC Gaming Show last week. Since then, GeForce NOW members have been able to experience all the bone-chilling action in the sequel to the title based on Universal Pictures genre-defining 1982 film.And dont miss out on the limited-time GeForce NOW holiday sale, which offers 50% off the first month of a new Ultimate or Performance membership. The 25% off Day Pass sale ends today take advantage of the offer to experience 24 hours of cloud gaming with all the benefits of Ultimate or Performance membership.Its Alive!Freeze enemies, not frame rates.The Thing: Remastered brings the 2002 third-person shooter into the modern era with stunning visual upgrades, including improved character models, textures and animations, all meticulously crafted to enhance the games already-tense atmosphere.Playing as Captain J.F. Blake, leader of a U.S. governmental rescue team, navigate the blood-curdling aftermath of the events depicted in the original film. Trust is a precious commodity as members command their squad through 11 terrifying levels, never knowing who might harbor the alien within. The remaster introduces enhanced lighting and atmospheric effects that make the desolate research facility more immersive and frightening than ever.With an Ultimate or Performance membership, stream this blood-curdling experience in all its remastered glory without the need for high-end hardware. GeForce NOW streams from powerful GeForce RTX-powered servers in the cloud, rendering every shadow, every flicker of doubt in teammates eyes and every grotesque transformation with crystal-clear fidelity.The Performance tier now offers up to 1440p resolution, allowing members to immerse themselves in the games oppressive atmosphere with even greater clarity. Ultimate members can experience the paranoia-inducing gameplay at up to 4K resolution and 120 frames per second, making every heart-pounding moment feel more real than ever.Feast on ThisDive into the depths of a gothic vampire saga, slide through feudal Japan and flip burgers at breakneck speed with GeForce NOW and the power of the cloud. Grab a controller and rally the gaming squad to stream these mouth-watering additions.Time to rise again.The highly anticipated Legacy of Kain Soul Reaver 1&2 Remastered from Aspyr and Crystal Dynamics breathes new life into the classic vampire saga genre. These beloved titles have been meticulously overhauled to offer stunning visuals and improved controls. Join the epic conflict of Kain and Raziel in the gothic world of Nosgoth and traverse between the Spectral and Material Realms to solve puzzles, reveal new paths and defeat foes.Defend the forbidden village.The Spirit of the Samurai from Digital Mind Games and Kwalee brings a blend of Souls and Metroidvania elements to feudal Japan. This stop-motion inspired 2D action-adventure game offers three playable characters and intense combat with legendary Japanese weapons, all set against a backdrop of mythological landscapes.The ice cream machine actually works.Or take on the chaotic world of fast-food management with Fast Food Simulator, a multiplayer simulation game from No Ceiling Games. Take orders, make burgers and increase earnings by dealing with customers. Play solo or co-op with up to four players and take on unexpected and bizarre events that can occur at any moment.Shift between realms in Legacy of Kain at up to 4K 120 fps with an Ultimate membership, slice through The Spirit of the Samurais mythical landscapes in stunning 1440p with RTX ON with a Performance membership or manage a fast-food empire with silky-smooth gameplay. With extended sessions and priority access, members will have plenty of time to master these diverse worlds.Play OnEvil never sleeps.Diablo Immortal the action-packed role-playing game from Blizzard Entertainment, set in the dark fantasy world of Sanctuary bridges the stories of Diablo II and Diablo III. Choose from a variety of classes, each offering unique playstyles and devastating abilities, to battle through diverse zones and randomly generated rifts, and uncover the mystery of the shattered Worldstone while facing off against hordes of demonic enemies.Since its launch, the game has offered frequent updates, including two new character classes, new zones, gear, competitive events and more demonic stories to experience. With its immersive storytelling, intricate character customization and endless replayability, Diablo Immortal provides members with a rich, hellish adventure to stream from the cloud across devices.Look for the following games available to stream in the cloud this week:Indiana Jones and the Great Circle (New release on Steam and Xbox, available on the Microsoft Store and PC Game Pass, Dec. 8)Fast Food Simulator (New release on Steam, Dec. 10)Legacy of Kain Soul Reaver 1&2 Remastered (New release on Steam, Dec. 10)The Spirit of the Samurai (New release on Steam, Dec. 12)Diablo Immortal (Battle.net)The Lord of the Rings: Return to Moria (Steam)What are you planning to play this weekend? Let us know on X or in the comments below.If you were the main character in a survival game, what would your main skill be? NVIDIA GeForce NOW (@NVIDIAGFN) December 11, 2024

Editors note: This post is part of the AI Decoded series, which demystifies AI by making the technology more accessible, and showcases new hardware, software, tools and accelerations for GeForce RTX PC and NVIDIA RTX workstation users.NVIDIA and GeForce RTX GPUs are built for the era of AI.RTX GPUs feature specialized AI Tensor Cores that can deliver more than 1,300 trillion operations per second (TOPS) of processing power for cutting-edge performance in gaming, creating, everyday productivity and more. Today there are more than 600 deployed AI-powered games and apps that are accelerated by RTX.RTX AI PCs can help anyone start their AI journey and supercharge their work.Every RTX AI PC comes with regularly updated NVIDIA Studio Drivers fine-tuned in collaboration with developers that enhance performance in top creative apps and are tested extensively to deliver maximum stability. Download the December Studio Driver today.The importance of large language models (LLM) continues to grow. Two benchmarks were introduced this week to spotlight LLM performance on various hardware: MLPerf Client v0.5 and Procyon AI Text Generation. These LLM-based benchmarks, which internal tests have shown accurately replicate real-world performance, are easy to run.This holiday season, content creators can participate in the #WinterArtChallenge, running through February. Share winter-themed art on Facebook, Instagram or X with #WinterArtChallenge for a chance to be featured on NVIDIA Studio social media channels.'Tis the season for the #WinterArtChallenge! We want to see your incredible winter art like this one from @Sweeper3D! Use the #WinterArtChallenge when posting for a chance to be featured on our channels! pic.twitter.com/6aC0KqJn13 NVIDIA Studio (@NVIDIAStudio) December 9, 2024Advanced AIWith NVIDIA and GeForce RTX GPUs, AI elevates everyday tasks and activities, as covered in our AI Decoded blog series. For example, AI can enable:Faster creativity: With Stable Diffusion, users can quickly create and refine images from text prompts to achieve their desired output. When using an RTX GPU, these results can be generated up to 2.2x faster than on an NPU. And thanks to software optimizations using the NVIDIA TensorRT SDK, the applications used to run these models, like ComfyUI, get an additional 60% boost.Greater gaming: NVIDIA DLSS technology boosts frame rates and improves image quality, using AI to automatically generate pixels in video games. With ongoing improvements, including to Ray Reconstruction, DLSS enables richer visual quality for more immersive gameplay.Enhanced entertainment: RTX Video Super Resolution uses AI to enhance video by removing compression artifacts and sharpening edges while upscaling video quality. RTX Video HDR converts any standard dynamic range video into vibrant high dynamic range, enabling more vivid, dynamic colors when streamed in Google Chrome, Microsoft Edge, Mozilla Firefox or VLC media player.Improved productivity:The NVIDIA ChatRTX tech demo app connects a large language model, like Metas Llama, to a users data for quickly querying notes, documents or images. Free for RTX GPU owners, the custom chatbot provides quick, contextually relevant answers. Since it runs locally on Windows RTX PCs and workstations, results are fast and private.This snapshot of AI capabilities barely scratches the surface of the technologys possibilities. With an NVIDIA or GeForce RTX GPU-powered system, users can also supercharge their STEM studies and research, and tap into the NVIDIA Studio suite of AI-powered tools.Decisions, DecisionsMore than 200 powerful RTX AI PCs are capable of running advanced AI.ASUS Vivobook Pro 16X comes with up to a GeForce RTX 4070 Laptop GPU.ASUS Vivobook Pro 16X comes with up to a GeForce RTX 4070 Laptop GPU, as well as a superbright 550-nit panel, ultrahigh contrast ratio and ultrawide 100% DCI-P3 color gamut. Its available on Amazon and ASUS.com.Dells Inspiron 16 Plus 7640 comes with up to a GeForce RTX 4060 Laptop GPU.Dells Inspiron 16 Plus 7640 comes with up to a GeForce RTX 4060 Laptop GPU and a 16:10 aspect ratio display, ideal for users working on multiple projects. It boasts military-grade testing for added reliability and an easy-to-use, built-in Trusted Platform Module to protect sensitive data. Its available on Amazon and Dell.com.GIGABYTEs AERO 16 OLED comes with up to a GeForce RTX 4070 Laptop GPU.GIGABYTEs AERO 16 OLED, equipped with up to a GeForce RTX 4070 Laptop GPU, is designed for professionals, designers and creators. The 16:10 thin-bezel 4K+ OLED screen is certified by multiple third parties to provide the best visual experience with X-Rite 2.0 factory-by-unit color calibration and Pantone Validated color calibration. Its available on Amazon and GIGABYTE.com.MSIs Creator M14 comes with up to a GeForce RTX 4070 Laptop GPU.MSIs Creator M14 comes with up to a GeForce RTX 4070 Laptop GPU, delivering a quantum leap in performance with DLSS 3 to enable lifelike virtual worlds with full ray tracing. Plus, its Max-Q suite of technologies optimizes system performance, power, battery life and acoustics for peak efficiency. Purchase one on Amazon or MSI.com.These are just a few of the many RTX AI PCs available, with some on sale, including the Acer Nitro V, ASUS TUF 16, HP Envy 16 and Lenovo Yoga Pro 9i.Follow NVIDIA Studio on Facebook, Instagram and X. Access tutorials on the Studio YouTube channel and get updates directly in your inbox by subscribing to the Studio newsletter.Generative AI is transforming gaming, videoconferencing and interactive experiences of all kinds. Make sense of whats new and whats next by subscribing to the AI Decoded newsletter.

Vay, a Berlin-based provider of automotive-grade remote driving (teledriving) technology, is offering an alternative approach to autonomous driving.Through the companys app, a user can hail a car, and a professionally trained teledriver will remotely drive the vehicle to the customers location. Once the car arrives, the user manually drives it.After completing their trip, the user can end the rental in the app and pull over to a safe location to exit the car, away from traffic flow. Theres no need to park the vehicle, as the teledriver will handle the parking or drive the car to the next customer.This system offers sustainable, door-to-door mobility, with the unique advantage of having a human driver remotely controlling the vehicle in real time.Vays technology is built on the NVIDIA DRIVE AGX centralized compute platform, running the NVIDIA DriveOS operating system for safe, AI-defined autonomous vehicles.These technologies enable Vays fleets to process large volumes of camera and other vehicle data over the air. DRIVE AGXs real-time, low-latency video streaming capabilities provide enhanced situational awareness for teledrivers, while its automotive-grade design ensures reliability in any driving condition.By combining Vays innovative remote driving capabilities with the advanced AI and computing power of NVIDIA DRIVE AGX, were setting a new standard for remotely driven vehicles, said Justin Spratt, chief business officer at Vay. This collaboration helps us bring safe, reliable and accessible driverless options to the market and provides an adaptable solution that can be deployed in real-world environments now not years from now.High-Quality Video StreamVays advanced technology stack includes NVIDIA DRIVE AGX software thats optimized for latency and processing power. By harnessing NVIDIA GPUs specifically designed for autonomous driving, the companys teledriving system can process and transmit high-definition video feeds in real time, delivering critical situational awareness to the teledriver, even in complex environments. In the event of an emergency, the vehicle can safely bring itself to a complete stop.Working with NVIDIA, Vay is setting a new standard in driverless technology, said Bogdan Djukic, cofounder and vice president of engineering, teledrive experience and autonomy at Vay. We are proud to not only accelerate the deployment of remotely driven and autonomous vehicles but also to expand the boundaries of whats possible in urban transportation, logistics and beyond transforming mobility for both businesses and communities.Reshaping Mobility With TeledrivingVays technology enables professionally trained teledrivers to remotely drive vehicles from specialized teledrive stations equipped with industry-standard controls, such as a steering wheel and pedals.The companys teledrivers are totally immersed in the drive road traffic sounds, such as those from emergency vehicles and other warning signals, are transmitted via microphones to the operators headphones. Camera sensors reproduce the cars surroundings and transmit them to the screens of the teledrive station with minimum latency. The vehicles can operate at speeds of up to 26 mph.Vays technology effectively addresses complex edge cases with human supervision, enhancing safety while significantly reducing costs and development challenges.Vay is a member of NVIDIA Inception, a program that nurtures AI startups with go-to-market support, expertise and technology. Last year, Vay became the first and only company in Europe to teledrive a vehicle on public streets without a safety driver.Since January, Vay has been operating its commercial services in Las Vegas. The startup recently secured a partnership with Bayanat, a provider of AI-powered geospatial solutions, and is working with Ush and Poppy, Belgium-based car-sharing companies, as well as Peugeot, a French automaker.In October, Vay announced a $35 million investment from the European Investment Bank, which will help it roll out its technology across Europe and expand its development team.Learn more about the NVIDIA DRIVE platform.

Quantum computing has the potential to transform industries ranging from drug discovery to logistics, but a huge barrier standing between todays quantum devices and useful applications is noise. These disturbances, introduced by environmental interactions and imperfect hardware, mean that todays qubits can only perform hundreds of operations before quantum computations irretrievably deteriorate.Though seemingly inevitable, noise in quantum hardware can be tackled by so-called logical qubits collections of tens, hundreds or even thousands of actual physical qubits that allow the correction of noise-induced errors. Logical qubits are the holy grail of quantum computing, and quantum hardware builder Infleqtion today published groundbreaking work that used the NVIDIA CUDA-Q platform to both design and demonstrate an experiment with two of them.These logical qubits were used to perform a small-scale demonstration of the so-called single-impurity Anderson model, a high-accuracy approach necessary for many important materials science applications.This constitutes the first time that a demonstration of a materials science quantum algorithm has been performed on logical qubits. The creation of just a single logical qubit is extremely challenging. Infleqtion was able to achieve such a feat thanks to accurate modeling of its quantum computer using CUDA-Qs unique GPU-accelerated simulation capabilities.Having developed and tested its entire experiment within CUDA-Qs simulators, with only trivial changes, Infleqtion could then use CUDA-Q to orchestrate the experiment using the actual physical qubits within its Sqale neutral atom quantum processor.This work sets the stage for quantum computings move toward large-scale, error-corrected systems.Many scaling challenges still stand between todays quantum devices and large systems of logical qubits, which will only be solved by integrating quantum hardware with AI supercomputers to form accelerated quantum supercomputers.NVIDIA continues to work with partners like Infleqtion to enable this breakthrough research needed to make accelerated quantum supercomputing a reality.Learn more about NVIDIAs quantum computing platforms.

Artificial intelligence, once the realm of science fiction, claimed its place at the pinnacle of scientific achievement Monday in Sweden.In a historic ceremony at Stockholms iconic Konserthuset, John Hopfield and Geoffrey Hinton received the Nobel Prize in Physics for their pioneering work on neural networks systems that mimic the brains architecture and form the bedrock of modern AI.Meanwhile, Demis Hassabis and John Jumper accepted the Nobel Prize in Chemistry for Google DeepMinds AlphaFold, a system that solved biologys impossible problem: predicting the structure of proteins, a feat with profound implications for medicine and biotechnology.These achievements go beyond academic prestige. They mark the start of an era where GPU-powered AI systems tackle problems once deemed unsolvable, revolutionizing multitrillion-dollar industries from healthcare to finance.Hopfields Legacy and the Foundations of Neural NetworksIn the 1980s, Hopfield, a physicist with a knack for asking big questions, brought a new perspective to neural networks.He introduced energy landscapes borrowed from physics to explain how neural networks solve problems by finding stable, low-energy states. His ideas, abstract yet elegant, laid the foundation for AI by showing how complex systems optimize themselves.Fast forward to the early 2000s, when Geoffrey Hinton a British cognitive psychologist with a penchant for radical ideas picked up the baton. Hinton believed neural networks could revolutionize AI, but training these systems required enormous computational power.In 1983, Hinton and Sejnowski built on Hopfields work and invented the Boltzmann Machine which used stochastic binary neurons to jump out of local minima. They discovered an elegant and very simple learning procedure based on statistical mechanics which was an alternative to backpropagation.In 2006 a simplified version of this learning procedure proved to be very effective at initializing deep neural networks before training them with backpropagation. However, training these systems still required enormous computational power.AlphaFold: Biologys AI RevolutionA decade after AlexNet, AI moved to biology. Hassabis and Jumper led the development of AlphaFold to solve a problem that had stumped scientists for years: predicting the shape of proteins.Proteins are lifes building blocks. Their shapes determine what they can do. Understanding these shapes is the key to fighting diseases and developing new medicines. But finding them was slow, costly and unreliable.AlphaFold changed that. It used Hopfields ideas and Hintons networks to predict protein shapes with stunning accuracy. Powered by GPUs, it mapped almost every known protein. Now, scientists use AlphaFold to fight drug resistance, make better antibiotics and treat diseases once thought to be incurable.What was once biologys Gordian knot has been untangled by AI.The GPU Factor: Enabling AIs PotentialGPUs, the indispensable engines of modern AI, are at the heart of these achievements. Originally designed to make video games look good, GPUs were perfect for the massive parallel processing demands of neural networks.NVIDIA GPUs, in particular, became the engine driving breakthroughs like AlexNet and AlphaFold. Their ability to process vast datasets with extraordinary speed allowed AI to tackle problems on a scale and complexity never before possible.Redefining Science and IndustryThe Nobel-winning breakthroughs of 2024 arent just rewriting textbooks theyre optimizing global supply chains, accelerating drug development and helping farmers adapt to changing climates.Hopfields energy-based optimization principles now inform AI-powered logistics systems. Hintons architectures underpin self-driving cars and language models like ChatGPT. AlphaFolds success is inspiring AI-driven approaches to climate modeling, sustainable agriculture and even materials science.The recognition of AI in physics and chemistry signals a shift in how we think about science. These tools are no longer confined to the digital realm. Theyre reshaping the physical and biological worlds.

Content streaming and engagement are entering a new dimension with QUEEN, an AI model by NVIDIA Research and the University of Maryland that makes it possible to stream free-viewpoint video, which lets viewers experience a 3D scene from any angle.QUEEN could be used to build immersive streaming applications that teach skills like cooking, put sports fans on the field to watch their favorite teams play from any angle, or bring an extra level of depth to video conferencing in the workplace. It could also be used in industrial environments to help teleoperate robots in a warehouse or a manufacturing plant.The model will be presented at NeurIPS, the annual conference for AI research that begins Tuesday, Dec. 10, in Vancouver.To stream free-viewpoint videos in near real time, we must simultaneously reconstruct and compress the 3D scene, said Shalini De Mello, director of research and a distinguished research scientist at NVIDIA. QUEEN balances factors including compression rate, visual quality, encoding time and rendering time to create an optimized pipeline that sets a new standard for visual quality and streamability.Reduce, Reuse and Recycle for Efficient StreamingFree-viewpoint videos are typically created using video footage captured from different camera angles, like a multicamera film studio setup, a set of security cameras in a warehouse or a system of videoconferencing cameras in an office.Prior AI methods for generating free-viewpoint videos either took too much memory for livestreaming or sacrificed visual quality for smaller file sizes. QUEEN balances both to deliver high-quality visuals even in dynamic scenes featuring sparks, flames or furry animals that can be easily transmitted from a host server to a clients device. It also renders visuals faster than previous methods, supporting streaming use cases.In most real-world environments, many elements of a scene stay static. In a video, that means a large share of pixels dont change from one frame to another. To save computation time, QUEEN tracks and reuses renders of these static regions focusing instead on reconstructing the content that changes over time.Using an NVIDIA Tensor Core GPU, the researchers evaluated QUEENs performance on several benchmarks and found the model outperformed state-of-the-art methods for online free-viewpoint video on a range of metrics. Given 2D videos of the same scene captured from different angles, it typically takes under five seconds of training time to render free-viewpoint videos at around 350 frames per second.This combination of speed and visual quality can support media broadcasts of concerts and sports games by offering immersive virtual reality experiences or instant replays of key moments in a competition.In warehouse settings, robot operators could use QUEEN to better gauge depth when maneuvering physical objects. And in a videoconferencing application such as the 3D videoconferencing demo shown at SIGGRAPH and NVIDIA GTC it could help presenters demonstrate tasks like cooking or origami while letting viewers pick the visual angle that best supports their learning.The code for QUEEN will soon be released as open source and shared on the project page.QUEEN is one of over 50 NVIDIA-authored NeurIPS posters and papers that feature groundbreaking AI research with potential applications in fields including simulation, robotics and healthcare.Generative Adversarial Nets, the paper that first introduced GAN models, won the NeurIPS 2024 Test of Time Award. Cited more than 85,000 times, the paper was coauthored by Bing Xu, distinguished engineer at NVIDIA. Hear more from its lead author, Ian Goodfellow, research scientist at DeepMind, on the AI Podcast:The AI Podcast Ep. 25: Googles Ian Goodfellow on How an Argument in a Bar Led to Generative Adversarial NetworksLearn more about NVIDIA Research at NeurIPS.See the latest work from NVIDIA Research, which has hundreds of scientists and engineers worldwide, with teams focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics.Academic researchers working on large language models, simulation and modeling, edge AI and more can apply to the NVIDIA Academic Grant Program.See notice regarding software product information.

Southeast Asia is embracing sovereign AI.The prime ministers of Thailand and Vietnam this week met with NVIDIA founder and CEO Jensen Huang to discuss initiatives that will accelerate AI innovation in their countries.During his visit to the region, Huang also joined Bangkok-based cloud infrastructure company SIAM.AI Cloud onstage for a fireside chat on sovereign AI. In Vietnam, he announced NVIDIAs collaboration with the countrys government on an AI research and development center and NVIDIAs acquisition of VinBrain, a health technology startup funded by Vingroup, one of Vietnams largest public companies.These events capped a year of global investments in sovereign AI, the ability for countries to develop and harness AI using domestic computing infrastructure, data and workforces. AI will contribute nearly $20 trillion to the global economy through the end of the decade, according to IDC.Canada, Denmark and Indonesia are among the countries that have announced initiatives to develop sovereign AI infrastructure powered by NVIDIA technology. And at the recent NVIDIA AI Summits in India and Japan, leading enterprises, infrastructure providers and startups in both countries announced sovereign AI projects in sectors including finance, healthcare and manufacturing.Supporting Sovereign Cloud Infrastructure in ThailandHuangs Southeast Asia visit kicked off with a meeting with Thailand Prime Minister Paetongtarn Shinawatra, where he discussed the opportunities for sovereign AI development in Thailand and shared memories of his childhood years spent in Bangkok.The pair discussed how further investing in AI education and training can help Thailand drive AI innovations in fields such as weather prediction, climate simulation and healthcare. NVIDIA is working with dozens of local universities and startups to support AI advancement in the country.Huang and Shinawatra met in the Purple Room of the Thai-Khu-Fah building, which houses the offices of the prime minister and cabinet.Huang later took the stage at an AI Vision for Thailand event hosted by SIAM.AI Cloud, a cloud platform company that offers customers access to virtual servers featuring NVIDIA Tensor Core GPUs.The most important part of artificial intelligence is the data. And the data of Thailand belongs to the Thai people, Huang said in a fireside chat with Ratanaphon Wongnapachant, CEO of SIAM.AI Cloud. Highlighting the importance of sovereign AI development, Huang said, The digital data of Thailand encodes the knowledge, the history, the culture, the common sense of your people. It should be harvested by your people.Following the conversation, Wongnapachant gifted Huang a custom leather jacket lined with Thai silk. The pair also signed an NVIDIA DGX H200 system in recognition of SIAM.AI Clouds plans to expand its offerings to NVIDIA H200 Tensor Core GPUs and NVIDIA GB200 Grace Blackwell Superchips.Advancing AI From Research to Industry in VietnamIn Hanoi the next day, Huang met with Vietnams Prime Minister Pham Minh Chinh, and NVIDIA signed an agreement to open the companys first research and development center in the country. The center will focus on software development and collaborate with Vietnams enterprises, startups, government agencies and universities to accelerate AI adoption in the country.The announcement builds on NVIDIAs existing work with 65 universities in Vietnam and more than 100 of the countrys AI startups through NVIDIA Inception, a global program designed to help startups evolve faster. NVIDIA has acquired Inception member VinBrain, a Hanoi-based company that applies AI diagnostics to multimodal health data.While in Vietnam, Huang also received the 2024 VinFuture Prize alongside AI pioneers Yoshua Bengio, Geoffrey Hinton, Yann Le Cun and Fei-Fei Li for their transformational contributions to the advancement of deep learning.Broadcast live nationally in the country, the awards ceremony was hosted by the VinFuture Foundation, a nonprofit that recognizes innovations in science and technology with significant societal impact.Our award today is recognition by the VinFuture committee of the transformative power of AI to revolutionize every field of science and every industry, Huang said in his acceptance speech.Bengio, Huang and LeCun accepted the 2024 VinFuture Prize onstage in Hanoi.Learn more about sovereign AI.Editors note: The data on the economic impact of AI is from IDCs press release titled IDC: Artificial Intelligence Will Contribute $19.9 Trillion to the Global Economy through 2030 and Drive 3.5% of Global GDP in 2030, published in September 2024.

GeForce NOW is wrapping a sleigh-full of gaming gifts this month, stuffing members cloud gaming stockings with new titles and fresh offers to keep holiday gaming spirits merry and bright.Adventure calls and whip-cracking action awaits in the highly anticipated Indiana Jones and the Great Circle, streaming in the cloud today during the Advanced Access period for those who have preordered the Premium Edition from Steam or the Microsoft Store.The title can only be played with RTX ON GeForce NOW is offering gamers without high-performance hardware the ability to play it with 25% off Ultimate and Performance Day Passes. Its like finding that extra-special gift hidden behind the tree.This GFN Thursday also brings a new limited-time offer: 50% off the first month of new Ultimate or Performance memberships a gift that can keep on giving.Whether looking to try out GeForce NOW or buckle in for long-term cloud gaming, new members can choose between the Day Pass sale or the new membership offer. Theres a perfect gaming gift for everyone this holiday season.GFN Thursday also brings 13 new titles in December, with four available this week to get the festivities going.Plus, the latest update to GeForce NOW version 2.0.69 includes expanded support for 10-bit color precision. This feature enhances image quality when streaming on Windows, macOS and NVIDIA SHIELD TVs and now to Edge and Chrome browsers on Windows devices, as well as to the Chrome browser on Chromebooks, Samsung TVs and LG TVs.An Epic, Globetrotting AdventureUncover one of historys greatest mysteries, streaming Indiana Jones and the Great Circle from the cloud. Members can access it today through Steams Advance Access period and will also be able to enjoy it via PC Game Pass on GeForce NOW next week.The year is 1937, sinister forces are scouring the globe for the secret to an ancient power connected to the Great Circle, and only Indiana Jones can stop them. Experience a thrilling story full of exploration, immersive action and intriguing puzzles. Travel the world, from the pyramids of Egypt to the sunken temples of Sukhothai and beyond. Combine stealth infiltration, melee combat and gunplay to overcome enemies. Use guile and wits to unearth fascinating secrets, solve ancient riddles and survive deadly traps.Members can indulge their inner explorer by streaming Indiana Jones and the Great Circle on GeForce NOW at release. Enhanced with NVIDIAs ray-tracing technology, every game scene is bathed in rich, natural light that bounces realistically off surfaces for enhanced immersion.Ultimate and Performance members can max out their settings for a globe-trotting journey at the highest resolution and lowest latency, even on low-powered devices, thanks to enhancements like NVIDIA DLSS 3 Frame Generation and NVIDIA Reflex. Ultimate members can experience additional perks, like 4K resolution and longer gaming sessions.This game requires RTX ON, so free members can upgrade today to join in on the action. Take advantage of a limited-time Day Pass sale, with 25% off through Thursday, Dec. 12. Experience all the premium features of GeForce NOWs Ultimate and Performance tiers with a 24-hour trial before upgrading to a one- or six-month membership.Making the Cloud Merry and BrightDeals for days.For gamers looking to take their cloud gaming journey even further, unlock the power of GeForce RTX-powered cloud gaming with a monthly GeForce NOW membership. Its the perfect time to do so, with new members gettings 50% off their first month, now through Monday, Dec. 30.Experience gaming at its finest with an Ultimate membership by streaming at up to 4K resolution and 120 frames per second, or 1080p at 240 fps. The Performance membership offers an enhanced streaming experience at up to 1440p resolution with ultrawide resolutions for even more immersive gameplay. Both premium tiers provide extended session lengths, priority access to servers and the ability to play the latest and greatest titles with RTX ON.Whether looking to conquer new worlds, compete at the highest level or unwind with a long-time favorite game, now is an ideal time to join the cloud gaming community. Sign up to transform any device into a powerful gaming rig just in time for the holiday gaming marathons.Dashing DecemberThe cloud is the path of least resistance.Path of Exile 2 is the highly anticipated sequel to the popular free-to-play action role-playing game from Grinding Gear Games. The game will be available for members to stream Friday, Dec. 6, in early access with a wealth of content to experience.Explore the three acts of the campaign, six playable character classes and a robust endgame system in the dark world of Wraeclast, a continent populated by unique cultures, ancient secrets and monstrous dangers. A sinister threat, long thought destroyed, has begun creeping back on the edge of civilisation, driving people mad and sickening the land with Corruption. Play Path of Exile 2 solo or grab the squad for online co-op with up to six players.Look for these games available to stream in the cloud this week:Indiana Jones and the Great Circle (Advanced Access on Steam and Xbox, available on the Microsoft Store)Path of Exile 2 (New release on Steam and Grinding Gears, Dec. 6)JR EAST Train Simulator (Steam)JR EAST Train Simulator Demo (Steam)Heres what members can expect in December:Fast Food Simulator (New release on Steam, Dec. 10)Legacy of Kain Soul Reaver 1&2 Remastered (New release on Steam, Dec. 10)The Spirit of the Samurai (New release on Steam, Dec. 12)The Lord of the Rings: Return to Moria (Steam)NieR:Automata (Steam)NieR Replicant ver.1.22474487139 (Steam)Replikant Chat (Steam)Supermarket Together (Steam)Ys X: Nordics (Steam)New to NovemberIn addition to the 17 games announced last month, 13 more joined the GeForce NOW library:Ara: History Untold (Steam and Xbox, available on PC Game Pass)Call of Duty: Black Ops Cold War (Steam and Battle.net)Call of Duty: Vanguard (Steam and Battle.net)Crash Bandicoot N. Sane Trilogy (Steam and Xbox, available on PC Game Pass)The Elder Scrolls IV: Oblivion Game of the Year Edition (Epic Games Store, Steam and Xbox, available on PC Game Pass)Fallout 3: Game of the Year Edition (Epic Games Store, Steam and Xbox, available on PC Game Pass)Magicraft (Steam)MEGA MAN X DiVE Offline Demo (Steam)New Arc Line (New release on Steam, Nov. 26)Resident Evil 7 Teaser: Beginning Hour Demo (Steam)Spyro Reignited Trilogy (Steam and Xbox, available on PC Game Pass)StarCraft II (Xbox, available on PC Game Pass, Nov. 5. Members need to enable access.)StarCraft Remastered (Xbox, available on PC Game Pass, Nov. 5. Members need to enable access.)Metal Slug Tactics, Dungeons & Degenerate Gamblers and Headquarters: World War II didnt make it last month. Stay tuned to future GFN Thursday for updates.What are you planning to play this weekend? Let us know on X or in the comments below.There's no place like the cloud for the holidays. Unlock the power of GeForce RTX-powered gaming with a GeForce NOW monthly membership % the first month for new members! RUN https://t.co/aCfuQ7KtkB pic.twitter.com/8cjY5ZTBPW NVIDIA GeForce NOW (@NVIDIAGFN) December 4, 2024

From boardroom to break room, generative AI took this year by storm, stirring discussion across industries about how to best harness the technology to enhance innovation and creativity, improve customer service, transform product development and even boost communication.The adoption of generative AI and large language models is rippling through nearly every industry, as incumbents and new entrants reimagine products and services to generate an estimated $1.3 trillion in revenue by 2032, according to a report by Bloomberg Intelligence.Yet, some companies and startups are still slow to adopt AI, sticking to experimentation and siloed projects even as the technology advances at a dizzying pace. Thats partly because AI benefits vary by company, use case and level of investment.Cautious approaches are giving way to optimism. Two-thirds of the respondents to Forrester Researchs 2024 State of AI Survey believe their organizations would require less than 50% return on investments to consider their AI initiatives successful.The next big thing on the horizon is agentic AI, a form of autonomous or reasoning AI that requires using diverse language models, sophisticated retrieval-augmented generation stacks and advanced data architectures.NVIDIA experts in industry verticals already shared their expectations for the year ahead. Now, hear from company experts driving innovation in AI across enterprises, research and the startup ecosystem:IAN BUCKVice President of Hyperscale and HPCInference drives the AI charge: As AI models grow in size and complexity, the demand for efficient inference solutions will increase.The rise of generative AI has transformed inference from simple recognition of the query and response to complex information generation including summarizing from multiple sources and large language models such as OpenAI o1 and Llama 450B which dramatically increases computational demands. Through new hardware innovations, coupled with continuous software improvements, performance will increase and total cost of ownership is expected to shrink by 5x or more.Accelerate everything: With GPUs becoming more widely adopted, industries will look to accelerate everything, from planning to production. New architectures will add to that virtuous cycle, delivering cost efficiencies and an order of magnitude higher compute performance with each generation.As nations and businesses race to build AI factories to accelerate even more workloads, expect many to look for platform solutions and reference data center architectures or blueprints that can get a data center up and running in weeks versus months. This will help them solve some of the worlds toughest challenges, including quantum computing and drug discovery.Quantum computing all trials, no errors: Quantum computing will make significant strides as researchers focus on supercomputing and simulation to solve the greatest challenges to the nascent field: errors.Qubits, the basic unit of information in quantum computing, are susceptible to noise, becoming unstable after performing only thousands of operations. This prevents todays quantum hardware from solving useful problems. In 2025, expect to see the quantum computing community move toward challenging, but crucial, quantum error correction techniques. Error correction requires quick, low-latency calculations. Also expect to see quantum hardware thats physically colocated within supercomputers, supported by specialized infrastructure.AI will also play a crucial role in managing these complex quantum systems, optimizing error correction and enhancing overall quantum hardware performance. This convergence of quantum computing, supercomputing and AI into accelerated quantum supercomputers will drive progress in realizing quantum applications for solving complex problems across various fields, including drug discovery, materials development and logistics.BRYAN CATANZAROVice President of Applied Deep Learning ResearchPutting a face to AI: AI will become more familiar to use, emotionally responsive and marked by greater creativity and diversity. The first generative AI models that drew pictures struggled with simple tasks like drawing teeth. Rapid advances in AI are making image and video outputs much more photorealistic, while AI-generated voices are losing that robotic feel.These advancements will be driven by the refinement of algorithms and datasets and enterprises acknowledgment that AI needs a face and a voice to matter to 8 billion people. This will also cause a shift from turn-based AI interactions to more fluid and natural conversations. Interactions with AI will no longer feel like a series of exchanges but instead offer a more engaging and humanlike conversational experience.Rethinking industry infrastructure and urban planning: Nations and industries will begin examining how AI automates various aspects of the economy to maintain the current standard of living, even as the global population shrinks.These efforts could help with sustainability and climate change. For instance, the agriculture industry will begin investing in autonomous robots that can clean fields and remove pests and weeds mechanically. This will reduce the need for pesticides and herbicides, keeping the planet healthier and freeing up human capital for other meaningful contributions. Expect to see new thinking in urban planning offices to account for autonomous vehicles and improve traffic management.Longer term, AI can help find solutions for reducing carbon emissions and storing carbon, an urgent global challenge.KARI BRISKIVice President of Generative AI SoftwareA symphony of agents AI orchestrators: Enterprises are set to have a slew of AI agents, which are semiautonomous, trained models that work across internal networks to help with customer service, human resources, data security and more. To maximize these efficiencies, expect to see a rise in AI orchestrators that work across numerous agents to seamlessly route human inquiries and interpret collective results to recommend and take actions for users.These orchestrators will have access to deeper content understanding, multilingual capabilities and fluency with multiple data types, ranging from PDFs to video streams. Powered by self-learning data flywheels, AI orchestrators will continuously refine business-specific insights. For instance, in manufacturing, an AI orchestrator could optimize supply chains by analyzing real-time data and making recommendations on production schedules and supplier negotiations.This evolution in enterprise AI will significantly boost productivity and innovation across industries while becoming more accessible. Knowledge workers will be more productive because they can tap into a personalized team of AI-powered experts. Developers will be able to build these advanced agents using customizable AI blueprints.Multistep reasoning amplifies AI insights: AI for years has been good at giving answers to specific questions without having to delve into the context of a given query. With advances in accelerated computing and new model architectures, AI models will tackle increasingly complex problems and respond with greater accuracy and deeper analysis.Using a capability called multistep reasoning, AI systems increase the amount of thinking time by breaking down large, complex questions into smaller tasks sometimes even running multiple simulations to problem-solve from various angles. These models dynamically evaluate each step, ensuring contextually relevant and transparent responses. Multistep reasoning also involves integrating knowledge from various sources to enable AI to make logical connections and synthesize information across different domains.This will likely impact fields ranging from finance and healthcare to scientific research and entertainment. For example, a healthcare model with multistep reasoning could make a number of recommendations for a doctor to consider, depending on the patients diagnosis, medications and response to other treatments.Start your AI query engine: With enterprises and research organizations sitting on petabytes of data, the challenge is gaining quick access to the data to deliver actionable insights.AI query engines will change how businesses mine that data, and company-specific search engines will be able to sift through structured and unstructured data, including text, images and videos, using natural language processing and machine learning to interpret a users intent and provide more relevant and comprehensive results.This will lead to more intelligent decision-making processes, improved customer experiences and enhanced productivity across industries. The continuous learning capabilities of AI query engines will create self-improving data flywheels that help applications become increasingly effective.CHARLIE BOYLEVice President of DGX PlatformsAgentic AI makes high-performance inference essential for enterprises: The dawn of agentic AI will drive demand for near-instant responses from complex systems of multiple models. This will make high-performance inference just as important as high-performance training infrastructure. IT leaders will need scalable, purpose-built and optimized accelerated computing infrastructure that can keep pace with the demands of agentic AI to deliver the performance required for real-time decision-making.Enterprises expand AI factories to process data into intelligence: Enterprise AI factories transform raw data into business intelligence. Next year, enterprises will expand these factories to leverage massive amounts of historical and synthetic data, then generate forecasts and simulations for everything from consumer behavior and supply chain optimization to financial market movements and digital twins of factories and warehouses. AI factories will become a key competitive advantage that helps early adopters anticipate and shape future scenarios, rather than just react to them.Chill factor liquid-cooled AI data centers: As AI workloads continue to drive growth, pioneering organizations will transition to liquid cooling to maximize performance and energy efficiency. Hyperscale cloud providers and large enterprises will lead the way, using liquid cooling in new AI data centers that house hundreds of thousands of AI accelerators, networking and software.Enterprises will increasingly choose to deploy AI infrastructure in colocation facilities rather than build their own in part to ease the financial burden of designing, deploying and operating intelligence manufacturing at scale. Or, they will rent capacity as needed. These deployments will help enterprises harness the latest infrastructure without needing to install and operate it themselves. This shift will accelerate broader industry adoption of liquid cooling as a mainstream solution for AI data centers.GILAD SHAINERSenior Vice President of NetworkingGoodbye network, hello computing fabric: The term networking in the data center will seem dated as data center architecture transforms into an integrated compute fabric that enables thousands of accelerators to efficiently communicate with one another via scale-up and scale-out communications, spanning miles of cabling and multiple data center facilities.This integrated compute fabric will include NVIDIA NVLink, which enables scale-up communications, as well as scale-out capabilities enabled by intelligent switches, SuperNICs and DPUs. This will help securely move data to and from accelerators and perform calculations on the fly that drastically minimize data movement. Scale-out communication across networks will be crucial to large-scale AI data center deployments and key to getting them up and running in weeks versus months or years.As agentic AI workloads grow requiring communication across multiple interconnected AI models working together rather than monolithic and localized AI models compute fabrics will be essential to delivering real-time generative AI.Distributed AI: All data centers will become accelerated as new approaches to Ethernet design emerge that enable hundreds of thousands of GPUs to support a single workload. This will help democratize AI factory rollouts for multi-tenant generative AI clouds and enterprise AI data centers.This breakthrough technology will also enable AI to expand quickly into enterprise platforms and simplify the buildup and management of AI clouds.Companies will build data center resources that are more geographically dispersed located hundreds or even thousands of miles apart because of power limitations and the need to build closer to renewable energy sources. Scale-out communications will ensure reliable data movement over these long distances.LINXI (JIM) FANSenior Research Scientist, AI AgentsRobotics will evolve more into humanoids: Robots will begin to understand arbitrary language commands. Right now, industry robots must be programmed by hand, and they dont respond intelligently to unpredictable inputs or languages other than those programmed. Multimodal robot foundation models that incorporate vision, language and arbitrary actions will evolve this AI brain, as will agentic AI that allows for greater AI reasoning.To be sure, dont expect to immediately see intelligent robots in homes, restaurants, service areas and factories. But these use cases may be closer than you think, as governments look for solutions to aging societies and shrinking labor pools. Physical automation is going to happen gradually, in 10 years being as ubiquitous as the iPhone.AI agents are all about inferencing: In September, OpenAI announced a new large language model trained with reinforcement learning to perform complex reasoning. OpenAI o1, dubbed Strawberry, thinks before it answers: It can produce a long internal chain of thought, correcting mistakes and breaking down tricky steps into simple ones, before responding to the user.2025 will be the year a lot of computation begins to shift to inference at the edge. Applications will need hundreds of thousands of tokens for a single query, as small language models make one query after another in microseconds before churning out an answer.Small models will be more energy efficient and will become increasingly important for robotics, creating humanoids and robots that can assist humans in everyday jobs and promoting mobile intelligence applications..BOB PETTEVice President of Enterprise PlatformsSeeking sustainable scalability: As enterprises prepare to embrace a new generation of semiautonomous AI agents to enhance various business processes, theyll focus on creating robust infrastructure, governance and human-like capabilities for effective large-scale deployment. At the same time, AI applications will increasingly use local processing power to enable more sophisticated AI features to run directly on workstations, including thin, lightweight laptops and compact form factors, and improve performance while reducing latency for AI-driven tasks.Validated reference architectures, which provide guidance on appropriate hardware and software platforms, will become crucial to optimize performance and accelerate AI deployments. These architectures will serve as essential tools for organizations navigating the complex terrain of AI implementation by helping ensure that their investments align with current needs and future technological advancements.Revolutionizing construction, engineering and design with AI: Expect to see a rise in generative AI models tailored to the construction, engineering and design industries that will boost efficiency and accelerate innovation.In construction, agentic AI will extract meaning from massive volumes of construction data collected from onsite sensors and cameras, offering insights that lead to more efficient project timelines and budget management.AI will evaluate reality capture data (lidar, photogrammetry and radiance fields) 24/7 and derive mission-critical insights on quality, safety and compliance resulting in reduced errors and worksite injuries.For engineers, predictive physics based on physics-informed neural networks will accelerate flood prediction, structural engineering and computational fluid dynamics for airflow solutions tailored to individual rooms or floors of a building allowing for faster design iteration.In design, retrieval-augmented generation will enable compliance early in the design phase by ensuring that information modeling for designing and constructing buildings complies with local building codes. Diffusion AI models will accelerate conceptual design and site planning by enabling architects and designers to combine keyword prompts and rough sketches to generate richly detailed conceptual images for client presentations. That will free up time to focus on research and design.SANJA FIDLERVice President of AI ResearchPredicting unpredictability: Expect to see more models that can learn in the everyday world, helping digital humans, robots and even autonomous cars understand chaotic and sometimes unpredictable situations, using very complex skills with little human intervention.From the research lab to Wall Street, were entering a hype cycle similar to the optimism about autonomous driving 5-7 years ago. It took many years for companies like Waymo and Cruise to deliver a system that works and its still not scalable because the troves of data these companies and others, including Tesla, have collected may be applicable in one region but not another.With models introduced this year, we can now move more quickly and with much less capital expense to use internet-scale data to understand natural language and emulate movements by observing human and other actions. Edge applications like robots, cars and warehouse machinery will quickly learn coordination, dexterity and other skills in order to navigate, adapt and interact with the real world.Will a robot be able to make coffee and eggs in your kitchen, and then clean up after? Not yet. But it may come sooner than you think.Getting real: Fidelity and realism is coming to generative AI across the graphics and simulation pipeline, leading to hyperrealistic games, AI-generated movies and digital humans.Unlike with traditional graphics, the vast majority of images will come from generated pixels instead of renderings, resulting in more natural motions and appearances. Tools that develop and iterate on contextual behaviors will result in more sophisticated games for a fraction of the cost of todays AAA titles.Industries adopt generative AI: Nearly every industry is poised to use AI to enhance and improve the way people live and play.Agriculture will use AI to optimize the food chain, improving the delivery of food. For example, AI can be used to predict the greenhouse gas emissions from different crops on individual farms. These analyses can help inform design strategies that help reduce greenhouse gas in supply chains. Meanwhile, AI agents in education will personalize learning experiences, speaking in a persons native language and asking or answering questions based on level of education in a particular subject.As next-generation accelerators enter the marketplace, youll also see a lot more efficiency in delivering these generative AI applications. By improving the training and efficiency of the models in testing, businesses and startups will see better and faster returns on investment across those applications.ANDREW FENGVice President of GPU SoftwareAccelerated data analytics offers insights with no code change: In 2025, accelerated data analytics will become mainstream for organizations grappling with ever-increasing volumes of data.Businesses generate hundreds of petabytes of data annually, and every company is seeking ways to put it to work. To do so, many will adopt accelerated computing for data analytics.The future lies in accelerated data analytics solutions that support no code change and no configuration change, enabling organizations to combine their existing data analytics applications with accelerated computing with minimum effort. Generative AI-empowered analytics technology will further widen the adoption of accelerated data analytics by empowering users even those who dont have traditional programming knowledge to create new data analytics applications.The seamless integration of accelerated computing, facilitated by a simplified developer experience, will help eliminate adoption barriers and allow organizations to harness their unique data for new AI applications and richer business intelligence.NADER KHALILDirector of Developer TechnologyThe startup workforce: If you havent heard much about prompt engineers or AI personality designers, you will in 2025. As businesses embrace AI to increase productivity, expect to see new categories of essential workers for both startups and enterprises that blend new and existing skills.A prompt engineer designs and refines precise text strings that optimize AI training and produce desired outcomes based on the creation, testing and iteration of prompt designs for chatbots and agentic AI. The demand for prompt engineers will extend beyond tech companies to sectors like legal, customer support and publishing. As AI agents proliferate, businesses and startups will increasingly lean in to AI personality designers to enhance agents with unique personalities.Just as the rise of computers spawned job titles like computer scientists, data scientists and machine learning engineers, AI will create different types of work, expanding opportunities for people with strong analytical skills and natural language processing abilities.Understanding employee efficiency: Startups incorporating AI into their practices increasingly will add revenue per employee (RPE) to their lexicon when talking to investors and business partners.Instead of a growth at all costs mentality, AI supplementation of the workforce will allow startup owners to home in on how hiring each new employee helps everyone else in the business generate more revenue. In the world of startups, RPE fits into discussions about the return on investment in AI and the challenges of filling roles in competition against big enterprises and tech companies.

Generative AI is rapidly transforming industries, driving demand for secure, high-performance inference solutions to scale increasingly complex models efficiently and cost-effectively.Expanding its collaboration with NVIDIA, Amazon Web Services (AWS) revealed today at its annual AWS re:Invent conference that it has extended NVIDIA NIM microservices across key AWS AI services to support faster AI inference and lower latency for generative AI applications.NVIDIA NIM microservices are now available directly from the AWS Marketplace, as well as Amazon Bedrock Marketplace and Amazon SageMaker JumpStart, making it even easier for developers to deploy NVIDIA-optimized inference for commonly used models at scale.NVIDIA NIM, part of the NVIDIA AI Enterprise software platform available in the AWS Marketplace, provides developers with a set of easy-to-use microservices designed for secure, reliable deployment of high-performance, enterprise-grade AI model inference across clouds, data centers and workstations.These prebuilt containers are built on robust inference engines, such as NVIDIA Triton Inference Server, NVIDIA TensorRT, NVIDIA TensorRT-LLM and PyTorch, and support a broad spectrum of AI models from open-source community ones to NVIDIA AI Foundation models and custom ones.NIM microservices can be deployed across various AWS services, including Amazon Elastic Compute Cloud (EC2), Amazon Elastic Kubernetes Service (EKS) and Amazon SageMaker.Developers can preview over 100 NIM microservices built from commonly used models and model families, including Metas Llama 3, Mistral AIs Mistral and Mixtral, NVIDIAs Nemotron, Stability AIs SDXL and many more on the NVIDIA API catalog. The most commonly used ones are available for self-hosting to deploy on AWS services and are optimized to run on NVIDIA accelerated computing instances on AWS.NIM microservices now available directly from AWS include:NVIDIA Nemotron-4, available in Amazon Bedrock Marketplace, Amazon SageMaker Jumpstart and AWS Marketplace. This is a cutting-edge LLM designed to generate diverse synthetic data that closely mimics real-world data, enhancing the performance and robustness of custom LLMs across various domains.Llama 3.1 8B-Instruct, available on AWS Marketplace. This 8-billion-parameter multilingual large language model is pretrained and instruction-tuned for language understanding, reasoning and text-generation use cases.Llama 3.1 70B-Instruct, available on AWS Marketplace. This 70-billion-parameter pretrained, instruction-tuned model is optimized for multilingual dialogue.Mixtral 8x7B Instruct v0.1, available on AWS Marketplace. This high-quality sparse mixture of experts model with open weights can follow instructions, complete requests and generate creative text formats.NIM on AWS for EveryoneCustomers and partners across industries are tapping NIM on AWS to get to market faster, maintain security and control of their generative AI applications and data, and lower costs.SoftServe, an IT consulting and digital services provider, has developed six generative AI solutions fully deployed on AWS and accelerated by NVIDIA NIM and AWS services. The solutions, available on AWS Marketplace, include SoftServe Gen AI Drug Discovery, SoftServe Gen AI Industrial Assistant, Digital Concierge, Multimodal RAG System, Content Creator and Speech Recognition Platform.Theyre all based on NVIDIA AI Blueprints, comprehensive reference workflows that accelerate AI application development and deployment and feature NVIDIA acceleration libraries, software development kits and NIM microservices for AI agents, digital twins and more.Start Now With NIM on AWSDevelopers can deploy NVIDIA NIM microservices on AWS according to their unique needs and requirements. By doing so, developers and enterprises can achieve high-performance AI with NVIDIA-optimized inference containers across various AWS services.Visit the NVIDIA API catalog to try out over 100 different NIM-optimized models, and request either a developer license or 90-day NVIDIA AI Enterprise trial license to get started deploying the microservices on AWS services. Developers can also explore NIM microservices in the AWS Marketplace, Amazon Bedrock Marketplace or Amazon SageMaker JumpStart.See notice regarding software product information.

A recent survey from the Special Olympics Global Center for Inclusion in Education shows that while a majority of students with an intellectual and developmental disability (IDD) and their parents view AI as a potentially transformative technology, only 35% of educators believe that AI developers currently account for the needs and priorities of students with IDD.In this episode of the NVIDIA AI Podcast, U.S. Special Advisor on International Disability Rights at the U.S. Department of State Sara Minkara and Timothy Shriver, chairman of the board of Special Olympics, discuss AIs potential to enhance special education and disability inclusion.U.S. Special Advisor on International Disability Rights at the U.S. Department of State Sara Minkara at the G7 Summit. Image courtesy of the Government of Italy.They highlight the critical need to include the voices from disability communities in AI development and policy conversations. Minkara and Shriver also explain the cultural, financial and social importance of building an inclusive future.The AI Podcast How AI Can Help Boost Disability Inclusion Ep. 238Time Stamps2:12: Minkara and Shrivers work on disability inclusion9:47: Benefits of AI for people with disabilities20:46: Notes from the recent G7 ministerial meeting on inclusion and disability24:51: Challenges and future directions of AI in disability inclusionImage courtesy of Special Olympics.You Might Also LikeTaking AI to School: A Conversation With MITs Anant Agarwal Ep. 197Educators and technologists alike have long been excited about AIs potential to transform teaching and learning. Anant Agarwal, founder of edX and chief platform officer at 2U, talked about the future of online education and how AI is revolutionizing the learning experience.NVIDIAs Louis Stewart on How AI Is Shaping Workforce Development Ep. 237Workforce development is central to ensuring the changes brought by AI benefit all of us. Louis Stewart, head of strategic initiatives for NVIDIAs global developer ecosystem, explains what workforce development looks like in the age of AI, and why it all starts with education.Dotlumen CEO Cornel Amariei on Assistive Technology for the Visually Impaired Ep. 217Equipped with sensors and powered by AI, Dotlumen Glasses compute a safely walkable path for persons who are blind or have low vision, and offer haptic or tactile feedback on how to proceed via corresponding vibrations. Dotlumen founder and CEO Cornel Amariei discusses the challenges and breakthroughs of developing assistive technology.How the Ohio Supercomputer Center Drives the Future of Computing Ep. 213Alan Chalker, director of strategic programs at the Ohio Supercomputing Center, dives into the history and evolution of the OSC, how its working with client companies like NASCAR, and how the centers Open OnDemand program empowers Ohio higher education institutions and industries with computational services and training and educational programs.

As generative AI continues to grow, implementing and managing the right infrastructure becomes even more critical to ensure the secure and efficient development and deployment of AI-based solutions.To meet these needs, NVIDIA has introduced two professional-level certifications that offer structured paths for infrastructure and operations practitioners to enhance and validate the skills needed to work effectively with advanced AI technologies.The certification exams and recommended training to prepare for them are designed for network and system administrators, DevOps and MLOps engineers, and others who need to understand AI infrastructure and operations.NVIDIAs certification program equips professionals with skills in areas such as AI infrastructure, deep learning and accelerated computing to enhance their career prospects and give them a competitive edge in these high-demand fields.Developed in collaboration with industry experts, the program features relevant content that emphasizes practical application alongside theoretical knowledge.The NVIDIA-Certified Professional: AI Infrastructure certification is designed for practitioners seeking to showcase advanced skills in deploying AI infrastructure. Candidates must demonstrate expertise in GPU and DPU installation, hardware validation and system optimization for both AI and HPC workloads. The exam also tests proficiency in managing physical layers, configuring MIG, deploying the NVIDIA BlueField operating system, and integrating NVIDIAs cloud-native stack with Docker and NVIDIA NGC.To prepare for this professional-level certification, candidates are encouraged to attend the AI Infrastructure Professional Workshop. This hands-on training covers critical AI data center technologies, including compute platforms, GPU operations, networking, storage solutions and BlueField DPUs. The workshop is recommended for professionals aiming to elevate their AI infrastructure expertise.The NVIDIA-Certified Professional: AI Operations certification is tailored for individuals seeking proficiency in managing and optimizing AI operations. The exam tests expertise in managing AI data centers, including the use of Kubernetes, Slurm, MIG, BCM, NGC containers, storage configuration and DPU services.To prepare for this professional-level certification, candidates are encouraged to attend the AI Operations Professional Workshop, where they will gain hands-on experience in managing AI data centers, including compute platforms, networking, storage and GPU virtualization. The workshop also provides practical experience with NVIDIA AI software and solutions, including NGC containers and the NVIDIA AI Enterprise software suite, making it ideal for professionals looking to deepen their AI operations expertise.Both of these professional-level certifications build upon the foundational knowledge covered in the NVIDIA-Certified Associate: AI Infrastructure and Operations certification.Additional NVIDIA certifications include:NVIDIA-Certified Associate: Generative AI LLMs validates skills in using generative AI and large language modelsNVIDIA-Certified Associate: Generative AI Multimodal focuses on multimodal AI content creationSaleh Hassan, an embedded software engineer at Two Six Technologies, successfully completed three NVIDIA certification exams at the NVIDIA AI Summit in Washington, D.C., earlier this year.The knowledge I gained has definitely made me a better developer when it comes to integrating AI, said Hassan, who encourages others to pursue certifications as a key milestone for advancing their AI careers.Saleh Hassan showing off one of his NVIDIA certifications.All NVIDIA certifications are part of a comprehensive learning path that offers foundational courses, advanced training and hands-on labs to thoroughly prepare candidates for real-world applications.The certifications support individual career growth and organizations can use them to enhance workforce capabilities.Explore the options on the NVIDIA Certification portal and sign up for NVIDIAs monthly newsletter to stay updated on the latest offerings.

Field AI is building robot brains that enable robots to autonomously manage a wide range of industrial processes. Vention creates pretrained skills to ease development of robotic tasks. And Cobot offers Proxie, an AI-powered cobot designed to handle material movement and adapt to dynamic environments, working seamlessly alongside humans.These leading robotics startups are all making advances using NVIDIA Isaac Sim on Amazon Web Services. Isaac Sim is a reference application built on NVIDIA Omniverse for developers to simulate and test AI-driven robots in physically based virtual environments.NVIDIA announced at AWS re:Invent today that Isaac Sim now runs on Amazon Elastic Cloud Computing (EC2) G6e instances accelerated by NVIDIA L40S GPUs. And with NVIDIA OSMO, a cloud-native orchestration platform, developers can easily manage their complex robotics workflows across their AWS computing infrastructure.This combination of NVIDIA-accelerated hardware and software available on the cloud allows teams of any size to scale their physical AI workflows.Physical AI describes AI models that can understand and interact with the physical world. It embodies the next wave of autonomous machines and robots, such as self-driving cars, industrial manipulators, mobile robots, humanoids and even robot-run infrastructure like factories and warehouses.With physical AI, developers are embracing a three computer solution for training, simulation and inference to make breakthroughs.Yet physical AI for robotics systems requires robust training datasets to achieve precision inference in deployment. Developing such datasets, however, and testing them in real situations can be impractical and costly.Simulation offers an answer, as it can significantly accelerate the training, testing and deployment of AI-driven robots.Harnessing L40S GPUs in the Cloud to Scale Robotics Simulation and TrainingSimulation is used to verify, validate and optimize robot designs as well as the systems and their algorithms before deployment. Simulation can also optimize facility and system designs before construction or remodeling starts for maximum efficiencies, reducing costly manufacturing change orders.Amazon EC2 G6e instances accelerated by NVIDIA L40S GPUs provide a 2x performance gain over the prior architecture, while allowing the flexibility to scale as scene and simulation complexity grows. The instances are used to train many computer vision models that power AI-driven robots. This means the same instances can be extended for various tasks, from data generation to simulation to model training.Using NVIDIA OSMO in the cloud allows teams to orchestrate and scale complex robotics development workflows across distributed computing resources, whether on premises or in the AWS cloud.Isaac Sim provides access to the latest robotics simulation capabilities and the cloud, fostering collaboration. One of the critical workflows is generating synthetic data for perception model training.Using a reference workflow that combines NVIDIA Omniverse Replicator, a framework for building custom synthetic data generation (SDG) pipelines and a core extension of Isaac Sim, with NVIDIA NIM microservices, developers can build generative AI-enabled SDG pipelines.These include the USD Code NIM microservice for generating Python USD code and answering OpenUSD queries, and the USD Search NIM microservice for exploring OpenUSD assets using natural language or image inputs. The Edify 360 HDRi NIM microservice generates 360-degree environment maps, while the Edify 3D NIM microservice creates ready-to-edit 3D assets from text or image prompts. This eases the synthetic data generation process by reducing many tedious and manual steps, from asset creation to image augmentation, using the power of generative AI.Rendered.ais synthetic data engineering platform integrated with Omniverse Replicator enables companies to generate synthetic data for computer vision models used in industries from security and intelligence to manufacturing and agriculture.SoftServe, an IT consulting and digital services provider, uses Isaac Sim to generate synthetic data and validate robots used in vertical farming with Pfeifer & Langen, a leading European food producer.Tata Consultancy Services is building custom synthetic data generation pipelines to power its Mobility AI suite to address automotive and autonomous use cases by simulating real-world scenarios. Its applications include defect detection, end-of-line quality inspection and hazard avoidance.Learning to Be Robots in SimulationWhile Isaac Sim enables developers to test and validate robots in physically accurate simulation, Isaac Lab, an open-source robot learning framework built on Isaac Sim, provides a virtual playground for building robot policies that can run on AWS Batch.Because these simulations are repeatable, developers can easily troubleshoot and reduce the number of cycles required for validation and testing.Several robotics developers are embracing NVIDIA Isaac on AWS to develop physical AI, such as:Aescapes robots are able to provide precision-tailored massages by accurately modeling and tuning onboard sensors in Isaac Sim.Cobot has used Isaac Sim with its AI-powered cobot, Proxie, to optimize logistics in warehouses, hospitals, manufacturing sites, and more.Cohesive Robotics has integrated Isaac Sim into its software framework called Argus OS for developing and deploying robotic workcells used in high-mix manufacturing environments.Field AI, a builder of robot foundation models, uses Isaac Sim and Isaac Lab to evaluate the performance of its models in complex, unstructured environments across industries such as construction, manufacturing, oil and gas, mining and more.Standard Bots is simulating and validating the performance of its R01 robot used in manufacturing and machining setup.Swiss Mile is using Isaac Sim and Isaac Lab for robot learning so that wheeled quadruped robots can perform tasks autonomously with new levels of efficiency in factories and warehouses.Vention, which offers a full-stack cloud-based automation platform, is harnessing Isaac Sim for developing and testing new capabilities for robot cells used by small to medium-size manufacturers.Learn more about Isaac Sim 4.2, now available on Amazon EC2 G6e instances powered by NVIDIA L40S GPUs on AWS Marketplace.

Expanding whats possible for developers and enterprises in the cloud, NVIDIA and Amazon Web Services are converging at AWS re:Invent in Las Vegas this week to showcase new solutions designed to accelerate AI and robotics breakthroughs and simplify research in quantum computing development.AWS re:Invent is a conference for the global cloud-computing community packed with keynotes and more than 2,000 technical sessions.Announcement highlights include the availability of NVIDIA DGX Cloud on AWS and enhanced AI, quantum computing and robotics tools.NVIDIA DGX Cloud on AWS for AI at ScaleThe NVIDIA DGX Cloud AI computing platform is now available through AWS Marketplace Private Offers, offering a high-performance, fully managed solution for enterprises to train and customize AI models.DGX Cloud offers flexible terms, a fully managed and optimized platform, and direct access to NVIDIA experts to help businesses scale their AI capabilities quickly.Early adopter Leonardo.ai, part of the Canva family, is already using DGX Cloud on AWS to develop advanced design tools.AWS Liquid-Cooled Data Centers With NVIDIA BlackwellNewer AI servers benefit from liquid cooling to cool high-density compute chips more efficiently for better performance and energy efficiency. AWS has developed solutions that provide configurable liquid-to-chip cooling across its data centers.The cooling solution announced today will seamlessly integrate air- and liquid-cooling capabilities for the most powerful rack-scale AI supercomputing systems like NVIDIA GB200 NVL72, as well as AWS network switches and storage servers.This flexible, multimodal cooling design provides maximum performance and efficiency for running AI models and will be used for the next-generation NVIDIA Blackwell platform.Blackwell will be the foundation of Amazon EC2 P6 instances, DGX Cloud on AWS and Project Ceiba.NVIDIA Advances Physical AI With Accelerated Robotics Simulation on AWSNVIDIA is also expanding the reach of NVIDIA Omniverse on AWS with NVIDIA Isaac Sim, now running on high-performance Amazon EC2 G6e instances accelerated by NVIDIA L40S GPUs.Available now, this reference application built on NVIDIA Omniverse enables developers to simulate and test AI-driven robots in physically based virtual environments.One of the many workflows enabled by Isaac Sim is synthetic data generation. This pipeline is now further accelerated with the infusion of OpenUSD NIM microservices, from scene creation to data augmentation.Robotics companies such as Aescape, Cohesive Robotics, Cobot, Field AI, Standard Bots, Swiss Mile and Vention are using Isaac Sim to simulate and validate the performance of their robots prior to deployment.In addition, Rendered.ai, SoftServe and Tata Consultancy Services are using the synthetic data generation capabilities of Omniverse Replicator and Isaac Sim to bootstrap perception AI models that power various robotics applications.NVIDIA BioNeMo on AWS for Advanced AI-Based Drug DiscoveryNVIDIA BioNeMo NIM microservices and AI Blueprints, developed to advance drug discovery, are now integrated into AWS HealthOmics, a fully managed biological data compute and storage service designed to accelerate scientific breakthroughs in clinical diagnostics and drug discovery.This collaboration gives researchers access to AI models and scalable cloud infrastructure tailored to drug discovery workflows. Several biotech companies already use NVIDIA BioNeMo on AWS to drive their research and development pipelines.For example, A-Alpha Bio, a biotechnology company based in Seattle, recently published a study in biorxiv describing a collaborative effort with NVIDIA and AWS to develop and deploy an antibody AI model called AlphaBind.Using AlphaBind via the BioNeMo framework on Amazon EC2 P5 instances equipped with NVIDIA H100 Tensor Core GPUs, A-Alpha Bio achieved a 12x increase in inference speed and processed over 108 million inference calls in two months.Additionally, SoftServe today launched Drug Discovery, its generative AI solution built with NVIDIA Blueprints, to enable computer-aided drug discovery and efficient drug development. This solution is set to deliver faster workflows and will soon be available in AWS Marketplace.Real-Time AI Blueprints: Ready-to-Deploy Options for Video, Cybersecurity and MoreNVIDIAs latest AI Blueprints are available for instant deployment on AWS, making real-time applications like vulnerability analysis for container security, and video search and summarization agents readily accessible.Developers can easily integrate these blueprints into existing workflows to speed deployments.Developers and enterprises can use the NVIDIA AI Blueprint for video search and summarization to build visual AI agents that can analyze real-time or archived videos to answer user questions, generate summaries and enable alerts for specific scenarios.AWS collaborated with NVIDIA to provide a reference architecture applying the NVIDIA AI Blueprint for vulnerability analysis to augment early security patching in continuous integration pipelines on AWS cloud-native services.NVIDIA CUDA-Q on Amazon Braket: Quantum Computing Made PracticalNVIDIA CUDA-Q is now integrated with Amazon Braket to streamline quantum computing development. CUDA-Q users can use Amazon Brakets quantum processors, while Braket users can tap CUDA-Qs GPU-accelerated workflows for development and simulation.The CUDA-Q platform allows developers to build hybrid quantum-classical applications and run them on many different types of quantum processors, simulated and physical.Now preinstalled on Amazon Braket, CUDA-Q provides a seamless development platform for hybrid quantum-classical applications, unlocking new potential in quantum research.Enterprise Platform Providers and Consulting Leaders Advance AI With NVIDIA on AWSLeading software platforms and global system integrators are helping enterprises rapidly scale generative AI applications built with NVIDIA AI on AWS to drive innovation across industries.Cloudera is using NVIDIA AI on AWS to enhance its new AI inference solution, helping Mercy Corps improve the precision and effectiveness of its aid distribution technology.Cohesity has integrated NVIDIA NeMo Retriever microservices in its generative AI-powered conversational search assistant, Cohesity Gaia, to improve the recall performance of retrieval-augmented generation. Cohesity customers running on AWS can take advantage of the NeMo Retriever integration within Gaia.DataStax announced that Wikimedia Deutschland is applying the DataStax AI Platform to make Wikidata available to developers as an embedded vectorized database. The Datastax AI Platform is built with NVIDIA NeMo Retriever and NIM microservices, and available on AWS.Deloittes C-Suite AI now supports NVIDIA AI Enterprise software, including NVIDIA NIM microservices and NVIDIA NeMo for CFO-specific use cases, including financial statement analysis, scenario modeling and market analysis.RAPIDS Quick Start Notebooks Now Available on Amazon EMR NVIDIA and AWS are also speeding data science and data analytics workloads with the RAPIDS Accelerator for Apache Spark, which accelerates analytics and machine learning workloads with no code change and reduces data processing costs by up to 80%.Quick Start notebooks for RAPIDS Accelerator for Apache Spark are now available on Amazon EMR, Amazon EC2 and Amazon EMR on EKS. These offer a simple way to qualify Spark jobs tuned to maximize the performance of RAPIDS on GPUs, all within AWS EMR.NVIDIA and AWS Power the Next Generation of Industrial Edge SystemsThe NVIDIA IGX Orin and Jetson Orin platforms now integrate seamlessly with AWS IoT Greengrass to streamline the deployment and running of AI models at the edge and to efficiently manage fleets of connected devices at scale. This combination enhances scalability and simplifies the deployment process for industrial and robotics applications.Developers can now tap into NVIDIAs advanced edge computing power with AWS purpose-built IoT services, creating a secure, scalable environment for autonomous machines and smart sensors. A guide for getting started, authored by AWS, is now available to support developers putting these capabilities to work.The integration underscores NVIDIAs work in advancing enterprise-ready industrial edge systems to enable rapid, intelligent operations in real-world applications.Catch more of NVIDIAs work at AWS: re:Invent 2024 through live demos, technical sessions and hands-on labs.See notice regarding software product information.

3.6 billion. Thats about how many medical imaging tests are performed annually worldwide to diagnose, monitor and treat various conditions.Speeding up the processing and evaluation of all these X-rays, CT scans, MRIs and ultrasounds is essential to helping doctors manage their workloads and to improving health outcomes.Thats why NVIDIA introduced MONAI, which serves as an open-source research and development platform for AI applications used in medical imaging and beyond. MONAI unites doctors with data scientists to unlock the power of medical data to build deep learning models and deployable applications for medical AI workflows.This week at the annual meeting of RSNA, the Radiological Society of North America, NVIDIA announced that Siemens Healthineers has adopted MONAI Deploy, a module within MONAI that bridges the gap from research to clinical production, to boost the speed and efficiency of integrating AI workflows for medical imaging into clinical deployments.With over 15,000 installations in medical devices around the world, the Siemens Healthineers Syngo Carbon and syngo.via enterprise imaging platforms help clinicians better read and extract insights from medical images of many sources.Developers typically use a variety of frameworks when building AI applications. This makes it a challenge to deploy their applications into clinical environments.With a few lines of code, MONAI Deploy builds AI applications that can run anywhere. It is a tool for developing, packaging, testing, deploying and running medical AI applications in clinical production. Using it streamlines the process of developing and integrating medical imaging AI applications into clinical workflows.MONAI Deploy on the Siemens Healthineers platform has significantly accelerated the AI integration process, letting users port trained AI models into real-world clinical settings with just a few clicks, compared with what used to take months. This helps researchers, entrepreneurs and startups get their applications into the hands of radiologists more quickly.By accelerating AI model deployment, we empower healthcare institutions to harness and benefit from the latest advancements in AI-based medical imaging faster than ever, said Axel Heitland, head of digital technologies and research at Siemens Healthineers. With MONAI Deploy, researchers can quickly tailor AI models and transition innovations from the lab to clinical practice, providing thousands of clinical researchers worldwide access to AI-driven advancements directly on their syngo.via and Syngo Carbon imaging platforms.Enhanced with MONAI-developed apps, these platforms can significantly streamline AI integration. These apps can be easily provided and used on the Siemens Healthineers Digital Marketplace, where users can browse, select and seamlessly integrate them into their clinical workflows.MONAI Ecosystem Boosts Innovation and AdoptionNow marking its five-year anniversary, MONAI has seen over 3.5 million downloads, 220 contributors from around the world, acknowledgements in over 3,000 publications, 17 MICCAI challenge wins and use in numerous clinical products.The latest release of MONAI v1.4 includes updates that give researchers and clinicians even more opportunities to take advantage of the innovations of MONAI and contribute to Siemens Healthineers Syngo Carbon, syngo.via and the Siemens Healthineers Digital Marketplace.The updates in MONAI v1.4 and related NVIDIA products include new foundation models for medical imaging, which can be customized in MONAI and deployed as NVIDIA NIM microservices. The following models are now generally available as NIM microservices:MAISI (Medical AI for Synthetic Imaging) is a latent diffusion generative AI foundation model that can simulate high-resolution, full-format 3D CT images and their anatomic segmentations.VISTA-3D is a foundation model for CT image segmentation that offers accurate out-of-the-box performance covering over 120 major organ classes. It also offers effective adaptation and zero-shot capabilities to learn to segment novel structures.Alongside MONAI 1.4s major features, the new MONAI Multi-Modal Model, or M3, is now accessible through MONAIs VLM GitHub repo. M3 is a framework that extends any multimodal LLM with medical AI experts such as trained AI models from MONAIs Model Zoo. The power of this new framework is demonstrated by the VILA-M3 foundation model thats now available on Hugging Face, offering state-of-the-art radiological image copilot performance.MONAI Bridges Hospitals, Healthcare Startups and Research InstitutionsLeading healthcare institutions, academic medical centers, startups and software providers around the world are adopting and advancing MONAI, including:German Cancer Research Center leads MONAIs benchmark and metrics working group, which provides metrics for measuring AI performance and guidelines for how and when to use those metrics.Nadeem Lab from Memorial Sloan Kettering Cancer Center (MSK) pioneered the cloud-based deployment of multiple AI-assisted annotation pipelines and inference modules for pathology data using MONAI.University of Colorado School of Medicine faculty developed MONAI-based ophthalmology tools for detecting retinal diseases using a variety of imaging modalities. The university also leads some of the original federated learning developments and clinical demonstrations using MONAI.MathWorks has integrated MONAI Label with its Medical Imaging Toolbox, bringing medical imaging AI and AI-assisted annotation capabilities to thousands of MATLAB users engaged in medical and biomedical applications throughout academia and industry.GSK is exploring MONAI foundation models such as VISTA-3D and VISTA-2D for image segmentation.Flywheel offers a platform, which includes MONAI for streamlining imaging data management, automating research workflows, and enabling AI development and analysis, that scales for the needs of research institutions and life sciences organizations.Alara Imaging published its work on integrating MONAI foundation models such as VISTA-3D with LLMs such as Llama 3 at the 2024 Society for Imaging Informatics in Medicine conference.RadImageNet is exploring the use of MONAIs M3 framework to develop cutting-edge vision language models that utilize expert image AI models from MONAI to generate high-quality radiological reports.Kitware is providing professional software development services surrounding MONAI, helping integrate MONAI into custom workflows for device manufacturers as well as regulatory-approved products.Researchers and companies are also using MONAI on cloud service providers to run and deploy scalable AI applications. Cloud platforms providing access to MONAI include AWS HealthImaging, Google Cloud, Precision Imaging Network, part of Microsoft Cloud for Healthcare, and Oracle Cloud Infrastructure.See disclosure statements about syngo.via, Syngo Carbon and products in the Digital Marketplace.

Turn Black Friday into Green Thursday with a new deal on GeForce NOW Ultimate and Performance memberships this week. For a limited time, get 50% off new Ultimate or Performance memberships for the first three months to experience the power of GeForce RTX-powered gaming at a fraction of the cost.The giving continues for GeForce NOW members: SteelSeries is offering a 30% discount exclusively to all GeForce NOW members on Stratus+ or Nimbus+ controllers, perfect for gaming anytime, anywhere when paired with GeForce NOW on Android and iOS devices. To redeem the discount, opt in to GeForce NOW rewards and look out for an email with details. Enjoy this exclusive offer on its own it cant be combined with other SteelSeries promotions.Its not a GFN Thursday without new games this week, six are joining the over 2,000 titles in the GeForce NOW library.Plus, the Steam Autumn Sale is happening now, featuring stellar discounts on GeForce NOW-supported games. Snag beloved publishers top titles, including Nightingale from Inflexion Games, Remnant and Remnant II from Arc Games, and Cult of the Lamb and The Plucky Squire from Devolver and even more from publishers Frost Giant Studios, Metric Empire, tinyBuild, Torn Banner Studios and Tripwire. The sale runs through Wednesday, Dec. 4.Stuff Your StockingsThis holiday season, GeForce NOW is spreading cheer to gamers everywhere with an irresistible Black Friday offer. Those looking to try out the cloud gaming service can now level up their gaming with 50% off new Ultimate and Performance memberships for the first three months. Its the perfect time for gamers to treat themselves or a buddy to GeForce RTX-powered gaming without having to upgrade any hardware.Thankful for cloud gaming discounts.Lock in all the perks of the newly enhanced Performance membership, now featuring crisp 1440p streaming, at half off for the next three months. Or go all out with the Ultimate tier delivering the same premium experience GeForce RTX 4080 GPU owners enjoy now available at the regular monthly cost of a Performance membership.With a GeForce NOW membership, gamers can stream over 2,000 PC games from popular digital gaming stores with longer gaming sessions and real-time ray tracing for supported titlgames across nearly all devices. Performance members can stream at up to 1440p at 60 frames per second, and Ultimate members can stream up to 4K at 120 fps or 1080p at 240 fps.Dont let this festive deal slip away give the gift of gaming this holiday season with GeForce NOWs Black Friday sale. Whether battling winter bosses or exploring snowy landscapes, do it with exceptional performance at an exceptional price.Elevating New GamesIn addition, members can look for the following:New Arc Line (New release on Steam, Nov. 26)MEGA MAN X DiVE Offline Demo (Steam)PANICORE (Steam)Resident Evil 7 Teaser: Beginning Hour Demo (Steam)Slime Rancher (Steam)Sumerian Six (Steam)What are you planning to play this weekend? Let us know on X or in the comments below.Black Friday came early. Enjoy 50% off your first 3 months of an Ultimate or Performance monthly membership!Offer available for new & upgrading membersgrab it today! https://t.co/2quxLMccsG pic.twitter.com/3eRx930oSw NVIDIA GeForce NOW (@NVIDIAGFN) November 27, 2024

Editors note: This post is part of the AI Decoded series, which demystifies AI by making the technology more accessible, and showcases new hardware, software, tools and accelerations for GeForce RTX PC and NVIDIA RTX workstation users.Generative AI has transformed the way people bring ideas to life. Agentic AI takes this one step further using sophisticated, autonomous reasoning and iterative planning to help solve complex, multi-step problems.AnythingLLM is a customizable open-source desktop application that lets users seamlessly integrate large language model (LLM) capabilities into various applications locally on their PCs. It enables users to harness AI for tasks such as content generation, summarization and more, tailoring tools to meet specific needs.Accelerated on NVIDIA RTX AI PCs, AnythingLLM has launched a new Community Hub where users can share prompts, slash commands and AI agent skills while experimenting with building and running AI agents locally.Autonomously Solve Complex, Multi-Step Problems With Agentic AIAI agents can take chatbot capabilities further. They typically understand the context of the tasks and can analyze challenges and develop strategies and some can even fully execute assigned tasks.For example, while a chatbot could answer a prompt asking for a restaurant recommendation, an AI agent could even surface the restaurants phone number for a reservation and add reminders to the users calendar.Agents help achieve big-picture goals and dont get bogged down at the task level. There are many agentic apps in development to tackle to-do lists, manage schedules, help organize tasks, automate email replies, recommend personalized workout plans or plan trips.Once prompted, an AI agent can gather and process data from various sources, including databases. It can use an LLM for reasoning for example, to understand the task then generate solutions and specific functions. If integrated with external tools and software, an AI agent can next execute the task.Some sophisticated agents can even be improved through a feedback loop. When the data it generates is fed back into the system, the AI agent becomes smarter and faster.A step-by-step look at the process behind agentic AI systems. AI agents process user input, retrieve information from databases and other sources, and refine tasks in real time to deliver actionable results.Accelerated by NVIDIA RTX AI PCs, these agents can perform inferencing and execute tasks faster than any other PC. Users can operate the agent locally to help ensure data privacy, even without an internet connection.AnythingLLM: A Community Effort, Accelerated by RTXThe AI community is already diving into the possibilities of agentic AI, experimenting with ways to create smarter, more capable systems.Applications like AnythingLLM let developers easily build, customize and unlock agentic AI with their favorite models like Llama and Mistral as well as with other tools, such as Ollama and LMStudio. AnythingLLM is accelerated on RTX-powered AI PCs and workstations with high-performance Tensor Cores, dedicated hardware that provides the compute performance needed to run the latest and most demanding AI models.AnythingLLM is designed to make working with AI seamless, productive and accessible to everyone. It allows users to chat with their documents using intuitive interfaces, use AI agents to handle complex and custom tasks, and run cutting-edge LLMs locally on RTX-powered PCs and workstations. This means unlocked access to local resources, tools and applications that typically cant be integrated with cloud- or browser-based applications, or those that require extensive setup and knowledge to build. By tapping into the power of NVIDIA RTX GPUs, AnythingLLM delivers faster, smarter and more responsive AI for a variety of workflows all within a single desktop application.AnythingLLMs Community Hub lets AI enthusiasts easily access system prompts that can help steer LLM behavior, discover productivity-boosting slash commands, build specialized AI agent skills for unique workflows and custom tools, and access on-device resources.Example of a user invoking the agent to complete a Web Search query.Some example agent skills that are available in the Community Hub include Microsoft Outlook email assistants, calendar agents, web searches and home assistant controllers, as well as agents for populating and even integrating custom application programming interface endpoints and services for a specific use case.By enabling AI enthusiasts to download, customize and use agentic AI workflows on their own systems with full privacy, AnythingLLM is fueling innovation and making it easier to experiment with the latest technologies whether building a spreadsheet assistant or tackling more advanced workflows.Experience AnythingLLM now.Powered by People, Driven by InnovationAnythingLLM showcases how AI can go beyond answering questions to actively enhancing productivity and creativity. Such applications illustrate AIs move toward becoming an essential collaborator across workflows.Agentic AIs potential applications are vast and require creativity, expertise and computing capabilities. NVIDIA RTX AI PCs deliver peak performance for running agents locally, whether accomplishing simple tasks like generating and distributing content, or managing more complex use cases such as orchestrating enterprise software.Learn more and get started with agentic AI.Generative AI is transforming gaming, videoconferencing and interactive experiences of all kinds. Make sense of whats new and whats next by subscribing to the AI Decoded newsletter.

With startup Zordi, founder Gilwoo Lees enthusiasm for robotics, healthy eating, better produce and sustainable farming has taken root.Lee hadnt even finished her Ph.D. in AI and robotics at the University of Washington when investors seeded her ambitious plans for autonomous agriculture. Since researcher-turned-entrepreneur Lee founded Zordi in 2020 with Casey Call, formerly head grower at vertical farming startup Plenty, the robotic grower of strawberries has landed its fruits in Wegmans and is now expanding with partner farms in New Jersey and California.The most rewarding part is that the fruits you get taste amazing, said Lee. Youre able to consistently do that throughout the cycle of the plant because you are constantly optimizing.The company has two types of robots within its hydroponic operations. One is a scouting robot for gathering information on the health of plants using foundational models. The other is a harvesting robot for delicately picking and placing fruits and handling other tasks.Zordi, whose engineering team is based outside Boston, has farms in southern New Jersey and western New York. The company uses NVIDIA GPUs in the cloud and on desktops for training everything from crop health models to those for fruit picking and assessing fruit quality.Lee aims to deploy autonomous greenhouse systems globally to support regional markets, cutting down on the carbon footprint for transportation as well as providing fresher, better-tasting fruits grown more sustainably.Having operated greenhouses in New York and New Jersey for two years, the company recently formed partnerships with greenhouse farms in New Jersey and California to meet growing demand.Zordi is bringing NVIDIA accelerated AI automation to indoor growing that in many ways is parallel to developments in manufacturing and fulfillment operations.Adopting Jetson for Sustainable Farming, Energy EfficiencyZordi is building AI models and robots to enable sustainable farming at scale. It uses NVIDIA Jetson AGX Orin modules for testing out gathering sensor data and running its models to recognize the health of plants, flowers and fruits, early pest and disease symptoms, and the needs for hydration and nutrition, as well as light and temperature management.Jetsons energy efficiency and the availability of low-cost, high performance cameras from NVIDIA partners are attractive attributes for Zordi, said Lee. The company runs several cameras on each of its robots to collect data.Jetson opens up a lot of lower-cost cameras, said Lee. It lets us play with different cameras and gives us better battery management.With its scouting and harvesting robots, Zordi also aims to address a big issue farms worldwide complain about: a labor shortage that affects operations, leaving fruits and vegetables sometimes unattended and unharvested altogether.Zordi is planning to scale up its growing operations to meet consumer demand. The company expects that it can do more with AI and robotic automation despite labor challenges.We want our harvesting robots to do more dexterous tasks, like pruning leaves, with the help of simulation, said Lee.Omniverse Isaac Sim and Digital Twins to Boost OperationsZordi is looking at how to boost its indoor growing operations with AI much like industrial manufacturers do, using Isaac Sim in Omniverse for simulations and digital twins to optimize operations.The companys software platform for viewing all the data collected from its robots sensors provides a live dashboard with a spatial map. It offers a real-time view of every plant in its facilities so that its easy to monitor the production remotely.Whats more, it analyzes plant health and makes optional crop care recommendations using foundational models so that inexperienced farm operators can manage farms like experts.Were literally one step away from putting this all into Isaac Sim and Omniverse, said Lee, whose Ph.D. dissertation covered reinforcement learning and sim-to-real.Zordi is working on gripping simulations for strawberries as well as for cucumbers and tomatoes to expand into other categories.With strawberries or any other crops, if you can handle them delicately, then it helps with longer shelf life, Lee said.Lee is optimistic that the simulations in Isaac Sim will not only boost Zordis performance in harvest, but also let it do other manipulation tasks in other scenarios.Big picture, Zordi aims to create a fully autonomous farming system that makes farming easy and profitable, with AI recommending sustainable crop-care decisions and robots doing the hard work.Whats really important for us is how do we automate this, and how do we have a thinking AI that is actually making decisions for the farm with a lot of automations, said Lee.

AI is changing industries and economies worldwide.Workforce development is central to ensuring the changes benefit all of us, as Louis Stewart, head of strategic initiatives for NVIDIAs global developer ecosystem, explains in the latest AI Podcast.AI is fueling a lot of change in all ecosystems right now, Stewart said. Its disrupting how we think about traditional economic development how states and countries plan, how they stay competitive globally, and how they develop their workforces.Providing AI education, embracing the technology and addressing workforce challenges are all critical for future workplace development.It starts with education, Stewart said.The AI Podcast NVIDIAs Louis Stewart on How AI Is Shaping Workforce DevelopmentAI Education Crucial at All LevelsEducating people on what AI can do, and how the current generation of AI-powered tools work, is the starting point. AI education must come at all levels, according to Stewart however, higher education systems, in particular, need to be thinking about whats coming next, so graduating students can optimize their employability.Graduates need to understand AI, and need to have had touches in AI, he explained. Stewart emphasizes that this is broader than an engineering or a research challenge. This is really a true workforce issue.Stewart points to Gwinnett County in Georgia as an early education example, where the community has developed a full K-16 curriculum.If young kids are already playing with AI on their phones, they should actually be thinking about it a little bit deeper, he said. The idea, he explained, is for kids to move beyond simply using the tech to start seeing themselves as future creators of new technology, and being part of the broader evolution.Nobody Gets Left OutBeyond the classroom, a comprehensive view of AI education would expose people in the broader community to AI learning opportunities, Stewart said. His experience in the public sector informs his decidedly inclusive view on the matter.Before joining NVIDIA four years ago, Stewart spent more than a decade working for the state of California, and then its capital city of Sacramento. He points to his time as Sacramentos chief innovation officer to illustrate how important it is that all citizens be included in progress.Sacramento was trying to move into a place to be an innovation leader in the state and nationally. I knew the city because I grew up here, and I knew that there were areas of the city that would never see innovation unless it was brought to them, he explained. So if I was bringing autonomous cars to Sacramento, it was for the legislators, and it was for the CHP (California Highway Patrol), but it was also for the people.Stewart elaborated that everyone coming in touch with self-driving vehicles needed to understand their impact. There was the technology itself how autonomous vehicles work, how to use them as a passenger and so forth.But there were also broader questions, such as how mechanics would need new training to understand the computer systems powering autonomous cars. And how parents would need to understand self-driving vehicles from the point of view of getting their kids to and from school without having to miss work to do the driving themselves.Just as individuals will have different needs and wants from AI systems, so too will different communities, businesses and states take different approaches when implementing AI, Stewart said.Diverse Approaches to AI ImplementationPublic-private partnerships are critical to implementing AI across the U.S. and beyond. NVIDIA is partnering with states and higher education systems across the country for AI workforce development. And the programs being put in place are just as diverse as the states themselves.Every state has their idea about what they want to do when it comes to AI, Stewart explained.Still, some common goals hold across state lines. When Stewarts team engages a governors office with talk of AI to empower the workforce, create job opportunities, and improve collaboration, inclusivity and growth, he finds that state officials listen.Stewart added that they often open up about what theyve been working on. Weve been pleasantly surprised at how far along some of the states are with their AI strategies, he said.In August, NVIDIA announced it is working with the state of California to train 100,000 people on AI skills over the next three years. Its an undertaking that will involve all 116 of the states community colleges and Californias university system. NVIDIA will also collaborate with the California human resources system to help state employees understand how AI skills may be incorporated into current and future jobs.In Mississippi, a robust AI strategy is already in place.The Mississippi Artificial Intelligence Network (MAIN) is one of the first statewide initiatives focused on addressing the emergence of AI and its effects on various industries workforces. MAIN works with educational partners that include community colleges and universities in Mississippi, all collaborating to facilitate AI education and training.Embrace Technology, Embrace the FutureStewart said its important to encourage individuals, businesses and other organizations to actively engage with AI tools and develop an understanding of how theyre benefiting the workforce landscape.Now is not the time to stay on the sidelines, said Stewart.This is the time to jump in and start understanding.Small businesses, for example, can start using applications like ChatGPT to see firsthand how they can transform operations. From there, Stewart suggests, a business could partner with the local school system to empower student interns to develop AI-powered tools and workflows for data analysis, marketing and other needs.Its a win-win: The business can transform itself with AI while playing a crucial part in developing the workforce by giving students valuable real-world experience.Its crucial that people get up to speed on the changes that AI is driving. And that we all participate in shaping our collective future, Stewart explained.Workforce development is, I think, at the crux of this next part of the conversation because the innovation and the research and everything surrounding AI is driving change so rapidly, he said.Hear more from NVIDIAs Louis Stewart on workforce development opportunities in the latest AI Podcast.

A team of generative AI researchers created a Swiss Army knife for sound, one that allows users to control the audio output simply using text.While some AI models can compose a song or modify a voice, none have the dexterity of the new offering.Called Fugatto (short for Foundational Generative Audio Transformer Opus 1), it generates or transforms any mix of music, voices and sounds described with prompts using any combination of text and audio files.For example, it can create a music snippet based on a text prompt, remove or add instruments from an existing song, change the accent or emotion in a voice even let people produce sounds never heard before.This thing is wild, said Ido Zmishlany, a multi-platinum producer and songwriter and cofounder of One Take Audio, a member of the NVIDIA Inception program for cutting-edge startups. Sound is my inspiration. Its what moves me to create music. The idea that I can create entirely new sounds on the fly in the studio is incredible.A Sound Grasp of AudioWe wanted to create a model that understands and generates sound like humans do, said Rafael Valle, a manager of applied audio research at NVIDIA and one of the dozen-plus people behind Fugatto, as well as an orchestral conductor and composer.Supporting numerous audio generation and transformation tasks, Fugatto is the first foundational generative AI model that showcases emergent properties capabilities that arise from the interaction of its various trained abilities and the ability to combine free-form instructions.Fugatto is our first step toward a future where unsupervised multitask learning in audio synthesis and transformation emerges from data and model scale, Valle said.A Sample Playlist of Use CasesFor example, music producers could use Fugatto to quickly prototype or edit an idea for a song, trying out different styles, voices and instruments. They could also add effects and enhance the overall audio quality of an existing track.The history of music is also a history of technology. The electric guitar gave the world rock and roll. When the sampler showed up, hip-hop was born, said Zmishlany. With AI, were writing the next chapter of music. We have a new instrument, a new tool for making music and thats super exciting.An ad agency could apply Fugatto to quickly target an existing campaign for multiple regions or situations, applying different accents and emotions to voiceovers.Language learning tools could be personalized to use any voice a speaker chooses. Imagine an online course spoken in the voice of any family member or friend.Video game developers could use the model to modify prerecorded assets in their title to fit the changing action as users play the game. Or, they could create new assets on the fly from text instructions and optional audio inputs.Making a Joyful NoiseOne of the models capabilities were especially proud of is what we call the avocado chair, said Valle, referring to a novel visual created by a generative AI model for imaging.For instance, Fugatto can make a trumpet bark or a saxophone meow. Whatever users can describe, the model can create.With fine-tuning and small amounts of singing data, researchers found it could handle tasks it was not pretrained on, like generating a high-quality singing voice from a text prompt.Users Get Artistic ControlsSeveral capabilities add to Fugattos novelty.During inference, the model uses a technique called ComposableART to combine instructions that were only seen separately during training. For example, a combination of prompts could ask for text spoken with a sad feeling in a French accent.The models ability to interpolate between instructions gives users fine-grained control over text instructions, in this case the heaviness of the accent or the degree of sorrow.I wanted to let users combine attributes in a subjective or artistic way, selecting how much emphasis they put on each one, said Rohan Badlani, an AI researcher who designed these aspects of the model.In my tests, the results were often surprising and made me feel a little bit like an artist, even though Im a computer scientist, said Badlani, who holds a masters degree in computer science with a focus on AI from Stanford.The model also generates sounds that change over time, a feature he calls temporal interpolation. It can, for instance, create the sounds of a rainstorm moving through an area with crescendos of thunder that slowly fade into the distance. It also gives users fine-grained control over how the soundscape evolves.Plus, unlike most models, which can only recreate the training data theyve been exposed to, Fugatto allows users to create soundscapes its never seen before, such as a thunderstorm easing into a dawn with the sound of birds singing.A Look Under the HoodFugatto is a foundational generative transformer model that builds on the teams prior work in areas such as speech modeling, audio vocoding and audio understanding.The full version uses 2.5 billion parameters and was trained on a bank of NVIDIA DGX systems packing 32 NVIDIA H100 Tensor Core GPUs.Fugatto was made by a diverse group of people from around the world, including India, Brazil, China, Jordan and South Korea. Their collaboration made Fugattos multi-accent and multilingual capabilities stronger.One of the hardest parts of the effort was generating a blended dataset that contains millions of audio samples used for training. The team employed a multifaceted strategy to generate data and instructions that considerably expanded the range of tasks the model could perform, while achieving more accurate performance and enabling new tasks without requiring additional data.They also scrutinized existing datasets to reveal new relationships among the data. The overall work spanned more than a year.Valle remembers two moments when the team knew it was on to something. The first time it generated music from a prompt, it blew our minds, he said.Later, the team demoed Fugatto responding to a prompt to create electronic music with dogs barking in time to the beat.When the group broke up with laughter, it really warmed my heart.Hear what Fugatto can do:

Editors note: This article, originally published on November 15, 2023, has been updated.To understand the latest advance in generative AI, imagine a courtroom.Judges hear and decide cases based on their general understanding of the law. Sometimes a case like a malpractice suit or a labor dispute requires special expertise, so judges send court clerks to a law library, looking for precedents and specific cases they can cite.Like a good judge, large language models (LLMs) can respond to a wide variety of human queries. But to deliver authoritative answers that cite sources, the model needs an assistant to do some research.The court clerk of AI is a process called retrieval-augmented generation, or RAG for short.How It Got Named RAGPatrick Lewis, lead author of the 2020 paper that coined the term, apologized for the unflattering acronym that now describes a growing family of methods across hundreds of papers and dozens of commercial services he believes represent the future of generative AI.Patrick LewisWe definitely would have put more thought into the name had we known our work would become so widespread, Lewis said in an interview from Singapore, where he was sharing his ideas with a regional conference of database developers.We always planned to have a nicer sounding name, but when it came time to write the paper, no one had a better idea, said Lewis, who now leads a RAG team at AI startup Cohere.So, What Is Retrieval-Augmented Generation (RAG)?Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources.In other words, it fills a gap in how LLMs work. Under the hood, LLMs are neural networks, typically measured by how many parameters they contain. An LLMs parameters essentially represent the general patterns of how humans use words to form sentences.That deep understanding, sometimes called parameterized knowledge, makes LLMs useful in responding to general prompts at light speed. However, it does not serve users who want a deeper dive into a current or more specific topic.Combining Internal, External ResourcesLewis and colleagues developed retrieval-augmented generation to link generative AI services to external resources, especially ones rich in the latest technical details.The paper, with coauthors from the former Facebook AI Research (now Meta AI), University College London and New York University, called RAG a general-purpose fine-tuning recipe because it can be used by nearly any LLM to connect with practically any external resource.Building User TrustRetrieval-augmented generation gives models sources they can cite, like footnotes in a research paper, so users can check any claims. That builds trust.Whats more, the technique can help models clear up ambiguity in a user query. It also reduces the possibility a model will make a wrong guess, a phenomenon sometimes called hallucination.Another great advantage of RAG is its relatively easy. A blog by Lewis and three of the papers coauthors said developers can implement the process with as few as five lines of code.That makes the method faster and less expensive than retraining a model with additional datasets. And it lets users hot-swap new sources on the fly.How People Are Using RAGWith retrieval-augmented generation, users can essentially have conversations with data repositories, opening up new kinds of experiences. This means the applications for RAG could be multiple times the number of available datasets.For example, a generative AI model supplemented with a medical index could be a great assistant for a doctor or nurse. Financial analysts would benefit from an assistant linked to market data.In fact, almost any business can turn its technical or policy manuals, videos or logs into resources called knowledge bases that can enhance LLMs. These sources can enable use cases such as customer or field support, employee training and developer productivity.The broad potential is why companies including AWS, IBM, Glean, Google, Microsoft, NVIDIA, Oracle and Pinecone are adopting RAG.Getting Started With Retrieval-Augmented GenerationTo help users get started, NVIDIA developed an AI Blueprint for building virtual assistants. Organizations can use this reference architecture to quickly scale their customer service operations with generative AI and RAG, or get started building a new customer-centric solution.The blueprint uses some of the latest AI-building methodologies and NVIDIA NeMo Retriever, a collection of easy-to-use NVIDIA NIM microservices for large-scale information retrieval. NIM eases the deployment of secure, high-performance AI model inferencing across clouds, data centers and workstations.These components are all part of NVIDIA AI Enterprise, a software platform that accelerates the development and deployment of production-ready AI with the security, support and stability businesses need.There is also a free hands-on NVIDIA LaunchPad lab for developing AI chatbots using RAG so developers and IT teams can quickly and accurately generate responses based on enterprise data.Getting the best performance for RAG workflows requires massive amounts of memory and compute to move and process data. The NVIDIA GH200 Grace Hopper Superchip, with its 288GB of fast HBM3e memory and 8 petaflops of compute, is ideal it can deliver a 150x speedup over using a CPU.Once companies get familiar with RAG, they can combine a variety of off-the-shelf or custom LLMs with internal or external knowledge bases to create a wide range of assistants that help their employees and customers.RAG doesnt require a data center. LLMs are debuting on Windows PCs, thanks to NVIDIA software that enables all sorts of applications users can access even on their laptops.An example application for RAG on a PC.PCs equipped with NVIDIA RTX GPUs can now run some AI models locally. By using RAG on a PC, users can link to a private knowledge source whether that be emails, notes or articles to improve responses. The user can then feel confident that their data source, prompts and response all remain private and secure.A recent blog provides an example of RAG accelerated by TensorRT-LLM for Windows to get better results fast.The History of RAGThe roots of the technique go back at least to the early 1970s. Thats when researchers in information retrieval prototyped what they called question-answering systems, apps that use natural language processing (NLP) to access text, initially in narrow topics such as baseball.The concepts behind this kind of text mining have remained fairly constant over the years. But the machine learning engines driving them have grown significantly, increasing their usefulness and popularity.In the mid-1990s, the Ask Jeeves service, now Ask.com, popularized question answering with its mascot of a well-dressed valet. IBMs Watson became a TV celebrity in 2011 when it handily beat two human champions on the Jeopardy! game show.Today, LLMs are taking question-answering systems to a whole new level.Insights From a London LabThe seminal 2020 paper arrived as Lewis was pursuing a doctorate in NLP at University College London and working for Meta at a new London AI lab. The team was searching for ways to pack more knowledge into an LLMs parameters and using a benchmark it developed to measure its progress.Building on earlier methods and inspired by a paper from Google researchers, the group had this compelling vision of a trained system that had a retrieval index in the middle of it, so it could learn and generate any text output you wanted, Lewis recalled.The IBM Watson question-answering system became a celebrity when it won big on the TV game show Jeopardy!When Lewis plugged into the work in progress a promising retrieval system from another Meta team, the first results were unexpectedly impressive.I showed my supervisor and he said, Whoa, take the win. This sort of thing doesnt happen very often, because these workflows can be hard to set up correctly the first time, he said.Lewis also credits major contributions from team members Ethan Perez and Douwe Kiela, then of New York University and Facebook AI Research, respectively.When complete, the work, which ran on a cluster of NVIDIA GPUs, showed how to make generative AI models more authoritative and trustworthy. Its since been cited by hundreds of papers that amplified and extended the concepts in what continues to be an active area of research.How Retrieval-Augmented Generation WorksAt a high level, heres how an NVIDIA technical brief describes the RAG process.When users ask an LLM a question, the AI model sends the query to another model that converts it into a numeric format so machines can read it. The numeric version of the query is sometimes called an embedding or a vector.Retrieval-augmented generation combines LLMs with embedding models and vector databases.The embedding model then compares these numeric values to vectors in a machine-readable index of an available knowledge base. When it finds a match or multiple matches, it retrieves the related data, converts it to human-readable words and passes it back to the LLM.Finally, the LLM combines the retrieved words and its own response to the query into a final answer it presents to the user, potentially citing sources the embedding model found.Keeping Sources CurrentIn the background, the embedding model continuously creates and updates machine-readable indices, sometimes called vector databases, for new and updated knowledge bases as they become available.Retrieval-augmented generation combines LLMs with embedding models and vector databases.Many developers find LangChain, an open-source library, can be particularly useful in chaining together LLMs, embedding models and knowledge bases. NVIDIA uses LangChain in its reference architecture for retrieval-augmented generation.The LangChain community provides its own description of a RAG process.Looking forward, the future of generative AI lies in creatively chaining all sorts of LLMs and knowledge bases together to create new kinds of assistants that deliver authoritative results users can verify.Explore generative AI sessions and experiences at NVIDIA GTC, the global conference on AI and accelerated computing, running March 18-21 in San Jose, Calif., and online.

Get ready to dive deeper into the criminal underworld of a galaxy far, far away as GeForce NOW brings the first major story pack for Star Wars Outlaws to the cloud this week.The season of giving continues GeForce NOW members can access a new free reward: a special in-game Star Wars Outlaws enhancement.Its all part of an exciting GFN Thursday, topped with five new games joining the more than 2,000 titles supported in the GeForce NOW library, including the launch of S.T.A.L.K.E.R. 2: Heart of Chornobyl and Xbox Gaming Studios fan favorites Fallout 3: Game of the Year Edition and The Elder Scrolls IV: Oblivion.And make sure not to pass this opportunity up gamers who want to take the Performance and Ultimate memberships for a spin can do so with 25% off Day Passes, now through Friday, Nov. 22. Day Passes give access to 24 continuous hours of powerful cloud gaming.A New Saga BeginsThe galaxys most electrifying escapade gets even more exciting with the new Wild Card story pack for Star Wars Outlaws.This thrilling story pack invites scoundrels to join forces with the galaxys smoothest operator, Lando Calrissian, for a high-stakes Sabacc tournament thatll keep players on the edge of their seats. As Kay Vess, gamers bluff, charm and blast their way through new challenges, exploring uncharted corners of the Star Wars galaxy. Meanwhile, a free update will scatter fresh Contract missions across the stars, offering members ample opportunities to build their reputations and line their pockets with credits.To kick off this thrilling underworld adventure, GeForce NOW members are in for a special reward with the Forest Commando Character Pack.Time to get wild.The pack gives Kay and Nix, her loyal companion, a complete set of gear thats perfect for missions in lush forest worlds. Get equipped with tactical trousers, a Bantha leather belt loaded with attachments, a covert poncho to shield against jungle rain and a hood for Nix thats great for concealment in thick forests.Members of the GeForce NOW rewards program can check their email for instructions on how to claim the reward. Ultimate and Performance members can start redeeming style packages today. Dont miss out this offer is available through Saturday, Dec. 21, on a first-come, first-served basis.Welcome to the ZoneWelcome to the zone.S.T.A.L.K.E.R. 2: Heart of Chornobyl, the highly anticipated sequel in the cult-classic S.T.A.L.K.E.R. series, is a first-person-shooter survival-horror game set in the Chornobyl Exclusion Zone.In the game which blends postapocalyptic fiction with Ukrainian folklore and the eerie reality of the Chornobyl disaster players can explore a vast open world filled with mutated creatures, anomalies and other stalkers while uncovering the zones secrets and battling for survival.The title features advanced graphics and physics powered by Unreal Engine 5 for stunningly realistic and detailed environments. Players choices impact the game world and narrative, which comprises a nonlinear storyline with multiple possible endings.Players will take on challenging survival mechanics to test their skills and decision-making abilities. Members can make their own epic story with a Performance membership for enhanced GeForce RTX-powered streaming at 1440p or an Ultimate membership for up to 4K 120 frames per second streaming, offering the crispest visuals and smoothest gameplay.Adventures AwaitVault 101 has opened.Members can emerge from Vault 101 into the irradiated ruins of Washington, D.C., in Fallout 3: Game of the Year Edition, which includes all five downloadable content packs released for Fallout 3. Experience the game that redefined the postapocalyptic genre with its morally ambiguous choices, memorable characters and the innovative V.A.T.S. combat system. Whether revisiting the Capital Wasteland, exploring the Mojave Desert or delving into the realm of Cyrodiil, these iconic titles have never looked or played better thanks to the power of GeForce NOWs cloud streaming technology.Members can look for the following games available to stream in the cloud this week:Towers of Aghasba (New release on Steam, Nov. 19)S.T.A.L.K.E.R. 2: Heart of Chornobyl (New release on Steam and Xbox, available on PC Game Pass, Nov. 20)Star Wars Outlaws (New release on Steam, Nov. 21)The Elder Scrolls IV: Oblivion Game of the Year Edition (Epic Games Store, Steam and Xbox, available on PC Game Pass)Fallout 3: Game of the Year Edition (Epic Games Store, Steam and Xbox, available on PC Game Pass)What are you planning to play this weekend? Let us know on X or in the comments below.which sci-fi series or movie would make a great game? NVIDIA GeForce NOW (@NVIDIAGFN) November 20, 2024

Robots are moving goods in warehouses, packaging foods and helping assemble vehicles bringing enhanced automation to use cases across industries.There are two keys to their success: Physical AI and robotics simulation.Physical AI describes AI models that can understand and interact with the physical world. Physical AI embodies the next wave of autonomous machines and robots, such as self-driving cars, industrial manipulators, mobile robots, humanoids and even robot-run infrastructure like factories and warehouses.With virtual commissioning of robots in digital worlds, robots are first trained using robotic simulation software before they are deployed for real-world use cases.Robotics Simulation SummarizedAn advanced robotics simulator facilitates robot learning and testing of virtual robots without requiring the physical robot. By applying physics principles and replicating real-world conditions, these simulators generate synthetic datasets to train machine learning models for deployment on physical robots.Simulations are used for initial AI model training and then to validate the entire software stack, minimizing the need for physical robots during testing. NVIDIA Isaac Sim, a reference application built on the NVIDIA Omniverse platform, provides accurate visualizations and supports Universal Scene Description (OpenUSD)-based workflows for advanced robot simulation and validation.NVIDIAs 3 Computer Framework Facilitates Robot SimulationThree computers are needed to train and deploy robot technology.A supercomputer to train and fine-tune powerful foundation and generative AI models.A development platform for robotics simulation and testing.An onboard runtime computer to deploy trained models to physical robots.Only after adequate training in simulated environments can physical robots be commissioned.The NVIDIA DGX platform can serve as the first computing system to train models.NVIDIA Ominverse running on NVIDIA OVX servers functions as the second computer system, providing the development platform and simulation environment for testing, optimizing and debugging physical AI.NVIDIA Jetson Thor robotics computers designed for onboard computing serve as the third runtime computer.Who Uses Robotics Simulation?Today, robot technology and robot simulations boost operations massively across use cases.Global leader in power and thermal technologies Delta Electronics uses simulation to test out its optical inspection algorithms to detect product defects on production lines.Deep tech startup Wandelbots is building a custom simulator by integrating Isaac Sim into its application, making it easy for end users to program robotic work cells in simulation and seamlessly transfer models to a real robot.Boston Dynamics is activating researchers and developers through its reinforcement learning researcher kit.Robotics Company Fourier is simulating real-world conditions to train humanoid robots with the precision and agility needed for close robot-human collaboration.Using NVIDIA Isaac Sim, robotics company Galbot built DexGraspNet, a comprehensive simulated dataset for dexterous robotic grasps containing over 1 million ShadowHand grasps on 5,300+ objects. The dataset can be applied to any dexterous robotic hand to accomplish complex tasks that require fine-motor skills.Using Robotics Simulation for Planning and Control OutcomesIn complex and dymanic industrial settings, robotics simulation is evolving to integrate digital twins, enhancing planning, control and learning outcomes.Developers import computer-aided design models into a robotics simulator to build virtual scenes and employ algorithms to create the robot operating system and enable task and motion planning. While traditional methods involve prescribing control signals, the shift toward machine learning allows robots to learn behaviors through methods like imitation and reinforcement learning, using simulated sensor signals.This evolution continues with digital twins in complex facilities like manufacturing assembly lines, where developers can test and refine real-time AIs entirely in simulation. This approach saves software development time and costs, and reduces downtime by anticipating issues. For instance, using NVIDIA Omniverse, Metropolis and cuOpt, developers can use digital twins to develop, test and refine physical AI in simulation before deploying in industrial infrastructure.High-Fidelity, Physics-Based Simulation BreakthroughsHigh-fidelity, physics-based simulations have supercharged industrial robotics through real-world experimentation in virtual environments.NVIDIA PhysX, integrated into Omniverse and Isaac Sim, empowers roboticists to develop fine- and gross-motor skills for robot manipulators, rigid and soft body dynamics, vehicle dynamics and other critical features that ensure the robot obeys the laws of physics. This includes precise control over actuators and modeling of kinematics, which are essential for accurate robot movements.To close the sim-to-real gap, Isaac Lab offers a high-fidelity, open-source framework for reinforcement learning and imitation learning that facilitates seamless policy transfer from simulated environments to physical robots. With GPU parallelization, Isaac Lab accelerates training and improves performance, making complex tasks more achievable and safe for industrial robots.To learn more about creating a locomotion reinforcement learning policy with Isaac Sim and Isaac Lab, read this developer blog.Teaching Collision-Free Motion for AutonomyIndustrial robot training often occurs in specific settings like factories or fulfillment centers, where simulations help address challenges related to various robot types and chaotic environments. A critical aspect of these simulations is generating collision-free motion in unknown, cluttered environments.Traditional motion planning approaches that attempt to address these challenges can come up short in unknown or dynamic environments. SLAM, or simultaneous localization and mapping, can be used to generate 3D maps of environments with camera images from multiple viewpoints. However, these maps require revisions when objects move and environments are changed.The NVIDIA Robotics research team and the University of Washington introduced Motion Policy Networks (MNets), an end-to-end neural policy that generates real-time, collision-free motion using a single fixed cameras data stream. Trained on over 3 million motion planning problems and 700 million simulated point clouds, MNets navigates unknown real-world environments effectively.While the MNets model applies direct learning for trajectories, the team also developed a point cloud-based collision model called CabiNet, trained on over 650,000 procedurally generated simulated scenes.With the CabiNet model, developers can deploy general-purpose, pick-and-place policies of unknown objects beyond a flat tabletop setup. Training with a large synthetic dataset allowed the model to generalize to out-of-distribution scenes in a real kitchen environment, without needing any real data.How Developers Can Get Started Building Robotic SimulatorsGet started with technical resources, reference applications and other solutions for developing physically accurate simulation pipelines by visiting the NVIDIA Robotics simulation use case page.Robot developers can tap into NVIDIA Isaac Sim, which supports multiple robot training techniques:Synthetic data generation for training perception AI modelsSoftware-in-the-loop testing for the entire robot stackRobot policy training with Isaac LabDevelopers can also pair ROS 2 with Isaac Sim to train, simulate and validate their robot systems. The Isaac Sim to ROS 2 workflow is similar to workflows executed with other robot simulators such as Gazebo. It starts with bringing a robot model into a prebuilt Isaac Sim environment, adding sensors to the robot, and then connecting the relevant components to the ROS 2 action graph and simulating the robot by controlling it through ROS 2 packages.Stay up to date by subscribing to our newsletter and follow NVIDIA Robotics on LinkedIn, Instagram, X and Facebook.

Editors note: This post is part of Into the Omniverse, a blog series focused on how developers, 3D artists and enterprises can transform their workflows using the latest advances in OpenUSD and NVIDIA Omniverse.3D product configurators are changing the way industries like retail and automotive engage with customers by offering interactive, customizable 3D visualizations of products.Using physically accurate product digital twins, even non-3D artists can streamline content creation and generate stunning marketing visuals.With the new NVIDIA Omniverse Blueprint for 3D conditioning for precise visual generative AI, developers can start using the NVIDIA Omniverse platform and Universal Scene Description (OpenUSD) to easily build personalized, on-brand and product-accurate marketing content at scale.By integrating generative AI into product configurators, developers can optimize operations and reduce production costs. With repetitive tasks automated, teams can focus on the creative aspects of their jobs.Developing Controllable Generative AI for Content ProductionThe new Omniverse Blueprint introduces a robust framework for integrating generative AI into 3D workflows to enable precise and controlled asset creation.Example images created using the NVIDIA Omniverse Blueprint for 3D conditioning for precise visual generative AI.Key highlights of the blueprint include:Model conditioning to ensure that the AI-generated visuals adhere to specific brand requirements like colors and logos.Multimodal approach that combines 3D and 2D techniques to offer developers complete control over final visual outputs while ensuring the products digital twin remains accurate.Key components such as an on-brand hero asset, a simple and untextured 3D scene, and a customizable application built with the Omniverse Kit App Template.OpenUSD integration to enhance development of 3D visuals with precise visual generative AI.Integration of NVIDIA NIM, such as the Edify 360 NIM, Edify 3D NIM, USD Code NIM and USD Search NIM microservices, allows the blueprint to be extensible and customizable. The microservices are available to preview on build.nvidia.com.How Developers Are Building AI-Enabled Content PipelinesKatana Studio developed a content creation tool with OpenUSD called COATcreate that empowers marketing teams to rapidly produce 3D content for automotive advertising. By using 3D data prepared by creative experts and vetted by product specialists in OpenUSD, even users with limited artistic experience can quickly create customized, high-fidelity, on-brand content for any region or use case without adding to production costs.Global marketing leader WPP has built a generative AI content engine for brand advertising with OpenUSD. The Omniverse Blueprint for precise visual generative AI helped facilitate the integration of controllable generative AI in its content creation tools. Leading global brands like The Coca-Cola Company are already beginning to adopt tools from WPP to accelerate iteration on its creative campaigns at scale.Watch the replay of a recent livestream with WPP for more on its generative AI- and OpenUSD-enabled workflow:The NVIDIA creative team developed a reference workflow called CineBuilder on Omniverse that allows companies to use text prompts to generate ads personalized to consumers based on region, weather, time of day, lifestyle and aesthetic preferences.Developers at independent software vendors and production services agencies are building content creation solutions infused with controllable generative AI and built on OpenUSD. Accenture Song, Collective World, Grip, Monks and WPP are among those adopting Omniverse Blueprints to accelerate development.Read the tech blog on developing product configurators with OpenUSD and get started developing solutions using the DENZA N7 3D configurator and CineBuilder reference workflow.Get Plugged Into the World of OpenUSDVarious resources are available to help developers get started building AI-enabled product configuration solutions:Omniverse Blueprint: 3D Conditioning for Precise Visual Generative AIReference Architecture: 3D Conditioning for Precise Visual Generative AIReference Architecture: Generative AI Workflow for Content CreationReference Architecture: Product ConfiguratorEnd-to-End Configurator Example GuideDLI Course: Building a 3D Product Configurator With OpenUSDLivestream: OpenUSD for Marketing and AdvertisingFor more on optimizing OpenUSD workflows, explore the new self-paced Learn OpenUSD training curriculum that includes free Deep Learning Institute courses for 3D practitioners and developers. For more resources on OpenUSD, attend our instructor-led Learn OpenUSD courses at SIGGRAPH Asia on December 3, explore the Alliance for OpenUSD forum and visit the AOUSD website.Dont miss the CES keynote delivered by NVIDIA founder and CEO Jensen Huang live in Las Vegas on Monday, Jan. 6, at 6:30 p.m. PT for more on the future of AI and graphics.Stay up to date by subscribing to NVIDIA news, joining the community and following NVIDIA Omniverse on Instagram, LinkedIn, Medium and X.

Editors note: This post is the first in the AI On blog series, which explores the latest techniques and real-world applications of agentic AI, chatbots and copilots. The series will also highlight the NVIDIA software and hardware powering advanced AI agents, which form the foundation of AI query engines that gather insights and perform tasks to transform everyday experiences and reshape industries.Whether its getting a complex service claim resolved or having a simple purchase inquiry answered, customers expect timely, accurate responses to their requests.AI agents can help organizations meet this need. And they can grow in scope and scale as businesses grow, helping keep customers from taking their business elsewhere.AI agents can be used as virtual assistants, which use artificial intelligence and natural language processing to handle high volumes of customer service requests. By automating routine tasks, AI agents ease the workload on human agents, allowing them to focus on tasks requiring a more personal touch.AI-powered customer service tools like chatbots have become table stakes across every industry looking to increase efficiency and keep buyers happy. According to a recent IDC study on conversational AI, 41% of organizations use AI-powered copilots for customer service and 60% have implemented them for IT help desks.Now, many of those same industries are looking to adopt agentic AI, semi-autonomous tools that have the ability to perceive, reason and act on more complex problems.How AI Agents Enhance Customer ServiceA primary value of AI-powered systems is the time they free up by automating routine tasks. AI agents can perform specific tasks, or agentic operations, essentially becoming part of an organizations workforce working alongside humans who can focus on more complex customer issues.AI agents can handle predictive tasks and problem-solve, can be trained to understand industry-specific terms and can pull relevant information from an organizations knowledge bases, wherever that data resides.With AI agents, companies can:Boost efficiency: AI agents handle common questions and repetitive tasks, allowing support teams to prioritize more complicated cases. This is especially useful during high-demand periods.Increase customer satisfaction: Faster, more personalized interactions result in happier and more loyal customers. Consistent and accurate support improves customer sentiment and experience.Scale Easily: Equipped to handle high volumes of customer support requests, AI agents scale effortlessly with growing businesses, reducing customer wait times and resolving issues faster.AI Agents for Customer Service Across IndustriesAI agents are transforming customer service across sectors, helping companies enhance customer conversations, achieve high-resolution rates and improve human representative productivity.For instance, ServiceNow recently introduced IT and customer service management AI agents to boost productivity by autonomously solving many employee and customer issues. Its agents can understand context, create step-by-step resolutions and get live agent approvals when needed.To improve patient care and reduce preprocedure anxiety, The Ottawa Hospital is using AI agents that have consistent, accurate and continuous access to information. The agent has the potential to improve patient care and reduce administrative tasks for doctors and nurses.The city of Amarillo, Texas, uses a multilingual digital assistant named Emma to provide its residents with 24/7 support. Emma brings more effective and efficient disbursement of important information to all residents, including the one-quarter who dont speak English.AI agents meet current customer service demands while preparing organizations for the future.Key Steps for Designing AI Virtual Assistants for Customer SupportAI agents for customer service come in a wide range of designs, from simple text-based virtual assistants that resolve customer issues, to animated avatars that can provide a more human-like experience.Digital human interfaces can add warmth and personality to the customer experience. These agents respond with spoken language and even animated avatars, enhancing service interactions with a touch of real-world flair. A digital human interface lets companies customize the assistants appearance and tone, aligning it with the brands identity.There are three key building blocks to creating an effective AI agent for customer service:Collect and organize customer data: AI agents need a solid base of customer data (such as profiles, past interactions, and transaction histories) to provide accurate, context-aware responses.Use memory functions for personalization: Advanced AI systems remember past interactions, allowing agents to deliver personalized support that feels human.Build an operations pipeline: Customer service teams should regularly review feedback and update the AI agents responses to ensure its always improving and aligned with business goals.Powering AI Agents With NVIDIA NIM MicroservicesNVIDIA NIM microservices power AI agents by enabling natural language processing, contextual retrieval and multilingual communication. This allows AI agents to deliver fast, personalized and accurate support tailored to diverse customer needs.Key NVIDIA NIM microservices for customer service agents include:NVIDIA NIM for Large Language Models Microservices that bring advanced language models to applications and enable complex reasoning, so AI agents can understand complicated customer queries.NVIDIA NeMo Retriever NIM Embedding and reranking microservices that support retrieval-augmented generation pipelines allow virtual assistants to quickly access enterprise knowledge bases and boost retrieval performance by ranking relevant knowledge-base articles and improving context accuracy.NVIDIA NIM for Digital Humans Microservices that enable intelligent, interactive avatars to understand speech and respond in a natural way. NVIDIA Riva NIM microservices for text-to-speech, automatic speech recognition (ASR), and translation services enable AI agents to communicate naturally across languages. The recently released Riva NIM microservices for ASR enable additional multilingual enhancements. To build realistic avatars, Audio2Face NIM converts streamed audio to facial movements for real-time lip syncing. 2D and 3D Audio2Face NIM microservices support varying use cases.Getting Started With AI Agents for Customer ServiceNVIDIA AI Blueprints make it easy to start building and setting up virtual assistants by offering ready-made workflows and tools to accelerate deployment. Whether for a simple AI-powered chatbot or a fully animated digital human interface, the blueprints offer resources to create AI assistants that are scalable, aligned with an organizations brand and deliver a responsive, efficient customer support experience.Editors note: IDC figures are sourced to IDC, Market Analysis Perspective: Worldwide Conversational AI Tools and Technologies, 2024 US51619524, Sept 2024

Starting with the release of CUDA in 2006, NVIDIA has driven advancements in AI and accelerated computing and the most recent TOP500 list of the worlds most powerful supercomputers highlights the culmination of the companys achievements in the field.This year, 384 systems on the TOP500 list are powered by NVIDIA technologies. Among the 53 new to the list, 87% 46 systems are accelerated. Of those accelerated systems, 85% use NVIDIA Hopper GPUs, driving advancements in areas like climate forecasting, drug discovery and quantum simulation.Accelerated computing is much more than floating point operations per second (FLOPS). It requires full-stack, application-specific optimization. At SC24 this week, NVIDIA announced the release of cuPyNumeric, an NVIDIA CUDA-X library that enables over 5 million developers to seamlessly scale to powerful computing clusters without modifying their Python code.NVIDIA also revealed significant updates to the NVIDIA CUDA-Q development platform, which empowers quantum researchers to simulate quantum devices at a scale previously thought computationally impossible.And, NVIDIA received nearly a dozen HPCwire Readers and Editors Choice awards across a variety of categories, marking its 20th consecutive year of recognition.A New Era of Scientific Discovery With Mixed Precision and AIMixed-precision floating-point operations and AI have become the tools of choice for researchers grappling with the complexities of modern science. They offer greater speed, efficiency and adaptability than traditional methods, without compromising accuracy.This shift isnt just theoretical its already happening. At SC24, two Gordon Bell finalist projects revealed how using AI and mixed precision helped advance genomics and protein design.In his paper titled Using Mixed Precision for Genomics, David Keyes, a professor at King Abdullah University of Science and Technology, used 0.8 exaflops of mixed precision to explore relationships between genomes and their generalized genotypes, and then to the prevalence of diseases to which they are subject.Similarly, Arvind Ramanathan, a computational biologist from the Argonne National Laboratory, harnessed 3 exaflops of AI performance on the NVIDIA Grace Hopper-powered Alps system to speed up protein design.To further advance AI-driven drug discovery and the development of lifesaving therapies, researchers can use NVIDIA BioNeMo, powerful tools designed specifically for pharmaceutical applications. Now in open source, the BioNeMo Framework can accelerate AI model creation, customization and deployment for drug discovery and molecular design.Across the TOP500, the widespread use of AI and mixed-precision floating-point operations reflects a global shift in computing priorities. A total of 249 exaflops of AI performance are now available to TOP500 systems, supercharging innovations and discoveries across industries.TOP500 total AI, FP32 and FP64 FLOPs by year.NVIDIA-accelerated TOP500 systems excel across key metrics like AI and mix-precision system performance. With over 190 exaflops of AI performance and 17 exaflops of single-precision (FP32), NVIDIAs accelerated computing platform is the new engine of scientific computing. NVIDIA also delivers 4 exaflops of double-precision (FP64) performance for certain scientific calculations that still require it.Accelerated Computing Is Sustainable ComputingAs the demand for computational capacity grows, so does the need for sustainability.In the Green500 list of the worlds most energy-efficient supercomputers, systems with NVIDIA accelerated computing rank among eight of the top 10. The JEDI system at EuroHPC/FZJ, for example, achieves a staggering 72.7 gigaflops per watt, setting a benchmark for whats possible when performance and sustainability align.For climate forecasting, NVIDIA announced at SC24 two new NVIDIA NIM microservices for NVIDIA Earth-2, a digital twin platform for simulating and visualizing weather and climate conditions. The CorrDiff NIM and FourCastNet NIM microservices can accelerate climate change modeling and simulation results by up to 500x.In a world increasingly conscious of its environmental footprint, NVIDIAs innovations in accelerated computing balance high performance with energy efficiency to help realize a brighter, more sustainable future.Supercomputing Community Embraces NVIDIAThe 11 HPCwire Readers Choice and Editors Choice awards NVIDIA received represent the work of the entire scientific community of engineers, developers, researchers, partners, customers and more.The awards include:Readers Choice: Best AI Product or Technology NVIDIA GH200 Grace Hopper SuperchipReaders Choice: Best HPC Interconnect Product or Technology NVIDIA Quantum-X800Readers Choice: Best HPC Server Product or Technology NVIDIA Grace CPU SuperchipReaders Choice: Top 5 New Products or Technologies to Watch NVIDIA Quantum-X800Readers Choice: Top 5 New Products or Technologies to Watch NVIDIA Spectrum-XReaders and Editors Choice: Top 5 New Products or Technologies to Watch NVIDIA Blackwell GPUEditors Choice: Top 5 New Products or Technologies to Watch NVIDIA CUDA-QReaders Choice: Top 5 Vendors to Watch NVIDIAReaders Choice: Best HPC Response to Societal Plight NVIDIA Earth-2Editors Choice: Best Use of HPC in Energy (one of two named contributors) Real-time simulation of CO2 plume migration in carbon capture and storageReaders Choice Award: Best HPC Collaboration (one of 11 named contributors) National Artificial Intelligence Research Resource PilotWatch the replay of NVIDIAs special address at SC24 and learn more about the companys news in the SC24 online press kit.See notice regarding software product information.

More than 96% of all manufactured goods ranging from everyday products, like laundry detergent and food packaging, to advanced industrial components, such as semiconductors, batteries and solar panels rely on chemicals that cannot be replaced with alternative materials.With AI and the latest technological advancements, researchers and developers are studying ways to create novel materials that could address the worlds toughest challenges, such as energy storage and environmental remediation.Announced today at the Supercomputing 2024 conference in Atlanta, the NVIDIA ALCHEMI NIM microservice accelerates such research by optimizing AI inference for chemical simulations that could lead to more efficient and sustainable materials to support the renewable energy transition.Its one of the many ways NVIDIA is supporting researchers, developers and enterprises to boost energy and resource efficiency in their workflows, including to meet requirements aligned with the global Net Zero Initiative.NVIDIA ALCHEMI for Material and Chemical SimulationsExploring the universe of potential materials, using the nearly infinite combinations of chemicals each with unique characteristics can be extremely complex and time consuming. Novel materials are typically discovered through laborious, trial-and-error synthesis and testing in a traditional lab.Many of todays plastics, for example, are still based on material discoveries made in the mid-1900s.More recently, AI has emerged as a promising accelerant for chemicals and materials innovation.With the new ALCHEMI NIM microservice, researchers can test chemical compounds and material stability in simulation, in a virtual AI lab, which reduces costs, energy consumption and time to discovery.For example, running MACE-MP-0, a pretrained foundation model for materials chemistry, on an NVIDIA H100 Tensor Core GPU, the new NIM microservice speeds evaluations of a potential compositions simulated long-term stability 100x. The below figure shows a 25x speedup from using the NVIDIA Warp Python framework for high-performance simulation, followed by a 4x speedup with in-flight batching. All in all, evaluating 16 million structures would have taken months with the NIM microservice, it can be done in just hours.By letting scientists examine more structures in less time, the NIM microservice can boost research on materials for use with solar and electric batteries, for example, to bolster the renewable energy transition.NVIDIA also plans to release NIM microservices that can be used to simulate the manufacturability of novel materials to determine how they might be brought from test tubes into the real world in the form of batteries, solar panels, fertilizers, pesticides and other essential products that can contribute to a healthier, greener planet.SES AI, a leading developer of lithium-metal batteries, is using the NVIDIA ALCHEMI NIM microservice with the AIMNet2 model to accelerate the identification of electrolyte materials used for electric vehicles.SES AI is dedicated to advancing lithium battery technology through AI-accelerated material discovery, using our Molecular Universe Project to explore and identify promising candidates for lithium metal electrolyte discovery, said Qichao Hu, CEO of SES AI. Using the ALCHEMI NIM microservice with AIMNet2 could drastically improve our ability to map molecular properties, reducing time and costs significantly and accelerating innovation.SES AI recently mapped 100,000 molecules in half a day, with the potential to achieve this in under an hour using ALCHEMI. This signals how the microservice is poised to have a transformative impact on material screening efficiency.Looking ahead, SES AI aims to map the properties of up to 10 billion molecules within the next couple of years, pushing the boundaries of AI-driven, high-throughput discovery.The new microservice will soon be available for researchers to test for free through the NVIDIA NGC catalog be notified of ALCHEMIs launch. It will also be downloadable from build.nvidia.com, and the production-grade NIM microservice will be offered through the NVIDIA AI Enterprise software platform.Learn more about the NVIDIA ALCHEMI NIM microservice, and hear the latest on how AI and supercomputing are supercharging researchers and developers workflows by joining NVIDIA at SC24, running through Friday, Nov. 22.See notice regarding software product information.

To meet demand for Blackwell, now in full production, Foxconn, the worlds largest electronics manufacturer, is using NVIDIA Omniverse. The platform for developing industrial AI simulation applications is helping bring facilities in the U.S., Mexico and Taiwan online faster than ever.Foxconn uses NVIDIA Omniverse to virtually integrate their facility and equipment layouts, NVIDIA Isaac Sim for autonomous robot testing and simulation, and NVIDIA Metropolis for vision AI.Omniverse enables industrial developers to maximize efficiency through test and optimization in a digital twin before deploying costly change-orders to the physical world. Foxconn expects its Mexico facility alone to deliver significant cost savings and a reduction in kilowatt-hour usage of more than 30% annually.Worlds Largest Electronics Maker Plans With Omniverse and AITo meet demands at Foxconn, factory planners are building physical AI-powered robotic factories with Omniverse and NVIDIA AI.The company has built digital twins with Omniverse that allow their teams to virtually integrate facility and equipment information from leading industry applications, such as Siemens Teamcenter X and Autodesk Revit. Floor plan layouts are optimized first in the digital twin, and planners can locate optimal camera positions that help measure and identify ways to streamline operations with Metropolis visual AI agents.In the construction process, the Foxconn teams use the Omniverse digital twin as the source of truth to communicate and validate the accurate layout and placement of equipment.Virtual integration on Omniverse offers significant advantages, potentially saving factory planners millions by reducing costly change orders in real-world operations.Delivering Robotics for Manufacturing With Omniverse Digital TwinOnce the digital twin of the factory is built, it becomes a virtual gym for Foxconns fleets of autonomous robots including industrial manipulators and autonomous mobile robots. Foxconns robot developers can simulate, test and validate their AI robot models in NVIDIA Isaac Sim before deploying to their real world robots.Using Omniverse, Foxconn can simulate robot AIs before deploying to NVIDIA Jetson-driven autonomous mobile robots.On assembly lines, they can simulate with Isaac Manipulator libraries and AI models for automated optical inspection, object identification, defect detection and trajectory planning.Omniverse also enables their facility planners to test and optimize intelligent camera placement before installing in the physical world ensuring they have complete coverage of the factory floor to support worker safety, and provide the foundation for visual AI agent frameworks.Creating Efficiencies While Building Resilient Supply ChainsUsing NVIDIA Omniverse and AI, Foxconn plans to replicate its precision production lines across the world. This will enable it to quickly deploy high-quality production facilities that meet unified standards, increasing the companys competitive edge and adaptability in the market.Foxconns ability to rapidly replicate will accelerate its global deployments and enhance its resilience in the supply chain in the face of disruptions, as it can quickly adjust production strategies and reallocate resources to ensure continuity and stability to meet changing demands.Foxconns Mexico facility will begin production early next year and the Taiwan location will begin production in December.Learn more about Blackwell and Omniverse.

Generative AI-powered laptops and PCs are unlocking advancements in gaming, content creation, productivity and development. Today, over 600 Windows apps and games are already running AI locally on more than 100 million GeForce RTX AI PCs worldwide, delivering fast, reliable and low-latency performance.At Microsoft Ignite, NVIDIA and Microsoft announced tools to help Windows developers quickly build and optimize AI-powered apps on RTX AI PCs, making local AI more accessible. These new tools enable application and game developers to harness powerful RTX GPUs to accelerate complex AI workflows for applications such as AI agents, app assistants and digital humans.RTX AI PCs Power Digital Humans With Multimodal Small Language ModelsMeet James, an interactive digital human knowledgeable about NVIDIA and its products. James uses a collection of NVIDIA NIM microservices, NVIDIA ACE and ElevenLabs digital human technologies to provide natural and immersive responses.NVIDIA ACE is a suite of digital human technologies that brings life to agents, assistants and avatars. To achieve a higher level of understanding so that they can respond with greater context-awareness, digital humans must be able to visually perceive the world like humans do.Enhancing digital human interactions with greater realism demands technology that enables perception and understanding of their surroundings with greater nuance. To achieve this, NVIDIA developed multimodal small language models that can process both text and imagery, excel in role-playing and are optimized for rapid response times.The NVIDIA Nemovision-4B-Instruct model, soon to be available, uses the latest NVIDIA VILA and NVIDIA NeMo framework for distilling, pruning and quantizing to become small enough to perform on RTX GPUs with the accuracy developers need.The model enables digital humans to understand visual imagery in the real world and on the screen to deliver relevant responses. Multimodality serves as the foundation for agentic workflows and offers a sneak peek into a future where digital humans can reason and take action with minimal assistance from a user.NVIDIA is also introducing the Mistral NeMo Minitron 128k Instruct family, a suite of large-context small language models designed for optimized, efficient digital human interactions, coming soon. Available in 8B-, 4B- and 2B-parameter versions, these models offer flexible options for balancing speed, memory usage and accuracy on RTX AI PCs. They can handle large datasets in a single pass, eliminating the need for data segmentation and reassembly. Built in the GGUF format, these models enhance efficiency on low-power devices and support compatibility with multiple programming languages.Turbocharge Gen AI With NVIDIA TensorRT Model Optimizer for WindowsWhen bringing models to PC environments, developers face the challenge of limited memory and compute resources for running AI locally. And they want to make models available to as many people as possible, with minimal accuracy loss.Today, NVIDIA announced updates to NVIDIA TensorRT Model Optimizer (ModelOpt) to offer Windows developers an improved way to optimize models for ONNX Runtime deployment.With the latest updates, TensorRT ModelOpt enables models to be optimized into an ONNX checkpoint for deploying the model within ONNX runtime environments using GPU execution providers such as CUDA, TensorRT and DirectML.TensorRT-ModelOpt includes advanced quantization algorithms, such as INT4-Activation Aware Weight Quantization. Compared to other tools such as Olive, the new method reduces the memory footprint of the model and improves throughput performance on RTX GPUs.During deployment, the models can have up to 2.6x reduced memory footprint compared to FP16 models. This results in faster throughput, with minimal accuracy degradation, allowing them to run on a wider range of PCs.Learn more about how developers on Microsoft systems, from Windows RTX AI PCs to NVIDIA Blackwell-powered Azure servers, are transforming how users interact with AI on a daily basis.

NVIDIA and Microsoft today unveiled product integrations designed to advance full-stack NVIDIA AI development on Microsoft platforms and applications.At Microsoft Ignite, Microsoft announced the launch of the first cloud private preview of the Azure ND GB200 V6 VM series, based on the NVIDIA Blackwell platform. The Azure ND GB200 v6 will be a new AI-optimized virtual machine (VM) series and combines the NVIDIA GB200 NVL72 rack design with NVIDIA Quantum InfiniBand networking.In addition, Microsoft revealed that Azure Container Apps now supports NVIDIA GPUs, enabling simplified and scalable AI deployment. Plus, the NVIDIA AI platform on Azure includes new reference workflows for industrial AI and an NVIDIA Omniverse Blueprint for creating immersive, AI-powered visuals.At Ignite, NVIDIA also announced multimodal small language models (SLMs) for RTX AI PCs and workstations, enhancing digital human interactions and virtual assistants with greater realism.NVIDIA Blackwell Powers Next-Gen AI on Microsoft AzureMicrosofts new Azure ND GB200 V6 VM series will harness the powerful performance of NVIDIA GB200 Grace Blackwell Superchips, coupled with advanced NVIDIA Quantum InfiniBand networking. This offering is optimized for large-scale deep learning workloads to accelerate breakthroughs in natural language processing, computer vision and more.The Blackwell-based VM series complements previously announced Azure AI clusters with ND H200 V5 VMs, which provide increased high-bandwidth memory for improved AI inferencing. The ND H200 V5 VMs are already being used by OpenAI to enhance ChatGPT.Azure Container Apps Enables Serverless AI Inference With NVIDIA Accelerated ComputingServerless computing provides AI application developers increased agility to rapidly deploy, scale and iterate on applications without worrying about underlying infrastructure. This enables them to focus on optimizing models and improving functionality while minimizing operational overhead.The Azure Container Apps serverless containers platform simplifies deploying and managing microservices-based applications by abstracting away the underlying infrastructure.Azure Container Apps now supports NVIDIA-accelerated workloads with serverless GPUs, allowing developers to use the power of accelerated computing for real-time AI inference applications in a flexible, consumption-based, serverless environment. This capability simplifies AI deployments at scale while improving resource efficiency and application performance without the burden of infrastructure management.Serverless GPUs allow development teams to focus more on innovation and less on infrastructure management. With per-second billing and scale-to-zero capabilities, customers pay only for the compute they use, helping ensure resource utilization is both economical and efficient. NVIDIA is also working with Microsoft to bring NVIDIA NIM microservices to serverless NVIDIA GPUs in Azure to optimize AI model performance.NVIDIA Unveils Omniverse Reference Workflows for Advanced 3D ApplicationsNVIDIA announced reference workflows that help developers to build 3D simulation and digital twin applications on NVIDIA Omniverse and Universal Scene Description (OpenUSD) accelerating industrial AI and advancing AI-driven creativity.A reference workflow for 3D remote monitoring of industrial operations is coming soon to enable developers to connect physically accurate 3D models of industrial systems to real-time data from Azure IoT Operations and Power BI.These two Microsoft services integrate with applications built on NVIDIA Omniverse and OpenUSD to provide solutions for industrial IoT use cases. This helps remote operations teams accelerate decision-making and optimize processes in production facilities.The Omniverse Blueprint for precise visual generative AI enables developers to create applications that let nontechnical teams generate AI-enhanced visuals while preserving brand assets. The blueprint supports models like SDXL and Shutterstock Generative 3D to streamline the creation of on-brand, AI-generated images.Leading creative groups, including Accenture Song, Collective, GRIP, Monks and WPP, have adopted this NVIDIA Omniverse Blueprint to personalize and customize imagery across markets.Accelerating Gen AI for Windows With RTX AI PCsNVIDIAs collaboration with Microsoft extends to bringing AI capabilities to personal computing devices.At Ignite, NVIDIA announced its new multimodal SLM, NVIDIA Nemovision-4B Instruct, for understanding visual imagery in the real world and on screen. Its coming soon to RTX AI PCs and workstations and will pave the way for more sophisticated and lifelike digital human interactions.Plus, updates to NVIDIA TensorRT Model Optimizer (ModelOpt) offer Windows developers a path to optimize a model for ONNX Runtime deployment. TensorRT ModelOpt enables developers to create AI models for PCs that are faster and more accurate when accelerated by RTX GPUs. This enables large models to fit within the constraints of PC environments, while making it easy for developers to deploy across the PC ecosystem with ONNX runtimes.RTX AI-enabled PCs and workstations offer enhanced productivity tools, creative applications and immersive experiences powered by local AI processing.Full-Stack Collaboration for AI DevelopmentNVIDIAs extensive ecosystem of partners and developers brings a wealth of AI and high-performance computing options to the Azure platform.SoftServe, a global IT consulting and digital services provider, today announced the availability of SoftServe Gen AI Industrial Assistant, based on the NVIDIA AI Blueprint for multimodal PDF data extraction, on the Azure marketplace. The assistant addresses critical challenges in manufacturing by using AI to enhance equipment maintenance and improve worker productivity.At Ignite, AT&T will showcase how its using NVIDIA AI and Azure to enhance operational efficiency, boost employee productivity and drive business growth through retrieval-augmented generation and autonomous assistants and agents.Learn more about NVIDIA and Microsofts collaboration and sessions at Ignite.See notice regarding software product information.