Lights, camera, render! In Part 3 of our Studio Sessions tutorial series, Aleksandr Eskin takes the next step in his 3D photorealistic dropper scene workflow with initial rendering. Watch now: https://nvda.ws/4iEzlEQ

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Time to sharpen the blade. GeForce NOW brings a legendary addition to the cloud: Ubisofts highly anticipated Assassins Creed Shadows is now available for members to stream.Plus, dive into the updated version of the iconic Fable Anniversary part of 11 games joining the cloud this week.Silent as a ShadowTake the Leap of Faith from the cloud.Explore 16th-century Japan, uncover conspiracies and shape the destiny of a nation all from the cloud.Assassins Creed Shadows unfolds in 1579, during the turbulent Azuchi-Momoyama period of feudal Japan, a time of civil war and cultural exchange.Step into the roles of Naoe, a fictional shinobi assassin and daughter of Fujibayashi Nagato, and Yasuke, a character based on the historical African samurai. Their stories intertwine as they find themselves on opposite sides of a conflict.The games dynamic stealth system enables players to hide in shadows and use a new Observe mechanic to identify targets, tag enemies and highlight objectives. Yasuke and Naoe each have unique abilities and playstyles: Naoe excels in stealth, equipped with classic Assassin techniques and shinobi skills, while Yasuke offers a more combat-focused approach.Navigate the turbulent Sengoku period on GeForce NOW, and experience the games breathtaking landscapes and intense combat at up to 4K resolution and 120 frames per second with an Ultimate membership. Every sword clash and sweeping vista is delivered with exceptional smoothness and clarity.A Classic RebornFable Anniversary revitalizes the original Fable: The Lost Chapters with enhanced graphics, a new save system and Xbox achievements. This action role-playing game invites players to shape their heroes destinies in the whimsical world of Albion.Make every choice from the cloud.Fable Anniversary weaves an epic tale of destiny and choice, following the journey of a young boy whose life is forever changed when bandits raid his peaceful village of Oakvale. Recruited to the Heroes Guild, he embarks on a quest to uncover the truth about his family and confront the mysterious Jack of Blades.Players shape their heros destiny through a series of moral choices. These decisions influence the storys progression and even manifest physically on the character.Stream the title with a GeForce NOW membership across PCs that may not be game-ready, Macs, mobile devices, and Samsung and LG smart TVs. GeForce NOW transforms these devices into powerful gaming rigs, with up to eight-hour gaming sessions for Ultimate members.Unleash the GamesCrash, smash, repeat.Wreckfest 2, the highly anticipated sequel by Bugbear Entertainment to the original demolition derby racing game, promises an even more intense and chaotic experience. The game features a range of customizable cars, from muscle cars to novelty vehicles, each with a story to tell.Play around with multiple modes, including traditional racing with physics-driven handling, and explore demolition derby arenas where the goal is to cause maximum destruction. With enhanced multiplayer features, including skills-based matchmaking and split-screen mode, Wreckfest 2 is the ultimate playground for destruction-racing enthusiasts.Look for the following games available to stream in the cloud this week:Assassins Creed Shadows (New release on Steam and Ubisoft Connect, March 20)Wreckfest 2 (New release on Steam, March 20)Aliens: Dark Descent (Xbox, available on PC Game Pass)Crime Boss: Rockay City (Epic Games Store)Eternal Strands (Xbox, available on PC Game Pass)Fable Anniversary (Steam)Motor Town: Behind the Wheel (Steam)Nine Sols (Xbox, available on PC Game Pass)Quake Live (Steam)Skydrift Infinity (Epic Games Store)To the Rescue! (Epic Games Store)What are you planning to play this weekend? Let us know on X or in the comments below.If you could go on a vacation to any video game realm, where would you go? NVIDIA GeForce NOW (@NVIDIAGFN) March 19, 2025

The power and utilities sector keeps the lights on for the worlds populations and industries. As the global energy landscape evolves, so must the tools it relies on.To advance the next generation of electricity generation and distribution, many of the industrys members are joining forces through the creation of the Open Power AI Consortium. The consortium includes energy companies, technology companies and researchers developing AI applications to tackle domain-specific challenges, such as adapting to an increased deployment of distributed energy resources and significant load growth on electric grids.Led by independent, nonprofit energy R&D organization EPRI, the consortium aims to spur AI adoption in the power sector through a collaborative effort to build open models using curated, industry-specific data. The initiative was launched today at NVIDIA GTC, a global AI conference taking place through Friday, March 21, in San Jose, California.Over the next decade, AI has the great potential to revolutionize the power sector by delivering the capability to enhance grid reliability, optimize asset performance, and enable more efficient energy management, said Arshad Mansoor, EPRIs president and CEO. With the Open Power AI Consortium, EPRI and its collaborators will lead this transformation, driving innovation toward a more resilient and affordable energy future.As part of the consortium, EPRI, NVIDIA and Articul8, a member of the NVIDIA Inception program for cutting-edge startups, are developing a set of domain-specific, multimodal large language models trained on massive libraries of proprietary energy and electrical engineering data from EPRI that can help utilities streamline operations, boost energy efficiency and improve grid resiliency.The first version of an industry-first open AI model for electric and power systems was developed using hundreds of NVIDIA H100 GPUs and is expected to soon be available in early access as an NVIDIA NIM microservice.Working with EPRI, we aim to leverage advanced AI tools to address todays unique industry challenges, positioning us at the forefront of innovation and operational excellence, said Vincent Sorgi, CEO of PPL Corporation and EPRI board chair.PPL is a leading U.S. energy company that provides electricity and natural gas to more than 3.6 million customers in Pennsylvania, Kentucky, Rhode Island and Virginia.The Open AI Consortiums Executive Advisory Committee includes executives from over 20 energy companies such as Duke Energy, Pacific Gas & Electric Company and Portland General Electric, as well as leading tech companies such as AWS, Oracle and Microsoft. The consortium plans to further expand its global member base.Powering Up AI to Energize Operations, Drive InnovationGlobal energy consumption is projected to grow by nearly 4% annually through 2027, according to the International Energy Agency. To support this surge in demand, electricity providers are looking to enhance the resiliency of power infrastructure, balance diverse energy sources and expand the grids capacity.AI agents trained on thousands of documents specific to this sector including academic research, industry regulations and standards, and technical documents can enable utility and energy companies to more quickly assess energy needs and prepare the studies and permits required to improve infrastructure.We can bring AI to the global power sector in a much more accelerated way by working together to develop foundation models for the industry, and collaborating with the power sector to y apply solutions tailored to its unique needs, Mansoor said.Utilities could tap the consortiums model to help accelerate interconnection studies, which analyze the feasibility and potential impact of connecting new generators to the existing electric grid. The process varies by region but can take up to four years to complete. By introducing AI agents that can support the analysis, the consortium aims to cut this timeline down by at least 5x.The AI model could also be used to support the preparation of licenses, permits, environmental studies and utility rate cases, where energy companies seek regulatory approval and public comment on proposed changes to electricity rates.Beyond releasing datasets and models, the consortium also aims to develop a standardized framework of benchmarks to help utilities, researchers and other energy sector stakeholders evaluate the performance and reliability of AI technologies.Learn more about the Open Power AI Consortium online and in EPRIs sessions at GTC:Accelerate Energy Transformation With Industry Domain AI Models Arshad Mansoor, president and CEO of EPRIEnergy Transition: Impact of Generative AI in the Power Ecosystem of Generation, Transmission and Distribution Swati Daji, executive vice president and chief financial, risk and operations officer at EPRITo learn more about advancements in AI across industries, watch the GTC keynote by NVIDIA founder and CEO Jensen Huang:See notice regarding software product information.

The roots of many of NVIDIAs landmark innovations the foundational technology that powers AI, accelerated computing, real-time ray tracing and seamlessly connected data centers can be found in the companys research organization, a global team of around 400 experts in fields including computer architecture, generative AI, graphics and robotics.Established in 2006 and led since 2009 by Bill Dally, former chair of Stanford Universitys computer science department, NVIDIA Research is unique among corporate research organizations set up with a mission to pursue complex technological challenges while having a profound impact on the company and the world.We make a deliberate effort to do great research while being relevant to the company, said Dally, chief scientist and senior vice president of NVIDIA Research. Its easy to do one or the other. Its hard to do both.Dally is among NVIDIA Research leaders sharing the groups innovations at NVIDIA GTC, the premier developer conference at the heart of AI, taking place this week in San Jose, California.We make a deliberate effort to do great research while being relevant to the company. Bill Dally, chief scientist and senior vice presidentWhile many research organizations may describe their mission as pursuing projects with a longer time horizon than those of a product team, NVIDIA researchers seek out projects with a larger risk horizon and a huge potential payoff if they succeed.Our mission is to do the right thing for the company. Its not about building a trophy case of best paper awards or a museum of famous researchers, said David Luebke, vice president of graphics research and NVIDIAs first researcher. We are a small group of people who are privileged to be able to work on ideas that could fail. And so it is incumbent upon us to not waste that opportunity and to do our best on projects that, if they succeed, will make a big difference.Innovating as One TeamOne of NVIDIAs core values is one team a deep commitment to collaboration that helps researchers work closely with product teams and industry stakeholders to transform their ideas into real-world impact.Everybody at NVIDIA is incentivized to figure out how to work together because the accelerated computing work that NVIDIA does requires full-stack optimization, said Bryan Catanzaro, vice president of applied deep learning research at NVIDIA. You cant do that if each piece of technology exists in isolation and everybodys staying in silos. You have to work together as one team to achieve acceleration.When evaluating potential projects, NVIDIA researchers consider whether the challenge is a better fit for a research or product team, whether the work merits publication at a top conference, and whether theres a clear potential benefit to NVIDIA. If they decide to pursue the project, they do so while engaging with key stakeholders.We are a small group of people who are privileged to be able to work on ideas that could fail. And so it is incumbent upon us to not waste that opportunity. David Luebke, vice president of graphics researchWe work with people to make something real, and often, in the process, we discover that the great ideas we had in the lab dont actually work in the real world, Catanzaro said. Its a tight collaboration where the research team needs to be humble enough to learn from the rest of the company what they need to do to make their ideas work.The team shares much of its work through papers, technical conferences and open-source platforms like GitHub and Hugging Face. But its focus remains on industry impact.We think of publishing as a really important side effect of what we do, but its not the point of what we do, Luebke said.NVIDIA Researchs first effort was focused on ray tracing, which after a decade of sustained work led directly to the launch of NVIDIA RTX and redefined real-time computer graphics. The organization now includes teams specializing in chip design, networking, programming systems, large language models, physics-based simulation, climate science, humanoid robotics and self-driving cars and continues expanding to tackle additional areas of study and tap expertise across the globe.You have to work together as one team to achieve acceleration. Bryan Catanzaro, vice president of applied deep learning researchTransforming NVIDIA and the IndustryNVIDIA Research didnt just lay the groundwork for some of the companys most well-known products its innovations have propelled and enabled todays era of AI and accelerated computing.It began with CUDA, a parallel computing software platform and programming model that enables researchers to tap GPU acceleration for myriad applications. Launched in 2006, CUDA made it easy for developers to harness the parallel processing power of GPUs to speed up scientific simulations, gaming applications and the creation of AI models.Developing CUDA was the single most transformative thing for NVIDIA, Luebke said. It happened before we had a formal research group, but it happened because we hired top researchers and had them work with top architects.Making Ray Tracing a RealityOnce NVIDIA Research was founded, its members began working on GPU-accelerated ray tracing, spending years developing the algorithms and the hardware to make it possible. In 2009, the project led by the late Steven Parker, a real-time ray tracing pioneer who was vice president of professional graphics at NVIDIA reached the product stage with the NVIDIA OptiX application framework, detailed in a 2010 SIGGRAPH paper.The researchers work expanded and, in collaboration with NVIDIAs architecture group, eventually led to the development of NVIDIA RTX ray-tracing technology, including RT Cores that enabled real-time ray tracing for gamers and professional creators.Unveiled in 2018, NVIDIA RTX also marked the launch of another NVIDIA Research innovation: NVIDIA DLSS, or Deep Learning Super Sampling. With DLSS, the graphics pipeline no longer needs to draw all the pixels in a video. Instead, it draws a fraction of the pixels and gives an AI pipeline the information needed to create the image in crisp, high resolution.https://blogs.nvidia.com/wp-content/uploads/2025/03/DLSS4.mp4Accelerating AI for Virtually Any ApplicationNVIDIAs research contributions in AI software kicked off with the NVIDIA cuDNN library for GPU-accelerated neural networks, which was developed as a research project when the deep learning field was still in its initial stages then released as a product in 2014.As deep learning soared in popularity and evolved into generative AI, NVIDIA Research was at the forefront exemplified by NVIDIA StyleGAN, a groundbreaking visual generative AI model that demonstrated how neural networks could rapidly generate photorealistic imagery.While generative adversarial networks, or GANs, were first introduced in 2014, StyleGAN was the first model to generate visuals that could completely pass muster as a photograph, Luebke said. It was a watershed moment.NVIDIA StyleGANNVIDIA researchers introduced a slew of popular GAN models such as the AI painting tool GauGAN, which later developed into the NVIDIA Canvas application. And with the rise of diffusion models, neural radiance fields and Gaussian splatting, theyre still advancing visual generative AI including in 3D with recent models like Edify 3D and 3DGUT.NVIDIA GauGANIn the field of large language models, Megatron-LM was an applied research initiative that enabled the efficient training and inference of massive LLMs for language-based tasks such as content generation, translation and conversational AI. Its integrated into the NVIDIA NeMo platform for developing custom generative AI, which also features speech recognition and speech synthesis models that originated in NVIDIA Research.Achieving Breakthroughs in Chip Design, Networking, Quantum and MoreAI and graphics are only some of the fields NVIDIA Research tackles several teams are achieving breakthroughs in chip architecture, electronic design automation, programming systems, quantum computing and more.In 2012, Dally submitted a research proposal to the U.S. Department of Energy for a project that would become NVIDIA NVLink and NVSwitch, the high-speed interconnect that enables rapid communication between GPU and CPU processors in accelerated computing systems.NVLink Switch trayIn 2013, the circuit research team published work on chip-to-chip links that introduced a signaling system co-designed with the interconnect to enable a high-speed, low-area and low-power link between dies. The project eventually became the link between the NVIDIA Grace CPU and NVIDIA Hopper GPU.In 2021, the ASIC and VLSI Research group developed a software-hardware codesign technique for AI accelerators called VS-Quant that enabled many machine learning models to run with 4-bit weights and 4-bit activations at high accuracy. Their work influenced the development of FP4 precision support in the NVIDIA Blackwell architecture.And unveiled this year at the CES trade show was NVIDIA Cosmos, a platform created by NVIDIA Research to accelerate the development of physical AI for next-generation robots and autonomous vehicles. Read the research paper and check out the AI Podcast episode on Cosmos for details.Learn more about NVIDIA Research at GTC. Watch the keynote by NVIDIA founder and CEO Jensen Huang below:See notice regarding software product information.

AI has been shaping the media and entertainment industry for decades, from early recommendation engines to AI-driven editing and visual effects automation. Real-time AI which lets companies actively drive content creation, personalize viewing experiences and rapidly deliver data insights marks the next wave of that transformation.With the NVIDIA RTX PRO Blackwell GPU series, announced yesterday at the NVIDIA GTC global AI conference, media companies can now harness real-time AI for media workflows with unprecedented speed, efficiency and creative potential.NVIDIA Blackwell serves as the foundation of NVIDIA Media2, an initiative that enables real-time AI by bringing together NVIDIA technologies including NVIDIA NIM microservices, NVIDIA AI Blueprints, accelerated computing platforms and generative AI software to transform all aspects of production workflows and experiences, starting with content creation, streaming and live media.Powering Intelligent Content CreationAccelerated computing enables AI-driven workflows to process massive datasets in real time, unlocking faster rendering, simulation and content generation.NVIDIA RTX PRO Blackwell GPUs series include new features that enable unprecedented graphics and AI performance. The NVIDIA Streaming Multiprocessor offers up to 1.5x faster throughput over the NVIDIA Ada generation, and new neural shaders that integrate AI inside of programmable shaders for advanced content creation.Fourth-generation RT Cores deliver up to 2x the performance of the previous generation, enabling the creation of massive photoreal and physically accurate animated scenes. Fifth-generation Tensor Cores deliver up to 4,000 AI trillion operations per second and add support for FP4 precision. And up to 96GB of GDDR7 memory boosts GPU bandwidth and capacity, allowing applications to run faster and work with larger, more complex datasets for massive 3D and AI projects, large-scale virtual-reality environments and more.Elio Disney/PixarOne of the most exciting aspects of new technology is how it empowers our artists with tools to enhance their creative workflows, said Steve May, chief technology officer of Pixar Animation Studios. With Pixars next-generation renderer, RenderMan XPU optimized for the NVIDIA Blackwell platform 99% of Pixar shots can now fit within the 96GB of memory on the NVIDIA RTX PRO 6000 Blackwell GPUs. This breakthrough will fundamentally improve the way we make movies. Lucasfilm Ltd.Our artists were frequently maxing out our 48GB cards with ILM StageCraft environments and having to battle performance issues on set for 6K and 8K real-time renders, said Stephen Hill, principal rendering engineer at Lucasfilm. The new NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition GPU lifts these limitations were seeing upwards of a 2.5x performance increase over our current production GPUs, and with 96GB of VRAM we now have twice as much memory to play with.In addition, neural rendering with NVIDIA RTX Kit brings cinematic-quality ray tracing and AI-enhanced graphics to real-time engines, elevating visual fidelity in film, TV and interactive media. Including neural texture compression, neural shaders, RTX Global Illumination and Mega Geometry, RTX Kit is a suite of neural rendering technologies that enhance graphics for games, animation, virtual production scenes and immersive experiences.Fueling the Future of Streaming and Data AnalyticsData analytics is transforming raw audience insights into actionable intelligence faster than ever. NVIDIA accelerated computing and AI-powered frameworks enable studios to analyze viewer behavior, predict engagement patterns and optimize content in real time, driving hyper-personalized experiences and smarter creative decisions.With the new GPUs, users can achieve real-time ingestion and data transformation with GPU-accelerated data loading and cleansing at scale.The NVIDIA technologies accelerating streaming and data analytics include a suite of NVIDIA CUDA-X data processing libraries that enable immediate insights from continuous data streams and reduce latency, such as:NVIDIA cuML: Enables GPU-accelerated training and inference for recommendation models using scikit-learn algorithms, providing real-time personalization capabilities and up-to-date relevant content recommendations that boost viewer engagement while reducing churn.NVIDIA cuDF: Offers pandas DataFrame operations on GPUs, enabling faster and more efficient NVIDIA-accelerated extract, transform and load operations and analytics. cuDF helps optimize content delivery by analyzing user data to predict demand and adjust content distribution in real time, improving overall user experiences.Along with cuML and cuDF, accelerated data science libraries provide seamless integration with the open-source Dask library for multi-GPU or multi-node clusters. NVIDIA RTX Blackwell PRO GPUs large GPU memory can further assist with handling massive datasets and spikes in usage without sacrificing performance.And, the video search and summarization blueprint integrates vision language models and large language models and provides cloud-native building blocks to build video analytics, search and summarization applications.Breathing Life Into Live MediaWith NVIDIA RTX PRO Blackwell GPUs, broadcasters can achieve higher performance than ever in high-resolution video processing, real-time augmented reality and AI-driven content production and video analytics.New features include:Ninth-Generation NVIDIA NVENC: Adds support for 4:2:2 encoding, accelerating video encoding speed and improving quality for broadcast and live media applications while reducing costs of storing uncompressed video.Sixth-Generation NVIDIA NVDEC: Provides up to double H.264 decoding throughput and offers support for 4:2:2 H.264 and HEVC decode. Professionals can benefit from high-quality video playback, accelerate video data ingestion and use advanced AI-powered video editing features.Fifth-Generation PCIe: Provides double the bandwidth over the previous generation, improving data transfer speeds from CPU memory and unlocking faster performance for data-intensive tasks.DisplayPort 2.1: Drives high-resolution displays at up to 8K at 240Hz and 16K at 60Hz. Increased bandwidth enables seamless multi-monitor setups, while high dynamic range and higher color depth support deliver more precise color accuracy for tasks like video editing and live broadcasting.The NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition GPU is a transformative force in Cosms mission to redefine immersive entertainment, said Devin Poolman, chief product and technology officer at Cosm, a global immersive technology, media and entertainment company. With its unparalleled performance, we can push the boundaries of real-time rendering, unlocking the ultra-high resolution and fluid frame rates needed to make our live, immersive experiences feel nearly indistinguishable from reality.As a key component of Cosms CX System 12K LED dome displays, RTX PRO 6000 Max-Q enables seamless merging of the physical and digital worlds to deliver shared reality experiences, enabling audiences to engage with sports, live events and cinematic content in entirely new ways.Cosms shared reality experience, featuring its 87-foot-diameter LED dome display in stunning 12K resolution, with millions of pixels shining 10x brighter than the brightest cinematic display. Image courtesy of Cosm.To learn more about NVIDIA Media2, watch the GTC keynote and register to attend sessions from NVIDIA and industry leaders at the show, which runs through Friday, March 21.Try NVIDIA NIM microservices and AI Blueprints on build.nvidia.com.

NVIDIA this week recognized 14 partners leading the way across the Americas for their work advancing agentic and physical AI across industries.The 2025 Americas NVIDIA Partner Network awards announced at the GTC 2025 global AI conference represent key efforts by industry leaders to help customers become experts in using AI to solve many of todays greatest challenges. The awards honor the diverse contributions of NPN members fostering AI-driven innovation and growth.This year, NPN introduced three new award categories that reflect how AI is driving economic growth and opportunities, including:Trailblazer, which honors a visionary partner spearheading AI adoption and setting new industry standards.Rising Star, which celebrates an emerging talent helping industries harness AI to drive transformation.Innovation, which recognizes a partner thats demonstrated exceptional creativity and forward thinking.This years NPN ecosystem winners have helped companies across industries use AI to adapt to new challenges and prioritize energy-efficient accelerated computing. NPN partners help customers implement a broad range of AI technologies, including NVIDIA-accelerated AI factories, as well as large language models and generative AI chatbots, to transform business operations.The 2025 NPN award winners for the Americas are:Global Consulting Partner of the Year Accenture is recognized for its impact and depth of engineering with its AI Refinery platform for industries, simulation and robotics, marketing and sovereignty, which helps organizations enhance innovation and growth with custom-built approaches to AI-driven enterprise reinvention.Trailblazer Partner of the Year Advizex is recognized for its commitment to driving innovation in AI and high-performance computing, helping industries like healthcare, manufacturing, retail and government seamlessly integrate advanced AI technologies into existing business frameworks. This enables organizations to achieve significant operations efficiencies, enhanced decision-making, and accelerated digital transformation.Rising Star Partner of the Year AHEAD is recognized for its leadership, technical expertise and deployment of NVIDIA software, NVIDIA DGX systems, NVIDIA HGX and networking technologies to advance AI, benefitting customers across healthcare, financial services, life sciences and higher education.Networking Partner of the Year Computacenter is recognized for advancing high-performance computing and data centers with NVIDIA networking technologies. The company achieved this by using the NVIDIA AI Enterprise software platform, DGX platforms and NVIDIA networking to drive innovation and growth throughout industries with efficient, accelerated data centers.Solution Integration Partner of the Year EXXACT is recognized for its efforts in helping research institutions and businesses tap into generative AI, large language models and high-performance computing. The company harnesses NVIDIA GPUs and networking technologies to deliver powerful computing platforms that accelerate innovation and tackle complex computational challenges across various industries.Enterprise Partner of the Year World Wide Technology (WWT) is recognized for its leadership in advancing AI adoption of customers across industry verticals worldwide. The company expanded its end-to-end AI capabilities by integrating NVIDIA Blueprints into its AI Proving Ground and has made a $500 million commitment to AI development over three years to help speed enterprise generative AI deployments.Software Partner of the Year Mark III is recognized for the work of its cross-functional team spanning data scientists, developers, 3D artists, systems engineers, and HPC and AI architects, as well as its close collaborations with enterprises and institutions, to deploy NVIDIA software, including NVIDIA AI Enterprise and NVIDIA Omniverse, across industries. These efforts have helped many customers build software-powered pipelines and data flywheels with machine learning, generative AI, high-performance computing and digital twins.Higher Education Research Partner of the Year Mark III is recognized for its close engagement with universities, academic institutions and research organizations to cultivate the next generation of leaders across AI, machine learning, generative AI, high-performance computing and digital twins.Healthcare Partner of the Year Lambda is recognized for empowering healthcare and biotech organizations with AI training, fine-tuning and inferencing solutions to speed innovation and drive breakthroughs in AI-driven drug discovery. The company provides AI training, fine-tuning and inferencing solutions at every scale from individual workstations to comprehensive AI factories that help healthcare providers seamlessly integrate NVIDIA accelerated computing and software into their infrastructure.Financial Services Partner of the Year WWT is recognized for driving the digital transformation of the worlds largest banks and financial institutions. The company harnesses NVIDIA AI technologies to optimize data management, enhance cybersecurity and deliver transformative generative AI solutions, helping financial services clients navigate rapid technological changes and evolving customer expectations.Innovation Partner of the Year Cambridge Computer is recognized for supporting customers deploying transformative technologies, including NVIDIA Grace Hopper, NVIDIA Blackwell and the NVIDIA Omniverse platform for physical AI.Service Delivery Partner of the Year SoftServe is recognized for its impact in driving enterprise adoption of NVIDIA AI and Omniverse with custom NVIDIA Blueprints that tap into NVIDIA NIM microservices and NVIDIA NeMo and Riva software. SoftServe helps customers create generative AI services for industries spanning manufacturing, retail, financial services, auto, healthcare and life sciences.Distribution Partner of the Year TD SYNNEX has been recognized for the second consecutive year for supporting customers in accelerating AI growth through rapid delivery of NVIDIA accelerated computing and software, as part of its Destination AI initiative.Rising Star Consulting Partner of the Year Tata Consultancy Services (TCS) is recognized for its growth and commitment to providing industry-specific solutions that help customers adopt AI faster and at scale. Through its recently launched business unit and center of excellence built on NVIDIA AI Enterprise and Omniverse, TCS is poised to accelerate adoption of agentic AI and physical AI solutions to speed innovation for customers worldwide.Canadian Partner of the Year Hypertec is recognized for its advancement of high-performance computing and generative AI across Canada. The company has employed the full-stack NVIDIA platform to accelerate AI for financial services, higher education and research.Public Sector Partner of the Year Government Acquisitions (GAI) is recognized for its rapid AI deployment and robust customer relationships, helping serve the unique needs of the federal government by adding AI to operations to improve public safety and efficiency.Learn more about the NPN program.

Scientists and engineers of all kinds are equipped to solve tough problems a lot faster with NVIDIA CUDA-X libraries powered by NVIDIA GB200 and GH200 superchips.Announced today at the NVIDIA GTC global AI conference, developers can now take advantage of tighter automatic integration and coordination between CPU and GPU resources enabled by CUDA-X working with these latest superchip architectures resulting in up to 11x speedups for computational engineering tools and 5x larger calculations compared with using traditional accelerated computing architectures.This greatly accelerates and improves workflows in engineering simulation, design optimization and more, helping scientists and researchers reach groundbreaking results faster.NVIDIA released CUDA in 2006, opening up a world of applications to the power of accelerated computing. Since then, NVIDIA has built more than 900 domain-specific NVIDIA CUDA-X libraries and AI models, making it easier to adopt accelerated computing and driving incredible scientific breakthroughs. Now, CUDA-X brings accelerated computing to a broad new set of engineering disciplines, including astronomy, particle physics, quantum physics, automotive, aerospace and semiconductor design.The NVIDIA Grace CPU architecture delivers a significant boost to memory bandwidth while reducing power consumption. And NVIDIA NVLink-C2C interconnects provide such high bandwidth that the GPU and CPU can share memory, allowing developers to write less-specialized code, run larger problems and improve application performance.Accelerating Engineering Solvers With NVIDIA cuDSSNVIDIAs superchip architectures allow users to extract greater performance from the same underlying GPU by making more efficient use of CPU and GPU processing capabilities.The NVIDIA cuDSS library is used to solve large engineering simulation problems involving sparse matrices for applications such as design optimization, electromagnetic simulation workflows and more. cuDSS uses Grace GPU memory and the high-bandwidth NVLink-C2C interconnect to factorize and solve large matrices that normally wouldnt fit in device memory. This enables users to solve extremely large problems in a fraction of the time.The coherent shared memory between the GPU and Grace GPU minimizes data movement, significantly reducing overhead for large systems. For a range of large computational engineering problems, tapping the Grace CPU memory and superchip architecture accelerated the most heavy-duty solution steps by up to 4x with the same GPU, with cuDSS hybrid memory.Ansys has integrated cuDSS into its HFSS solver, delivering significant performance enhancements for electromagnetic simulations. With cuDSS, HFSS software achieves up to an 11x speed improvement for the matrix solver.Altair OptiStruct has also adopted the cuDSS Direct Sparse Solver library, substantially accelerating its finite element analysis workloads.These performance gains are achieved by optimizing key operations on the GPU while intelligently using CPUs for shared memory and heterogeneous CPU and GPU execution. cuDSS automatically detects areas where CPU utilization provides additional benefits, further enhancing efficiency.Scaling Up at Warp Speed With Superchip MemoryScaling memory-limited applications on a single GPU becomes possible with the GB200 and GH200 architectures NVLink-CNC interconnects that provide CPU and GPU memory coherency.Many engineering simulations are limited by scale and require massive simulations to produce the resolution necessary to design equipment with intricate components, such as aircraft engines. By tapping into the ability to seamlessly read and write between CPU and GPU memories, engineers can easily implement out-of-core solvers to process larger data.For example, using NVIDIA Warp a Python-based framework for accelerating data generation and spatial computing applications Autodesk performed simulations of up to 48 billion cells using eight GH200 nodes. This is more than 5x larger than the simulations possible using eight NVIDIA H100 nodes.Powering Quantum Computing Research With NVIDIA cuQuantumQuantum computers promise to accelerate problems that are core to many science and industry disciplines. Shortening the time to useful quantum computing rests heavily on the ability to simulate extremely complex quantum systems.Simulations allow researchers to develop new algorithms today that will run at scales suitable for tomorrows quantum computers. They also play a key role in improving quantum processors, running complex simulations of performance and noise characteristics of new qubit designs.So-called state vector simulations of quantum algorithms require matrix operations to be performed on exponentially large vector objects that must be stored in memory. Tensor network simulations, on the other hand, simulate quantum algorithms through tensor contractions and can enable hundreds or thousands of qubits to be simulated for certain important classes of applications.The NVIDIA cuQuantum library accelerates these workloads. cuQuantum is integrated with every leading quantum computing framework, so all quantum researchers can tap into simulation performance with no code changes.Simulations of quantum algorithms are generally limited in scale by memory requirements. The GB200 and GH200 architectures provide an ideal platform for scaling up quantum simulations, as they enable large CPU memory to be used without bottlenecking performance. A GH200 system is up to 3x faster than an H100 system with x86 on quantum computing benchmarks.Learn more about CUDA-X libraries, attend the GTC session on how math libraries can help accelerate applications on NVIDIA Blackwell GPUs and watch NVIDIA founder and CEO Jensen Huangs GTC keynote.

The first NVIDIA Blackwell-powered data center GPU built for both enterprise AI and visual computing the NVIDIA RTX PRO 6000 Blackwell Server Edition is designed to accelerate the most demanding AI and graphics applications for every industry.Compared to the previous-generation NVIDIA Ada Lovelace architecture L40S GPU, the RTX PRO 6000 Blackwell Server Edition GPU will deliver a multifold increase in performance across a wide array of enterprise workloads up to 5x higher large language model (LLM) inference throughput for agentic AI applications, nearly 7x faster genomics sequencing, 3.3x speedups for text-to-video generation, nearly 2x faster inference for recommender systems and over 2x speedups for rendering.Its part of the NVIDIA RTX PRO Blackwell series of workstation and server GPUs announced today at NVIDIA GTC, the global AI conference taking place through Friday, March 21, in San Jose, California. The RTX PRO lineup includes desktop, laptop and data center GPUs that support AI and creative workloads across industries.With the RTX PRO 6000 Blackwell Server Edition, enterprises across various sectors including architecture, automotive, cloud services, financial services, game development, healthcare, manufacturing, media and entertainment and retail can enable breakthrough performance for workloads such as multimodal generative AI, data analytics, engineering simulation, and visual computing.Content creation, semiconductor manufacturing and genomics analysis companies are already set to harness its capabilities to accelerate compute-intensive, AI-enabled workflows.Universal GPU Delivers Powerful Capabilities for AI and GraphicsThe RTX PRO 6000 Blackwell Server Edition packages powerful RTX AI and graphics capabilities in a passively cooled form factor designed to run 24/7 in data center environments. With 96GB of ultrafast GDDR7 memory and support for Multi-Instance GPU, or MIG, each RTX PRO 6000 can be partitioned into as many as four fully isolated instances with 24GB each to run simultaneous AI and graphics workloads.RTX PRO 6000 is the first universal GPU to enable secure AI with NVIDIA Confidential Computing, which protects AI models and sensitive data from unauthorized access with strong, hardware-based security providing a physically isolated trusted execution environment to secure the entire workload while data is in use.To support enterprise-scale deployments, the RTX PRO 6000 can be configured in high-density accelerated computing platforms for distributed inference workloads or used to deliver virtual workstations with NVIDIA vGPU software to power AI development and graphics-intensive applications.The RTX PRO 6000 GPU delivers supercharged inferencing performance across a broad range of AI models and accelerates real-time, photorealistic ray tracing of complex virtual environments. It includes the latest Blackwell hardware and software innovations like fifth-generation Tensor Cores, fourth-generation RT Cores, DLSS 4, a fully integrated media pipeline and second-generation Transformer Engine with support for FP4 precision.Enterprises can run the NVIDIA Omniverse and NVIDIA AI Enterprise platforms at scale on RTX PRO 6000 Blackwell Server Edition GPUs to accelerate the development and deployment of agentic and physical AI applications, such as image and video generation, LLM inference, recommender systems, computer vision, digital twins and robotics simulation.Accelerated AI Inference and Visual Computing for Any IndustryBlack Forest Labs, creator of the popular FLUX image generation AI, aims to develop and optimize state-of-the-art text-to-image models using RTX PRO 6000 Server Edition GPUs.With the powerful multimodal inference capabilities of the RTX PRO 6000 Server Edition, our customers will be able to significantly reduce latency for image generation workflows, said Robin Rombach, CEO of Black Forest Labs. We anticipate that, with the server edition GPUs support for FP4 precision, our Flux models will run faster, enabling interactive, AI-accelerated content creation.Cloud graphics company OTOY will optimize its OctaneRender real-time rendering application for NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs.The new NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs unlock brand-new workflows that were previously out of reach for 3D content creators, said Jules Urbach, CEO of OTOY and founder of the Render Network. With 96 GB of VRAM, the new server-edition GPUs can run complex neural rendering models within OctaneRenders GPU path-tracer, enabling artists to tap into incredible new features and tools that blend the precision of traditional CGI augmented with frontier generative AI technology.Semiconductor equipment manufacturer KLA plans to use the RTX PRO 6000 Blackwell Server Edition to accelerate inference workloads powering the wafer manufacturing process the creation of thin discs of semiconductor materials that are core to integrated circuits.KLA and NVIDIA have worked together since 2008 to advance KLAs physics-based AI with optimized high-performance computing solutions. KLAs industry-leading inspection and metrology systems capture and process images by running complex AI algorithms at lightning-fast speeds to find the most critical semiconductor defects.Based on early results, we expect great performance from the RTX PRO 6000 Blackwell Server Edition, said Kris Bhaskar, senior fellow and vice president of AI initiatives at KLA. The increased memory capacity, FP4 reduced precision and new computational capabilities of NVIDIA Blackwell are going to be particularly helpful to KLA and its customers.Boosting Genomics and Drug Discovery WorkloadsThe RTX PRO 6000 Blackwell Server Edition also demonstrates game-changing acceleration for genomic analysis and drug discovery inference workloads, enabled by a new class of dynamic programming instructions.On a single RTX PRO 6000 Blackwell Server Edition GPU, Fastq2bam and DeepVariant elements of the NVIDIA Parabricks pipeline for germline analysis run up to 1.5x faster compared with using an L40S GPU, and 1.75x faster compared with using an NVIDIA H100 GPU.For Smith-Waterman, a core algorithm used in many sequence alignment and variant calling applications, RTX PRO 6000 Blackwell Server Edition GPUs accelerate throughput up to 6.8x compared with L40S GPUs.And for OpenFold2, an AI model that predicts protein structures for drug discovery research, RTX PRO 6000 Blackwell Server Edition GPUs boost inference performance by up to 4.8x compared with L40S GPUs.Genomics company Oxford Nanopore Technologies is collaborating with NVIDIA to bring the latest AI and accelerated computing technologies to its sequencing systems.The NVIDIA Blackwell architecture will help us drive the real-time sequencing analysis of anything, by anyone, anywhere, said Chris Seymour, vice president of advanced platform development at Oxford Nanopore Technologies. With the RTX PRO 6000 Blackwell Server Edition, we have seen up to a 2x improvement in basecalling speed across our Dorado platform.Availability via Global Network of Cloud Providers and System PartnersPlatforms featuring the RTX PRO 6000 Blackwell Server Edition will be available from a global ecosystem of partners starting in May.AWS, Google Cloud, Microsoft Azure, IBM Cloud, CoreWeave, Crusoe, Lambda, Nebius and Vultr will be among the first cloud service providers and GPU cloud providers to offer instances featuring the RTX PRO 6000 Blackwell Server Edition.Cisco, Dell Technologies, Hewlett Packard Enterprise, Lenovo and Supermicro are expected to deliver a wide range of servers featuring the RTX PRO 6000 Blackwell Server Edition, as are Advantech, Aetina, Aivres, ASRockRack, ASUS, Compal, Foxconn, GIGABYTE, Inventec, MSI, Pegatron, Quanta Cloud Technology (QCT), MiTAC Computing, NationGate, Wistron and Wiwynn.To learn more about the NVIDIA RTX PRO Blackwell series and other advancements in AI, watch the GTC keynote by NVIDIA founder and CEO Jensen Huang:

The industrial age was fueled by steam. The digital age brought a shift through software. Now, the AI age is marked by the development of generative AI, agentic AI and AI reasoning, which enables models to process more data to learn and reason to solve complex problems.Just as industrial factories transform raw materials into goods, modern businesses require AI factories to quickly transform data into insights that are scalable, accurate and reliable.Orchestrating this new infrastructure is far more complex than it was to build steam-powered factories. State-of-the-art models demand supercomputing-scale resources. Any downtime risks derailing weeks of progress and reducing GPU utilization.To enable enterprises and developers to manage and run AI factories at light speed, NVIDIA today announced at the NVIDIA GTC global AI conference NVIDIA Mission Control the only unified operations and orchestration software platform that automates the complex management of AI data centers and workloads.NVIDIA Mission Control enhances every aspect of AI factory operations. From configuring deployments to validating infrastructure to operating developer workloads, its capabilities help enterprises get frontier models up and running faster.It is designed to easily transition NVIDIA Blackwell-based systems from pretraining to post-training and now test-time scaling with speed and efficiency. The software enables enterprises to easily pivot between training and inference workloads on their Blackwell-based NVIDIA DGX systems and NVIDIA Grace Blackwell systems, dynamically reallocating cluster resources to match shifting priorities.In addition, Mission Control includes NVIDIA Run:ai technology to streamline operations and job orchestration for development, training and inference, boosting infrastructure utilization by up to 5x.Mission Controls autonomous recovery capabilities, supported by rapid checkpointing and automated tiered restart features, can deliver up to 10x faster job recovery compared with traditional methods that rely on manual intervention, boosting AI training and inference efficiency to keep AI applications in operation.Built on decades of NVIDIA supercomputing expertise, Mission Control lets enterprises simply run models by minimizing time spent managing AI infrastructure. It automates the lifecycle of AI factory infrastructure for all NVIDIA Blackwell-based NVIDIA DGX systems and NVIDIA Grace Blackwell systems from Dell Technologies, Hewlett Packard Enterprise (HPE), Lenovo and Supermicro to make advanced AI infrastructure more accessible to the worlds industries.Enterprises can further simplify and speed deployments of NVIDIA DGX GB300 and DGX B300 systems by using Mission Control with the NVIDIA Instant AI Factory service preconfigured in Equinix AI-ready data centers across 45 markets globally.Advanced Software Provides Enterprises Uninterrupted Infrastructure OversightMission Control automates end-to-end infrastructure management including provisioning, monitoring and error diagnosis to deliver uninterrupted operations. Plus, it continuously monitors every layer of the application and infrastructure stack to predict and identify sources of downtime and inefficiency saving time, energy and costs.Additional NVIDIA Mission Control software benefits include:Simplified cluster setup and provisioning with new automation and standardized application programming interfaces to speed time to deployment with integrated inventory management and visualizations.Seamless workload orchestration for simplified Slurm and Kubernetes workflows.Energy-optimized power profiles to balance power requirements and tune GPU performance for various workload types with developer-selectable controls.Autonomous job recovery to identify, isolate and recover from inefficiencies without manual intervention to maximize developer productivity and infrastructure resiliency.Customizable dashboards that track key performance indicators with access to critical telemetry data about clusters.On-demand health checks to validate hardware and cluster performance throughout the infrastructure lifecycle.Building management integration for enhanced coordination with building management systems to provide more control for power and cooling events, including rapid leakage detection.Leading System Makers Bring NVIDIA Mission Control to Grace Blackwell ServersLeading system makers plan to offer NVIDIA GB200 NVL72 and GB300 NVL72 systems with NVIDIA Mission Control.Dell plans to offer NVIDIA Mission Control software as part of the Dell AI Factory with NVIDIA.The AI industrial revolution demands efficient infrastructure that adapts as fast as business evolves, and the Dell AI Factory with NVIDIA delivers with comprehensive compute, networking, storage and support, said Ihab Tarazi, chief technology officer and senior vice president at Dell Technologies. Pairing NVIDIA Mission Control software and Dell PowerEdge XE9712 and XE9680 servers helps enterprises scale models effortlessly to meet the demands of both training and inference, turning data into actionable insights faster than ever before.HPE will offer the NVIDIA GB200 NVL72 by HPE and GB300 NVL72 by HPE systems with NVIDIA Mission Control software.We are helping service providers and cutting-edge enterprises to rapidly deploy, scale, and optimize complex AI clusters capable of training trillion parameter models, said Trish Damkroger, senior vice president and general manager, HPC & AI Infrastructure Solutions at HPE. As part of our collaboration with NVIDIA, we will deliver NVIDIA Grace Blackwell rack-scale systems and Mission Control software with HPEs global services and direct liquid cooling expertise to power the new AI era.Lenovo plans to update its Lenovo Hybrid AI Advantage with NVIDIA systems to include NVIDIA Mission Control software.Bringing NVIDIA Mission Control software to Lenovo Hybrid AI Advantage with NVIDIA systems empowers enterprises to navigate the demands of generative and agentic AI workloads with unmatched agility, said Brian Connors, worldwide vice president and general manager of enterprise and SMB segment and AI, infrastructure solutions group, at Lenovo. By automating infrastructure orchestration and enabling seamless transitions between training and inference workloads, Lenovo and NVIDIA are helping customers scale AI innovation at the speed of business.Supermicro plans to incorporate NVIDIA Mission Control software into its Supercluster systems.Supermicro is proud to team with NVIDIA on a Grace Blackwell NVL72 system that is fully supported by NVIDIA Mission Control software, Cenly Chen, chief growth officer at Supermicro. Running on Supermicros AI SuperCluster systems with NVIDIA Grace Blackwell, NVIDIA Mission Control software provides customers with a seamless management software suite to maximize performance on both current NVIDIA GB200 NVL72 systems and future platforms such as NVIDIA GB300 NVL72.Base Command Manager Offers Free Kickstart for AI Cluster ManagementTo help enterprises with infrastructure management, NVIDIA Base Command Manager software is expected to soon be available for free for up to eight accelerators per system, for any cluster size, with the option to purchase NVIDIA Enterprise Support separately.AvailabilityNVIDIA Mission Control for NVIDIA DGX GB200 and DGX B200 systems is available now. NVIDIA GB200 NVL72 systems with Mission Control are expected to soon be available from Dell, HPE, LeNewfonovo and Supermicro.NVIDIA Mission Control is expected to become available for the latest NVIDIA DGX GB300 and DGX B300 systems, as well as GB300 NVL72 systems from leading global providers, later this year.See notice regarding software product information.

Teaching autonomous robots and vehicles how to interact with the physical world requires vast amounts of high-quality data. To give researchers and developers a head start, NVIDIA is releasing a massive, open-source dataset for building the next generation of physical AI.Announced at NVIDIA GTC, a global AI conference taking place this week in San Jose, California, this commercial-grade, pre-validated dataset can help researchers and developers kickstart physical AI projects that can be prohibitively difficult to start from scratch. Developers can either directly use the dataset for model pretraining, testing and validation or use it during post-training to fine-tune world foundation models, accelerating the path to deployment.The initial dataset is now available on Hugging Face, offering developers 15 terabytes of data representing more than 320,000 trajectories for robotics training, plus up to 1,000 Universal Scene Description (OpenUSD) assets, including a SimReady collection. Dedicated data to support end-to-end autonomous vehicle (AV) development which will include 20-second clips of diverse traffic scenarios spanning over 1,000 cities across the U.S. and two dozen European countries is coming soon.The NVIDIA Physical AI Dataset includes hundreds of SimReady assets for rich scenario building.This dataset will grow over time to become the worlds largest unified and open dataset for physical AI development. It could be applied to develop AI models to power robots that safely maneuver warehouse environments, humanoid robots that support surgeons during procedures and AVs that can navigate complex traffic scenarios like construction zones.The NVIDIA Physical AI Dataset is slated to contain a subset of the real-world and synthetic data NVIDIA uses to train, test and validate physical AI for the NVIDIA Cosmos world model development platform, the NVIDIA DRIVE AV software stack, the NVIDIA Isaac AI robot development platform and the NVIDIA Metropolis application framework for smart cities.Early adopters include the Berkeley DeepDrive Center at the University of California, Berkeley, the Carnegie Mellon Safe AI Lab and the Contextual Robotics Institute at University of California, San Diego.We can do a lot of things with this dataset, such as training predictive AI models that help autonomous vehicles better track the movements of vulnerable road users like pedestrians to improve safety, said Henrik Christensen, director of multiple robotics and autonomous vehicle labs at UCSD. A dataset that provides a diverse set of environments and longer clips than existing open-source resources will be tremendously helpful to advance robotics and AV research.Addressing the Need for Physical AI DataThe NVIDIA Physical AI Dataset can help developers scale AI performance during pretraining, where more data helps build a more robust model and during post-training, where an AI model is trained on additional data to improve its performance for a specific use case.Collecting, curating and annotating a dataset that covers diverse scenarios and accurately represents the physics and variation of the real world is time-consuming, presenting a bottleneck for most developers. For academic researchers and small enterprises, running a fleet of vehicles over months to gather data for autonomous vehicle AI is impractical and costly and, since much of the footage collected is uneventful, typically just 10% of data is used for training.But this scale of data collection is essential to building safe, accurate, commercial-grade models. NVIDIA Isaac GR00T robotics models take thousands of hours of video clips for post-training the GR00T N1 model, for example, was trained on an expansive humanoid dataset of real and synthetic data. The NVIDIA DRIVE AV end-to-end AI model for autonomous vehicles requires tens of thousands of hours of driving data to develop.https://blogs.nvidia.com/wp-content/uploads/2025/03/rgb_5sec-1.mp4This open dataset, comprising thousands of hours of multicamera video at unprecedented diversity, scale and geography will particularly benefit the field of safety research by enabling new work on identifying outliers and assessing model generalization performance. The effort contributes to NVIDIA Halos full-stack AV safety system.In addition to harnessing the NVIDIA Physical AI Dataset to help meet their data needs, developers can further boost AI development with tools like NVIDIA NeMo Curator, which process vast datasets efficiently for model training and customization. Using NeMo Curator, 20 million hours of video can be processed in just two weeks on NVIDIA Blackwell GPUs, compared with 3.4 years on unoptimized CPU pipelines.Robotics developers can also tap the new NVIDIA Isaac GR00T blueprint for synthetic manipulation motion generation, a reference workflow built on NVIDIA Omniverse and NVIDIA Cosmos that uses a small number of human demonstrations to create massive amounts of synthetic motion trajectories for robot manipulation.University Labs Set to Adopt Dataset for AI DevelopmentThe robotics labs at UCSD include teams focused on medical applications, humanoids and in-home assistive technology. Christensen anticipates that the Physical AI Datasets robotics data could help develop semantic AI models that understand the context of spaces like homes, hotel rooms and hospitals.One of our goals is to achieve a level of understanding where, if a robot was asked to put your groceries away, it would know exactly which items should go in the fridge and what goes in the pantry, he said.In the field of autonomous vehicles, Christensens lab could apply the dataset to train AI models to understand the intention of various road users and predict the best action to take. His research teams could also use the dataset to support the development of digital twins that simulate edge cases and challenging weather conditions. These simulations could be used to train and test autonomous driving models in situations that are rare in real-world environments.At Berkeley DeepDrive, a leading research center on AI for autonomous systems, the dataset could support the development of policy models and world foundation models for autonomous vehicles.Data diversity is incredibly important to train foundation models, said Wei Zhan, codirector of Berkeley DeepDrive. This dataset could support state-of-the-art research for public and private sector teams developing AI models for autonomous vehicles and robotics.Researchers at Carnegie Mellon Universitys Safe AI Lab plan to use the dataset to advance their work evaluating and certifying the safety of self-driving cars. The team plans to test how a physical AI foundation model trained on this dataset performs in a simulation environment with rare conditions and compare its performance to an AV model trained on existing datasets.This dataset covers different types of roads and geographies, different infrastructure, different weather environments, said Ding Zhao, associate professor at CMU and head of the Safe AI Lab. Its diversity could be quite valuable in helping us train a model with causal reasoning capabilities in the physical world that understands edge cases and long-tail problems.Access the NVIDIA Physical AI dataset on Hugging Face. Build foundational knowledge with courses such as the Learn OpenUSD learning path and Robotics Fundamentals learning path. And to learn more about the latest advancements in physical AI, watch the GTC keynote by NVIDIA founder and CEO Jensen Huang.See notice regarding software product information.

AI agents are the new digital workforce, transforming business operations, automating complex tasks and unlocking new efficiencies. Now, with the ability to collaborate, these agents can work together to solve complex problems and drive even greater impact.Businesses across industries, including sports and finance, can more quickly harness these benefits with AI-Q a new NVIDIA Blueprint for developing agentic systems that can use reasoning to unlock knowledge in enterprise data.Smarter Agentic AI Systems With NVIDIA AI-Q and AgentIQ ToolkitAI-Q provides an easy-to-follow reference for integrating NVIDIA accelerated computing, partner storage platforms, and software and tools including the new NVIDIA Llama Nemotron reasoning models. AI-Q offers a powerful foundation for enterprises to build digital workforces that break down agentic silos and are capable of handling complex tasks with high accuracy and speed.AI-Q integrates fast multimodal extraction and world-class retrieval, using NVIDIA NeMo Retriever, NVIDIA NIM microservices and AI agents.The blueprint is powered by the new NVIDIA AgentIQ toolkit for seamless, heterogeneous connectivity between agents, tools and data. Released today on GitHub, AgentIQ is an open-source software library for connecting, profiling and optimizing teams of AI agents fueled by enterprise data to create multi-agent, end-to-end systems. It can be easily integrated with existing multi-agent systems either in parts or as a complete solution with a simple onboarding process thats 100% opt-in.The AgentIQ toolkit also enhances transparency with full system traceability and profiling enabling organizations to monitor performance, identify inefficiencies and gain fine-grained understanding of how business intelligence is generated. This profiling data can be used with NVIDIA NIM and the NVIDIA Dynamo open-source library to optimize the performance of agentic systems.The New Enterprise AI Agent WorkforceAs AI agents become digital employees, IT teams will support onboarding and training. The AI-Q blueprint and AgentIQ toolkit support digital employees by enabling collaboration between agents and optimizing performance across different agentic frameworks.Enterprises using these tools will be able to more easily connect AI agent teams across solutions like Salesforces Agentforce, Atlassian Rovo in Confluence and Jira, and the ServiceNow AI platform for business transformation to break down silos, streamline tasks and cut response times from days to hours.AgentIQ also integrates with frameworks and tools like CrewAI, LangGraph, Llama Stack, Microsoft Azure AI Agent Service and Letta, letting developers work in their preferred environment.Azure AI Agent Service is integrated with AgentIQ to enable more efficient AI agents and orchestration of multi-agent frameworks using Semantic Kernel, which is fully supported in AgentIQ.A wide range of industries are integrating visual perception and interactive capabilities into their agents and copilots.Financial services leader Visa is using AI agents to streamline cybersecurity, automating phishing email analysis at scale. Using the profiler feature of AI-Q, Visa can optimize agent performance and costs, maximizing AIs role in efficient threat response.Get Started With AI-Q and AgentIQAI-Q integration into the NVIDIA Metropolis VSS blueprint is enabling multimodal agents, combining visual perception with speech, translation and data analytics for enhanced intelligence.Developers can use the AgentIQ toolkit open-source library today and sign up for this hackathon to build hands-on skills for advancing agentic systems.Plus, learn how an NVIDIA solutions architect used the AgentIQ toolkit to improve AI code generation.Agentic systems built with AI-Q require a powerful AI data platform. NVIDIA partners are delivering these customized platforms that continuously process data to let AI agents quickly access knowledge to reason and respond to complex queries.See notice regarding software product information.

The autonomous vehicle (AV) revolution is here and NVIDIA is at its forefront, bringing more than two decades of automotive computing, software and safety expertise to power innovation from the cloud to the car.At NVIDIA GTC, a global AI conference taking place this week in San Jose, California, dozens of transportation leaders are showcasing their latest advancements with NVIDIA technologies that span passenger cars, trucks, commercial vehicles and more.Mobility leaders are increasingly turning to NVIDIAs three core accelerated compute platforms: NVIDIA DGX systems for training the AI-based stack in the data center, NVIDIA Omniverse and NVIDIA Cosmos running on NVIDIA OVX systems for simulation and synthetic data generation, and the NVIDIA DRIVE AGX in-vehicle computer to process real-time sensor data for safe, highly automated and autonomous driving capabilities.For manufacturers and developers in the multitrillion-dollar auto industry, this unlocks new possibilities for designing, manufacturing and deploying functionally safe, intelligent mobility solutions offering consumers safer, smarter and more enjoyable experiences.Transforming Passenger VehiclesThe U.S.s largest automaker, General Motors (GM), is collaborating with NVIDIA to develop and build its next-generation vehicles, factories and robots using NVIDIAs accelerated compute platforms. GM has been investing in NVIDIA GPU platforms for training AI models.The companies collaboration now expands to include optimizing factory planning using Omnivese with Cosmos and deploying next-generation vehicles at scale accelerated by the NVIDIA DRIVE AGX. This will help GM build physical AI systems tailored to its company vision, craft and know-how, and ultimately enable mobility thats safer, smarter and more accessible than ever.Volvo Cars, which is using the NVIDIA DRIVE AGX in-vehicle computer in its next-generation electric vehicles, and its subsidiary Zenseact use the NVIDIA DGX platform to analyze and contextualize sensor data, unlock new insights and train future safety models that will enhance overall vehicle performance and safety.Lenovo has teamed with robotics company Nuro to create a robust end-to-end system for level 4 autonomous vehicles that prioritize safety, reliability and convenience. The system is built on NVIDIA DRIVE AGX in-vehicle compute.Advancements in TruckingNVIDIAs AI-driven technologies are also supercharging trucking, helping address pressing challenges like driver shortages, rising e-commerce demands and high operational costs. NVIDIA DRIVE AGX delivers the computational muscle needed for safe, reliable and efficient autonomous operations improving road safety and logistics on a massive scale.Gatik is integrating DRIVE AGX for the onboard AI processing necessary for its freight-only class 6 and 7 trucks, manufactured by Isuzu Motors, which offer driverless middle-mile delivery of a wide range of goods to Fortune 500 customers including Tyson Foods, Kroger and Loblaw.Uber Freight is also adopting DRIVE AGX as the AI computing backbone of its current and future carrier fleets, sustainably enhancing efficiency and saving costs for shippers.Torc is developing a scalable, physical AI compute system for autonomous trucks. The system uses NVIDIA DRIVE AGX in-vehicle compute and the NVIDIA DriveOS operating system with Flexs Jupiter platform and manufacturing capabilities to support Torcs productization and scaled market entry in 2027.Growing Demand for DRIVE AGXNVIDIA DRIVE AGX Orin platform is the AI brain behind todays intelligent fleets and the next wave of mobility is already arriving, as production vehicles built on the NVIDIA DRIVE AGX Thor centralized car computer start to hit the roads.Magna is a key global automotive supplier helping to meet the surging demand for the NVIDIA Blackwell architecture-based DRIVE Thor platform designed for the most demanding processing workloads, including those involving generative AI, vision language models and large language models (LLMs). Magna will develop driving systems built with DRIVE AGX Thor for integration in automakers vehicle roadmaps, delivering active safety and comfort functions along with interior cabin AI experiences.Simulation and Data: The Backbone of AV DevelopmentEarlier this year, NVIDIA announced the Omniverse Blueprint for AV simulation, a reference workflow for creating rich 3D worlds for autonomous vehicle training, testing and validation. The blueprint is expanding to include NVIDIA Cosmos world foundation models (WFMs) to amplify photoreal data variation.Unveiled at the CES trade show in January, Cosmos is already being adopted in automotive, including by Plus, which is embedding Cosmos physical AI models into its SuperDrive technology, accelerating the development of level 4 self-driving trucks.Foretellix is extending its integration of the blueprint, using the Cosmos Transfer WFM to add conditions like weather and lighting to its sensor simulation scenarios to achieve greater situation diversity. Mcity is integrating the blueprint into the digital twin of its AV testing facility to enable physics-based modeling of camera, lidar, radar and ultrasonic sensor data.CARLA, which offers an open-source AV simulator, has integrated the blueprint to deliver high-fidelity sensor simulation. Global systems integrator Capgemini will be the first to use CARLAs Omniverse integration for enhanced sensor simulation in its AV development platform.NVIDIA is using Nexars extensive, high-quality, edge-case data to train and fine-tune NVIDIA Cosmos simulation capabilities. Nexar is tapping into Cosmos, neural infrastructure models and the NVIDIA DGX Cloud platform to supercharge its AI development, refining AV training, high-definition mapping and predictive modeling.Enhancing In-Vehicle Experiences With NVIDIA AI EnterpriseMobility leaders are integrating the NVIDIA AI Enterprise software platform, running on DRIVE AGX, to enhance in-vehicle experiences with generative and agentic AI.At GTC, Cerence AI is showcasing Cerence xUI, its new LLM-based AI assistant platform that will advance the next generation of agentic in-vehicle user experiences. The Cerence xUI hybrid platform runs in the cloud as well as onboard the vehicle, optimized first on NVIDIA DRIVE AGX Orin.As the foundation for Cerence xUI, the CaLLM family of language models is based on open-source foundation models and fine-tuned on Cerence AIs automotive dataset. Tapping into NVIDIA AI Enterprise and bolstering inference performance including through the NVIDIA TensorRT-LLM library and NVIDIA NeMo, Cerence AI has optimized CaLLM to serve as the central agentic orchestrator facilitating enriched driver experiences at the edge and in the cloud.SoundHound will also be demonstrating its next-generation in-vehicle voice assistant, which uses generative AI at the edge with NVIDIA DRIVE AGX, enhancing the in-car experience by bringing cloud-based LLM intelligence directly to vehicles.The Complexity of Autonomy and NVIDIAs Safety-First SolutionSafety is the cornerstone in deploying highly automated and autonomous vehicles to the roads at scale. But building AVs is one of todays most complex computing challenges. It demands immense computational power, precision and an unwavering commitment to safety.AVs and highly automated cars promise to extend mobility to those who need it most, reducing accidents and saving lives. To help deliver on this promise, NVIDIA has developed NVIDIA Halos, a full-stack comprehensive safety system that unifies vehicle architecture, AI models, chips, software, tools and services for the safe development of AVs from the cloud to the car.NVIDIA will host its inaugural AV Safety Day at GTC today, featuring in-depth discussions on automotive safety frameworks and implementation.In addition, NVIDIA will host Automotive Developer Day on Thursday, March 20, offering sessions on the latest advancements in end-to-end AV development and beyond.New Tools for AV DevelopersNVIDIA also released new NVIDIA NIM microservices for automotive designed to accelerate development and deployment of end-to-end stacks from cloud to car. The new NIM microservices for in-vehicle applications, which utilize the nuScenes dataset by Motional, include:BEVFormer, a state-of-the-art transformer-based model that fuses multi-frame camera data into a unified birds-eye-view representation for 3D perception.SparseDrive, an end-to-end autonomous driving model that performs motion prediction and planning simultaneously, outputting a safe planning trajectory.For automotive enterprise applications, NVIDIA offers a variety of models, including NV-CLIP, a multimodal transformer model that generates embeddings from images and text; Cosmos Nemotron, a vision language model that queries and summarizes images and videos for multimodal understanding and AI-powered perception; and many more.Learn more about NVIDIAs latest automotive news by watching the NVIDIA GTC keynote and register for sessions from NVIDIA and industry leaders at the show, which runs through March 21.

Tens of thousands of companies worldwide rely on Apache Spark to crunch massive datasets to support critical operations, as well as predict trends, customer behavior, business performance and more. The faster a company can process and understand its data, the more it stands to make and save.Thats why companies with massive datasets including the worlds largest retailers and banks have adopted NVIDIA RAPIDS Accelerator for Apache Spark. The open-source software runs on top of the NVIDIA accelerated computing platform to significantly accelerate the processing of end-to-end data science and analytics pipelines without any code changes.To make it even easier for companies to get value out of NVIDIA-accelerated Spark, NVIDIA today unveiled Project Aether a collection of tools and processes that automatically qualify, test, configure and optimize Spark workloads for GPU acceleration at scale.Project Aether Completes a Years Worth of Work in Less Than a WeekCustomers using Spark in production often manage tens of thousands of complex jobs, or more. Migrating from CPU-only to GPU-powered computing offers numerous and significant benefits, but can be a manual and time-consuming process.Project Aether automates the myriad steps that companies previously have done manually, including analyzing all of their Spark jobs to identify the best candidates for GPU acceleration, as well as staging and performing test runs of each job. It uses AI to fine-tune the configuration of each job to obtain the maximum performance.To understand the impact of Project Aether, consider an enterprise that has 100 Spark jobs to complete. With Project Aether, each of these jobs can be configured and optimized for NVIDIA GPU acceleration in as little as four days. The same process done manually by a single data engineer could take up to an entire year.CBA Drives AI Transformation With NVIDIA-Accelerated Apache SparkRunning Apache Spark on NVIDIA accelerated computing helps enterprises around the world complete jobs faster and with less hardware compared with using CPUs only saving time, space, power and cooling, as well as on-premises capital and operational costs in the cloud.Australias largest financial institution, the Commonwealth Bank of Australia, is responsible for processing 60% of the continents financial transactions. CBA was experiencing challenges from the latency and costs associated with running its Spark workloads. Using CPU-only computing clusters, the bank estimates it faced nearly nine years of processing time for its training backlog on top of handling already taxing daily data demands.With 40 million inferencing transactions a day, it was critical we were able to process these in a timely, reliable manner, said Andrew McMullan, chief data and analytics officer at CBA.Running RAPIDS Accelerator for Apache Spark on GPU-powered infrastructure provided CBA with a 640x performance boost, allowing the bank to process a training of 6.3 billion transactions in just five days. Additionally, on its daily volume of 40 million transactions, CBA is now able to conduct inference in 46 minutes and reduce costs by more than 80% compared with using a CPU-based solution.McMullan says another value of NVIDIA-accelerated Apache Spark is how it offers his team the compute time efficiency needed to cost-effectively build models that can help CBA deliver better customer service, anticipate when customers may need assistance with home loans and more quickly detect fraudulent transactions.CBA also plans to use NVIDIA-accelerated Apache Spark to better pinpoint where customers commonly end their digital journeys, enabling the bank to remediate when needed to reduce the rate of abandoned applications.Global EcosystemRAPIDS Accelerator for Apache Spark is available through a global network of partners. It runs on Amazon Web Services, Cloudera, Databricks, Dataiku, Google Cloud, Microsoft Azure and Oracle Cloud Infrastructure.Dell Technologies today also announced the integration of RAPIDS Accelerator for Apache Spark with Dell Data Lakehouse.To get assistance through NVIDIA Project Aether with a large-scale migration of Apache Spark workloads, apply for access.To learn more, register for NVIDIA GTC and attend these key sessions featuring Walmart, Capital One, CBA and other industry leaders:How Walmart Uses RAPIDS to Improve Efficiency, and What We Have Learned Along the WayAccelerate Distributed Apache Spark Applications on Kubernetes With RAPIDSBuild Lightning-Fast Data Science Pipelines in Industry With Accelerated ComputingAdvancing Transaction Fraud Detection in Financial Services With NVIDIA RAPIDS on AWSAccelerating Data Intelligence With GPUs and RAPIDS on DatabricksScale Your Apache Spark Data Processing With State-of-the-Art NVIDIA Blackwell GPUs for Cost Savings and PerformanceSee notice regarding software product information.

The quick-service restaurant industry is a marvel of modern logistics, where speed, teamwork and kitchen operations are key ingredients for every order. Yum! Brands is now introducing AI-powered agents at select Pizza Hut and Taco Bell locations to assist and enhance the team member experience.Today at the NVIDIA GTC conference, Yum! Brands announced a strategic partnership with NVIDIA with a goal of deploying multiple AI solutions using NVIDIA technology in 500 restaurants this year.Worlds Largest Restaurant Company Advances AI AdoptionSpanning more than 61,000 locations, Yum! operates more restaurants than any other company in the world. Globally, customers are drawn to the food, value, service and digital convenience from iconic brands like KFC, Taco Bell, Pizza Hut and Habit Burger & Grill.Yum!s industry-leading digital technology team continues to pioneer the companys AI-accelerated strategy with the recent announcement of Byte by Yum!, Yum!s proprietary and digital AI-driven restaurant technology platform.Generative AI-powered customer-facing experiences like automated ordering can help speed operations but theyre often difficult to scale because of complexity and costs.To manage that complexity, developers at Byte by Yum! harnessed NVIDIA NIM microservices and NVIDIA Riva to build new AI-accelerated voice ordering agents in under four months. The voice AI is deployed on Amazon EC2 P4d instances accelerated by NVIDIA A100 GPUs, which enables the agents to understand natural speech, process complex menu orders and suggest add-ons increasing accuracy and customer satisfaction and helping reduce bottlenecks in high-volume locations.The new collaboration with NVIDIA will help Yum! advance its ongoing efforts to have its engineering and data science teams in control of their own intelligence and deliver scalable inference costs, making large-scale deployments possible.At Yum, we have a bold vision to deliver leading-edge, AI-powered technology capabilities to our customers and team members globally, said Joe Park, chief digital and technology officer of Yum! Brands, Inc. and president of Byte by Yum!. We are thrilled to partner with a pioneering company like NVIDIA to help us accelerate this ambition. This partnership will enable us to harness the rich consumer and operational datasets on our Byte by Yum! integrated platform to build smarter AI engines that will create easier experiences for our customers and team members.Rollout of AI Solutions UnderwayYum!s voice AI agents are already being deployed across its brands, including in call centers to handle phone orders when demand surges during events like game days. An expanded rollout of AI solutions at up to 500 restaurants is expected this year.Computer Vision and Restaurant IntelligenceBeyond AI-accelerated ordering, Yum! is also testing NVIDIA computer vision software to analyze drive-thru traffic and explore new use cases for AI to perceive, alert and adjust staffing, with the goal of optimizing service speed.Another initiative focuses on NVIDIA AI-accelerated restaurant operational intelligence. Using NIM microservices, Yum! can deploy applications analyzing performance metrics across thousands of locations to generate customized recommendations for managers, identifying what top-performing stores do differently and applying those insights system-wide.With the NVIDIA AI Enterprise software platform available on AWS Marketplace Byte by Yum! is streamlining AI development and deployment through scalable NVIDIA infrastructure in the cloud.The bottom line: AI is making restaurant operations and dining experiences easier, faster and more personal for the worlds largest restaurant company.

Global telecommunications networks can support millions of user connections per day, generating more than 3,800 terabytes of data per minute on average.That massive, continuous flow of data generated by base stations, routers, switches and data centers including network traffic information, performance metrics, configuration and topology is unstructured and complex. Not surprisingly, traditional automation tools have often fallen short on handling massive, real-time workloads involving such data.To help address this challenge, NVIDIA today announced at the GTC global AI conference that its partners are developing new large telco models (LTMs) and AI agents custom-built for the telco industry using NVIDIA NIM and NeMo microservices within the NVIDIA AI Enterprise software platform. These LTMs and AI agents enable the next generation of AI in network operations.LTMs customized, multimodal large language models (LLMs) trained specifically on telco network data are core elements in the development of network AI agents, which automate complex decision-making workflows, improve operational efficiency, boost employee productivity and enhance network performance.SoftBank and Tech Mahindra have built new LTMs and AI agents, while Amdocs, BubbleRAN and ServiceNow, are dialing up their network operations and optimization with new AI agents, all using NVIDIA AI Enterprise.Its important work at a time when 40% of respondents in a recent NVIDIA-run telecom survey noted theyre deploying AI into their network planning and operations.LTMs Understand the Language of NetworksJust as LLMs understand and generate human language, and NVIDIA BioNeMo NIM microservices understand the language of biological data for drug discovery, LTMs now enable AI agents to master the language of telecom networks.The new partner-developed LTMs powered by NVIDIA AI Enterprise are:Specialized in network intelligence the LTMs can understand real-time network events, predict failures and automate resolutions.Optimized for telco workloads tapping into NVIDIA NIM microservices, the LTMs are optimized for efficiency, accuracy and low latency.Suited for continuous learning and adaptation with post-training scalability, the LTMs can use NVIDIA NeMo to learn from new events, alerts and anomalies to enhance future performance.NVIDIA AI Enterprise provides additional tools and blueprints to build AI agents that simplify network operations and deliver cost savings and operational efficiency, while improving network key performance indicators (KPIs), such as:Reduced downtime AI agents can predict failures before they happen, delivering network resilience.Improved customer experiences AI-driven optimizations lead to faster networks, fewer outages and seamless connectivity.Enhanced security as it continuously scans for threats, AI can help mitigate cyber risks in real time.Industry Leaders Launch LTMs and AI AgentsLeading companies across telecommunications are using NVIDIA AI Enterprise to advance their latest technologies.SoftBank has developed a new LTM based on a large-scale LLM base model, trained on its own network data. Initially focused on network configuration, the model which is available as an NVIDIA NIM microservice can automatically reconfigure the network to adapt to changes in network traffic, including during mass events at stadiums and other venues. SoftBank is also introducing network agent blueprints to help accelerate AI adoption across telco operations.Tech Mahindra has developed an LTM with the NVIDIA agentic AI tools to help address critical network operations. Tapping into this LTM, the companys Adaptive Network Insights Studio provides a 360-degree view of network issues, generating automated reports at various levels of detail to inform and assist IT teams, network engineers and company executives.In addition, Tech Mahindras Proactive Network Anomaly Resolution Hub is powered by the LTM to automatically resolve a significant portion of its network events, lightening engineers workloads and enhancing their productivity.Amdocs Network Assurance Agent, powered by amAIz Agents, automates repetitive tasks such as fault prediction. It also conducts impact analysis and prevention methods for network issues, providing step-by-step guidance on resolving any problems that occur. Plus, the companys Network Deployment Agent simplifies open radio access network (RAN) adoption by automating integration, deployment tasks and interoperability testing, and providing insights to network engineers.BubbleRAN is developing an autonomous multi-agent RAN intelligence platform on a cloud-native infrastructure, where LTMs can observe the network state, configuration, availability and KPIs to facilitate monitoring and troubleshooting. The platform also automates the process of network reconfiguration and policy enforcement through a high-level set of action tools. The companys AI agents satisfy user needs by tapping into advanced retrieval-augmented generation pipelines and telco-specific application programming interfaces, answering real-time, 5G deployment-specific questions.ServiceNows AI agents in telecom built with NVIDIA AI Enterprise on NVIDIA DGX Cloud drive productivity by generating resolution playbooks and predicting potential network disruptions before they occur. This helps communications service providers reduce resolution time and improve customer satisfaction. The new, ready-to-use AI agents also analyze network incidents, identifying root causes of disruptions so they can be resolved faster and avoided in the future.Learn more about the latest agentic AI advancements at NVIDIA GTC, running through Friday, March 21, in San Jose, California.

The telecom industry is increasingly embracing AI to deliver seamless connections even in conditions of poor signal strength while maximizing sustainability and spectral efficiency, the amount of information that can be transmitted per unit of bandwidth.Advancements in AI-RAN technology have set the course toward AI-native wireless networks for 6G, built using AI and accelerated computing from the start, to meet the demands of billions of AI-enabled connected devices, sensors, robots, cameras and autonomous vehicles.To help developers and telecom leaders pioneer these networks, NVIDIA today unveiled new tools in the NVIDIA Aerial Research portfolio.The expanded portfolio of solutions include the Aerial Omniverse Digital Twin on NVIDIA DGX Cloud, the Aerial Commercial Test Bed on NVIDIA MGX, the NVIDIA Sionna 1.0 open-source library and the Sionna Research Kit on NVIDIA Jetson helping accelerate AI-RAN and 6G research.Industry leaders like Amdocs, Ansys, Capgemini, DeepSig, Fujitsu, Keysight, Kyocera, MathWorks, Mediatek, Samsung Research, SoftBank and VIAVI Solutions and more than 150 higher education and research institutions from U.S. and around the world including Northeastern University, Rice University, The University of Texas at Austin, ETH Zurich, Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institut, HHI, Singapore University of Technology and Design,and University of Oulu are harnessing the NVIDIA Aerial Research portfolio to develop, train, simulate and deploy groundbreaking AI-native wireless innovations.New Tools for Research and DevelopmentThe Aerial Research portfolio provides exceptional flexibility and ease of use for developers at every stage of their research from early experimentation to commercial deployment. Its offerings include:Aerial Omniverse Digital Twin (AODT): A simulation platform to test and fine-tune algorithms in physically precise digital replicas of entire wireless systems, now available on NVIDIA DGX Cloud. Developers can now access AODT everywhere, whether on premises, on laptops, via the public cloud or on an NVIDIA cloud service.Aerial Commercial Test Bed (aka ARC-OTA): A full-stack AI-RAN deployment system that enables developers to deploy new AI models over the air and test them in real time, now available on NVIDIA MGX and available through manufacturers including Supermicro or as a managed offering via Sterling Skywave. ARC-OTA integrates commercial-grade Aerial CUDA-accelerated RAN software with open-source L2+ and 5G core from OpenAirInterface (OAI) and O-RAN-compliant 7.2 split open radio units from WNC and LITEON Technology to enable an end-to-end system for AI-RAN commercial testing.Sionna 1.0: The most widely used GPU-accelerated open-source library for research in communication systems, with more than 135,000 downloads. The latest release of Sionna features a lightning-fast ray tracer for radio propagation, a versatile link-level simulator and new system-level simulation capabilities.Sionna Research Kit: Powered by the NVIDIA Jetson platform, it integrates accelerated computing for AI and machine learning workloads and a software-defined RAN built on OAI. With the kit, researchers can connect 5G equipment and begin prototyping AI-RAN algorithms for next-generation wireless networks in just a few hours.NVIDIA Aerial Research Ecosystem for AI-RAN and 6GThe NVIDIA Aerial Research portfolio includes the NVIDIA 6G Developer Program, an open community that serves more than 2,000 members, representing leading technology companies, academia, research institutions and telecom operators using NVIDIA technologies to complement their AI-RAN and 6G research.Testing and simulation will play an essential role in developing AI-native wireless networks. Companies such as Amdocs, Ansys, Keysight, MathWorks and VIAVI are enhancing their simulation solutions with NVIDIA AODT, while operators have created digital twins of their radio access networks to optimize performance with changing traffic scenarios.Nine out of 10 demonstrations chosen by the AI-RAN Alliance for Mobile World Congress were developed using the NVIDIA Aerial Research portfolio, leading to breakthrough results.SoftBank and Fujitsu demonstrated an up to 50% throughput gain in poor radio environments using AI-based uplink channel interpolation.DeepSig developed OmniPHY, an AI-native air interface that eliminates traditional pilot overhead, harnessing neural networks to achieve up to 70% throughput gains in certain scenarios. Using the NVIDIA AI Aerial platform, OmniPHY integrates machine learning into modulation, reception and demodulation to optimize spectral efficiency, reduce power consumption and enhance wireless network performance.AI-native signal processing is transforming wireless networks, delivering real-world results, said Jim Shea, cofounder and CEO of DeepSig. By integrating deep learning to the air interface and leveraging NVIDIAs tools, were redefining how AI-native wireless networks are designed and built.In addition to the Aerial Research portfolio, using the open ecosystem of NVIDIA CUDA-X libraries, built on CUDA, developers can build applications that deliver dramatically higher performance.Join the NVIDIA 6G Developer Program to access NVIDIA Aerial Research platform tools.See notice regarding software product information.

AI agents are transforming work, delivering time and cost savings by helping people resolve complex challenges in new ways.Whether developed for humanitarian aid, customer service or healthcare, AI agents built with the NVIDIA AI Enterprise software platform make up a new digital workforce helping professionals accomplish their goals faster at lower costs and for greater impact.AI Agents Enable Growth and Education AI can instantly translate, summarize and process multimodal content in hundreds of languages. Integrated into agentic systems, the technology enables international organizations to engage and educate global stakeholders more efficiently.The United Nations (UN) is working with Accenture to develop a multilingual research agent to support over 150 languages to promote local economic sustainability. The agent will act like a researcher, answering questions about the UNs Sustainable Development Goals and fostering awareness and engagement toward its agenda of global peace and prosperity.Mercy Corps, in collaboration with Cloudera, has deployed an AI-driven Methods Matcher tool that supports humanitarian aid experts in more than 40 countries by providing research, summaries, best-practice guidelines and data-driven crisis responses, providing faster aid delivery in disaster situations.Wikimedia Deutschland, using the DataStax AI Platform, built with NVIDIA AI, can process and embed 10 million Wikidata items in just three days, with 30x faster ingestion performance.AI Agents Provide Tailored Customer Service Across IndustriesAgentic AI enhances customer service with real-time, highly accurate insights for more effective user experiences. AI agents provide 24/7 support, handling common inquiries with more personalized responses while freeing human agents to address more complex issues.Intelligent-routing capabilities categorize and prioritize requests so customers can be quickly directed to the right specialists. Plus, AI agents predictive-analytics capabilities enable proactive support by anticipating issues and empowering human agents with data-driven insights.Companies across industries including telecommunications, finance, healthcare and sports are already tapping into AI agents to achieve massive benefits.AT&T, in collaboration with Quantiphi, developed and deployed a new Ask AT&T AI agent to its call center, leading to a 84% decrease in call center analytics costs.Southern California Edison, working with WWT, is driving Project Orca to enhance data processing and predictions for 100,000+ network assets using agents to reduce downtime, enhance network reliability and enable faster, more efficient ticket resolution.With the adoption of ServiceNow Dispute Management, built with Visa, banks can use AI agents with the solution to achieve up to a 28% reduction in call center volumes and a 30% decrease in time to resolution.The Ottawa Hospital, working with Deloitte, deployed a team of 24/7 patient-care agents to provide preoperative support and answer patient questions regarding upcoming procedures for over 1.2 million people in eastern Ontario, Canada.With the VAST Data Platform, the National Hockey League can unlock over 550,000 hours of historical game footage. This supports sponsorship analysis, helps video producers quickly create broadcast clips and enhances personalized fan content.State-of-the-Art AI Agents Built With NVIDIA AI Enterprise AI agents have emerged as versatile tools that can be adapted and adopted across a wide range of industries. These agents connect to organizational knowledge bases to understand the business context theyre deployed in. Their core functionalities such as question-answering, translation, data processing, predictive analytics and automation can be tailored to improve productivity and save time and costs, by any organization, in any industry.NVIDIA AI Enterprise provides the building blocks for enterprise AI agents. It includes NVIDIA NIM microservices for efficient inference of state-of-the-art models including the new NVIDIA Llama Nemotron reasoning model family and NVIDIA NeMo tools to streamline data processing, model customization, system evaluation, retrieval-augmented generation and guardrailing.NVIDIA Blueprints are reference workflows that showcase best practices for developing high-performance agentic systems. With the AI-Q NVIDIA AI Blueprint, developers can build AI agents into larger agentic systems that can reason, then connect these systems to enterprise data to tackle complex problems, harness other tools, collaborate and operate with greater autonomy.Learn more about AI agent development by watching the NVIDIA GTC keynote and register for sessions from NVIDIA and industry leaders at the show, which runs through March 21.See notice regarding software product information.

AI deployments thrive on speed, data and scale. Thats why NVIDIA is expanding NVIDIA-Certified Systems to include enterprise storage certification for streamlined AI factory deployments in the enterprise with accelerated computing, networking, software and storage.As enterprises build AI factories, access to high-quality data is imperative to ensure optimal performance and reliability for AI models. The new NVIDIA-Certified Storage program announced today at the NVIDIA GTC global AI conference validates that enterprise storage systems meet stringent performance and scalability data requirements for AI and high-performance computing workloads.Leading enterprise data platform and storage providers are already onboard, ensuring businesses have trusted options from day one. These include DDN, Dell Technologies, Hewlett Packard Enterprise, Hitachi Vantara, IBM, NetApp, Nutanix, Pure Storage, VAST Data and WEKA.Building Blocks for a New Class of Enterprise InfrastructureAt GTC, NVIDIA also announced the NVIDIA AI Data Platform, a customizable reference design to build a new class of enterprise infrastructure for demanding agentic AI workloads.The NVIDIA-Certified Storage designation is a prerequisite for partners developing agentic AI infrastructure solutions built on the NVIDIA AI Data Platform. Each of these NVIDIA-Certified Storage partners will deliver customized AI data platforms, in collaboration with NVIDIA, that can harness enterprise data to reason and respond to complex queries.NVIDIA-Certified was created more than four years ago as the industrys first certification program dedicated to tuning and optimizing AI systems to ensure optimal performance, manageability and scalability. Each NVIDIA-Certified system is rigorously tested and validated to deliver enterprise-grade AI performance.There are now 50+ partners providing 500+ NVIDIA-Certified systems, helping enterprises reduce time, cost and complexity by giving them a wide selection of performance-optimized systems to power their accelerated computing workloads.NVIDIA Enterprise Reference Architectures (RAs) were introduced last fall to provide partners with AI infrastructure best practices and configuration guidance for deploying NVIDIA-Certified servers, NVIDIA Spectrum-X networking and NVIDIA AI Enterprise software.Solutions based on NVIDIA Enterprise RAs are available from the worlds leading systems providers to reduce the time, cost and complexity of enterprise AI deployments. Enterprise RAs are now available for a wide range of NVIDIA Hopper and NVIDIA Blackwell platforms, including NVIDIA HGX B200 systems and the new NVIDIA RTX PRO 6000 Blackwell Server Edition GPU.These NVIDIA technologies and partner solutions are the building blocks for enterprise AI factories, representing a new class of enterprise infrastructure for high-performance AI deployments at scale.Enterprise AI Needs Scalable StorageAs the pace of AI innovation and adoption accelerates, secure and reliable access to high-quality enterprise data is becoming more important than ever. Data is the fuel for the AI factory. With enterprise data creation projected to reach 317 zettabytes annually by 2028*, AI workloads require storage architectures built to handle massive, unstructured and multimodal datasets.NVIDIAs expanded storage certification program is designed to meet this need and help enterprises build AI factories with a foundation of high-performance, reliable data storage solutions. The program includes performance testing as well as validation that partner storage systems adhere to design best practices, optimizing performance and scalability for enterprise AI workloads.NVIDIA-Certified Storage will be incorporated into NVIDIA Enterprise RAs, providing enterprise-grade data storage for AI factory deployments with full-stack solutions from global systems partners.Certified Storage for Every DeploymentThis certification builds on existing NVIDIA DGX systems and NVIDIA Cloud Partner (NCP) storage programs, expanding the data ecosystem for AI infrastructure.These storage certification programs are aligned with their deployment models and architectures:NVIDIA DGX BasePOD and DGX SuperPOD Storage Certification designed for enterprise AI factory deployments with NVIDIA DGX systems.NCP Storage Certification designed for large-scale NCP Reference Architecture AI factory deployments with cloud providers.NVIDIA-Certified Storage Certification designed for enterprise AI factory deployments with NVIDIA-Certified servers available from global partners, based on NVIDIA Enterprise RA guidelines.With this framework, organizations of all sizes from cloud hyperscalers to enterprises can build AI factories that process massive amounts of data, train models faster and drive more accurate, reliable AI outcomes.Learn more about how NVIDIA-Certified Systems deliver seamless, high-speed performance and attend these related sessions at GTC:Unlock the Power of NVIDIA Certified Systems: Live Q&A With ExpertsEverything You Want to Ask About NVIDIA Enterprise Reference Architectures*Source: IDC, Worldwide IDC Global DataSphere Forecast, 20242028: AI Everywhere, But Upsurge in Data Will Take Time, doc #US52076424, May 2024

As quantum computers continue to develop, they will integrate with AI supercomputers to form accelerated quantum supercomputers capable of solving some of the worlds hardest problems.Integrating quantum processing units (QPUs) into AI supercomputers is key for developing new applications, helping unlock breakthroughs critical to running future quantum hardware and enabling developments in quantum error correction and device control.The NVIDIA Accelerated Quantum Research Center, or NVAQC, announced today at the NVIDIA GTC global AI conference, is where these developments will happen. With an NVIDIA GB200 NVL72 system and the NVIDIA Quantum-2 InfiniBand networking platform, the facility will house a supercomputer with 576 NVIDIA Blackwell GPUs dedicated to quantum computing research.The NVAQC draws on much-needed and long-sought-after tools for scaling quantum computing to next-generation devices, said Tim Costa, senior director of computer-aided engineering, quantum and CUDA-X at NVIDIA. The center will be a place for large-scale simulations of quantum algorithms and hardware, tight integration of quantum processors, and both training and deployment of AI models for quantum.The NVAQC will host a GB200 NVL72 system.Quantum computing innovators like Quantinuum, QuEra and Quantum Machines, along with academic partners from the Harvard Quantum Initiative and the Engineering Quantum Systems group at the MIT Center for Quantum Engineering, will work on projects with NVIDIA at the center to explore how AI supercomputing can accelerate the path toward quantum computing.The NVAQC is a powerful tool that will be instrumental in ushering in the next generation of research across the entire quantum ecosystem, said William Oliver, professor of electrical engineering and computer science, and of physics, leader of the EQuS group and director of the MIT Center for Quantum Engineering. NVIDIA is a critical partner for realizing useful quantum computing.There are several key quantum computing challenges where the NVAQC is already set to have a dramatic impact.Protecting Qubits With AI SupercomputingQubit interactions are a double-edged sword. While qubits must interact with their surroundings to be controlled and measured, these same interactions are also a source of noise unwanted disturbances that affect the accuracy of quantum calculations. Quantum algorithms can only work if the resulting noise is kept in check.Quantum error correction provides a solution, encoding noiseless, logical qubits within many noisy, physical qubits. By processing the outputs from repeated measurements on these noisy qubits, its possible to identify, track and correct qubit errors all without destroying the delicate quantum information needed by a computation.The process of figuring out where errors occurred and what corrections to apply is called decoding. Decoding is an extremely difficult task that must be performed by a conventional computer within a narrow time frame to prevent noise from snowballing out of control.A key goal of the NVAQC will be exploring how AI supercomputing can accelerate decoding. Studying how to collocate quantum hardware within the center will allow the development of low-latency, parallelized and AI-enhanced decoders, running on NVIDIA GB200 Grace Blackwell Superchips.The NVAQC will also tackle other challenges in quantum error correction. QuEra will work with NVIDIA to accelerate its search for new, improved quantum error correction codes, assessing the performance of candidate codes through demanding simulations of complex quantum circuits.The NVAQC will be an essential tool for discovering, testing and refining new quantum error correction codes and decoders capable of bringing the whole industry closer to useful quantum computing, said Mikhail Lukin, Joshua and Beth Friedman University Professor at Harvard and a codirector of the Harvard Quantum Initiative.Developing Applications for Accelerated Quantum SupercomputersThe majority of useful quantum algorithms draw equally from classical and quantum computing resources, ultimately requiring an accelerated quantum supercomputer that unifies both kinds of hardware.For example, the output of classical supercomputers is often needed to prime quantum computations. The NVAQC provides the heterogeneous compute infrastructure needed for research on developing and improving such hybrid algorithms.Accelerated quantum supercomputers will connect quantum and classical processors to execute hybrid algorithms.New AI-based compilation techniques will also be explored at the NVAQC, with the potential to accelerate the runtime of all quantum algorithms, including through work with Quantinuum. Quantinuum will build on its previous integration work with NVIDIA, offering its hardware and emulators through the NVIDIA CUDA-Q platform. Users of CUDA-Q are currently offered access to Quantinuums System H1 QPU hardware and emulator for 90 days.Were excited to collaborate with NVIDIA at this center, said Rajeeb Hazra, president and CEO of Quantinuum. By combining Quantinuums powerful quantum systems with NVIDIAs cutting-edge accelerated computing, were pushing the boundaries of hybrid quantum-classical computing and unlocking exciting new possibilities.QPU IntegrationIntegrating quantum hardware with AI supercomputing is one of the major remaining hurdles on the path to running useful quantum hardware.The requirements of such an integration can be extremely demanding. The decoding required by quantum error correction can only function if data from millions of qubits can be sent between quantum and classical hardware at ultralow latencies.Quantum Machines will work with NVIDIA at the NVAQC to develop and hone new controller technologies supporting rapid, high-bandwidth interfaces between quantum processors and GB200 superchips.Were excited to see NVIDIAs growing commitment to accelerating the realization of useful quantum computers, providing researchers with the most advanced infrastructure to push the boundaries of quantum-classical computing, said Itamar Sivan, CEO of Quantum Machines.The NVIDIA DGX Quantum system comprises an NVIDIA GH200 superchip and Quantum Machines OPX1000 control system.Key to integrating quantum and classical hardware is a platform that lets researchers and developers quickly shift context between these two disparate computing paradigms within a single application. The NVIDIA CUDA-Q platform will be the entry point for researchers to harness the NVAQCs quantum-classical integration.Building on tools like NVIDIA DGX Quantum a reference architecture for integrating quantum and classical hardware and CUDA-Q, the NVAQC is set to be an epicenter for next-generation developments in quantum computing, seeding the evolution of qubits into impactful quantum computers.Learn more about NVIDIA quantum computing.

AI is fueling a new industrial revolution one driven by AI factories.Unlike traditional data centers, AI factories do more than store and process data they manufacture intelligence at scale, transforming raw data into real-time insights. For enterprises and countries around the world, this means dramatically faster time to value turning AI from a long-term investment into an immediate driver of competitive advantage. Companies that invest in purpose-built AI factories today will lead in innovation, efficiency and market differentiation tomorrow.While a traditional data center typically handles diverse workloads and is built for general-purpose computing, AI factories are optimized to create value from AI. They orchestrate the entire AI lifecycle from data ingestion to training, fine-tuning and, most critically, high-volume inference.For AI factories, intelligence isnt a byproduct but the primary one. This intelligence is measured by AI token throughput the real-time predictions that drive decisions, automation and entirely new services.While traditional data centers arent disappearing anytime soon, whether they evolve into AI factories or connect to them depends on the enterprise business model.Regardless of how enterprises choose to adapt, AI factories powered by NVIDIA are already manufacturing intelligence at scale, transforming how AI is built, refined and deployed.The Scaling Laws Driving Compute DemandOver the past few years, AI has revolved around training large models. But with the recent proliferation of AI reasoning models, inference has become the main driver of AI economics. Three key scaling laws highlight why:Pretraining scaling: Larger datasets and model parameters yield predictable intelligence gains, but reaching this stage demands significant investment in skilled experts, data curation and compute resources. Over the last five years, pretraining scaling has increased compute requirements by 50 million times. However, once a model is trained, it significantly lowers the barrier for others to build on top of it.Post-training scaling: Fine-tuning AI models for specific real-world applications requires 30x more compute during AI inference than pretraining. As organizations adapt existing models for their unique needs, cumulative demand for AI infrastructure skyrockets.Test-time scaling (aka long thinking): Advanced AI applications such as agentic AI or physical AI require iterative reasoning, where models explore multiple possible responses before selecting the best one. This consumes up to 100x more compute than traditional inference.Traditional data centers arent designed for this new era of AI. AI factories are purpose-built to optimize and sustain this massive demand for compute, providing an ideal path forward for AI inference and deployment.Reshaping Industries and Economies With TokensAcross the world, governments and enterprises are racing to build AI factories to spur economic growth, innovation and efficiency.The European High Performance Computing Joint Undertaking recently announced plans to build seven AI factories in collaboration with 17 European Union member nations.This follows a wave of AI factory investments worldwide, as enterprises and countries accelerate AI-driven economic growth across every industry and region:India: Yotta Data Services has partnered with NVIDIA to launch the Shakti Cloud Platform, helping democratize access to advanced GPU resources. By integrating NVIDIA AI Enterprise software with open-source tools, Yotta provides a seamless environment for AI development and deployment.Japan: Leading cloud providers including GMO Internet, Highreso, KDDI, Rutilea and SAKURA internet are building NVIDIA-powered AI infrastructure to transform industries such as robotics, automotive, healthcare and telecom.Norway: Telenor has launched an NVIDIA-powered AI factory to accelerate AI adoption across the Nordic region, focusing on workforce upskilling and sustainability.These initiatives underscore a global reality: AI factories are quickly becoming essential national infrastructure, on par with telecommunications and energy.Inside an AI Factory: Where Intelligence Is ManufacturedFoundation models, secure customer data and AI tools provide the raw materials for fueling AI factories, where inference serving, prototyping and fine-tuning shape powerful, customized models ready to be put into production.As these models are deployed into real-world applications, they continuously learn from new data, which is stored, refined and fed back into the system using a data flywheel. This cycle of optimization ensures AI remains adaptive, efficient and always improving driving enterprise intelligence at an unprecedented scale.AI factories powered by NVIDIA for manufacturing enterprise intelligence at scale.An AI Factory Advantage With Full-Stack NVIDIA AINVIDIA delivers a complete, integrated AI factory stack where every layer from the silicon to the software is optimized for training, fine-tuning, and inference at scale. This full-stack approach ensures enterprises can deploy AI factories that are cost effective, high-performing and future-proofed for the exponential growth of AI.With its ecosystem partners, NVIDIA has created building blocks for the full-stack AI factory, offering:Powerful compute performanceAdvanced networkingInfrastructure management and workload orchestrationThe largest AI inference ecosystemStorage and data platformsBlueprints for design and optimizationReference architecturesFlexible deployment for every enterprisePowerful Compute PerformanceThe heart of any AI factory is its compute power. From NVIDIA Hopper to NVIDIA Blackwell, NVIDIA provides the worlds most powerful accelerated computing for this new industrial revolution. With the NVIDIA Blackwell Ultra-based GB300 NVL72 rack-scale solution, AI factories can achieve up to 50X the output for AI reasoning, setting a new standard for efficiency and scale.The NVIDIA DGX SuperPOD is the exemplar of the turnkey AI factory for enterprises, integrating the best of NVIDIA accelerated computing. NVIDIA DGX Cloud provides an AI factory that delivers NVIDIA accelerated compute with high performance in the cloud.Global systems partners are building full-stack AI factories for their customers based on NVIDIA accelerated computing now including the NVIDIA GB200 NVL72 and GB300 NVL72 rack-scale solutions.Advanced NetworkingMoving intelligence at scale requires seamless, high-performance connectivity across the entire AI factory stack. NVIDIA NVLink and NVLink Switch enable high-speed, multi-GPU communication, accelerating data movement within and across nodes.AI factories also demand a robust network backbone. The NVIDIA Quantum InfiniBand, NVIDIA Spectrum-X Ethernet, and NVIDIA BlueField networking platforms reduce bottlenecks, ensuring efficient, high-throughput data exchange across massive GPU clusters. This end-to-end integration is essential for scaling out AI workloads to million-GPU levels, enabling breakthrough performance in training and inference.Infrastructure Management and Workload OrchestrationBusinesses need a way to harness the power of AI infrastructure with the agility, efficiency and scale of a hyperscaler, but without the burdens of cost, complexity and expertise placed on IT.With NVIDIA Run:ai, organizations can benefit from seamless AI workload orchestration and GPU management, optimizing resource utilization while accelerating AI experimentation and scaling workloads. NVIDIA Mission Control software, which includes NVIDIA Run:ai technology, streamlines AI factory operations from workloads to infrastructure while providing full-stack intelligence that delivers world-class infrastructure resiliency.NVIDIA Mission Control streamlines workflows across the AI factory stack.The Largest AI Inference EcosystemAI factories need the right tools to turn data into intelligence. The NVIDIA AI inference platform, spanning the NVIDIA TensorRT ecosystem, NVIDIA Dynamo and NVIDIA NIM microservices all part (or soon to be part) of the NVIDIA AI Enterprise software platform provides the industrys most comprehensive suite of AI acceleration libraries and optimized software. It delivers maximum inference performance, ultra-low latency and high throughput.Storage and Data PlatformsData fuels AI applications, but the rapidly growing scale and complexity of enterprise data often make it too costly and time-consuming to harness effectively. To thrive in the AI era, enterprises must unlock the full potential of their data.The NVIDIA AI Data Platform is a customizable reference design to build a new class of AI infrastructure for demanding AI inference workloads. NVIDIA-Certified Storage partners are collaborating with NVIDIA to create customized AI data platforms that can harness enterprise data to reason and respond to complex queries.Blueprints for Design and OptimizationTo design and optimize AI factories, teams can use the NVIDIA Omniverse Blueprint for AI factory design and operations. The blueprint enables engineers to design, test and optimize AI factory infrastructure before deployment using digital twins. By reducing risk and uncertainty, the blueprint helps prevent costly downtime a critical factor for AI factory operators.For a 1 gigawatt-scale AI factory, every day of downtime can cost over $100 million. By solving complexity upfront and enabling siloed teams in IT, mechanical, electrical, power and network engineering to work in parallel, the blueprint accelerates deployment and ensures operational resilience.Reference ArchitecturesNVIDIA Enterprise Reference Architectures and NVIDIA Cloud Partner Reference Architectures provide a roadmap for partners designing and deploying AI factories. They help enterprises and cloud providers build scalable, high-performance and secure AI infrastructure based on NVIDIA-Certified Systems with the NVIDIA AI software stack and partner ecosystem.NVIDIA full-stack AI factories, built on NVIDIA reference architectures. (*NVIS = NVIDIA infrastructure specialists)Every layer of the AI factory stack relies on efficient computing to meet growing AI demands. NVIDIA accelerated computing serves as the foundation across the stack, delivering the highest performance per watt to ensure AI factories operate at peak energy efficiency. With energy-efficient architecture and liquid cooling, businesses can scale AI while keeping energy costs in check.Flexible Deployment for Every EnterpriseWith NVIDIAs full-stack technologies, enterprises can easily build and deploy AI factories, aligning with customers preferred IT consumption models and operational needs.Some organizations opt for on-premises AI factories to maintain full control over data and performance, while others use cloud-based solutions for scalability and flexibility. Many also turn to their trusted global systems partners for pre-integrated solutions that accelerate deployment.The NVIDIA DGX GB300 is the highest-performing, largest-scale AI factory infrastructure available for enterprises that are built for the era of AI reasoning.On PremisesNVIDIA DGX SuperPOD is a turnkey AI factory infrastructure solution that provides accelerated infrastructure with scalable performance for the most demanding AI training and inference workloads. It features a design-optimized combination of AI compute, network fabric, storage and NVIDIA Mission Control software, empowering enterprises to get AI factories up and running in weeks instead of months and with best-in-class uptime, resiliency and utilization.AI factory solutions are also offered through the NVIDIA global ecosystem of enterprise technology partners with NVIDIA-Certified Systems. They deliver leading hardware and software technology, combined with data center systems expertise and liquid-cooling innovations, to help enterprises de-risk their AI endeavors and accelerate the return on investment of their AI factory implementations.These global systems partners are providing full-stack solutions based on NVIDIA reference architectures integrated with NVIDIA accelerated computing, high-performance networking and AI software to help customers successfully deploy AI factories and manufacture intelligence at scale.In the CloudFor enterprises looking to use a cloud-based solution for their AI factory, NVIDIA DGX Cloud delivers a unified platform on leading clouds to build, customize and deploy AI applications. Every layer of DGX Cloud is optimized and fully managed by NVIDIA, offering the best of NVIDIA AI in the cloud, and features enterprise-grade software and large-scale, contiguous clusters on leading cloud providers, offering scalable compute resources ideal for even the most demanding AI training workloads.DGX Cloud also includes a dynamic and scalable serverless inference platform that delivers high throughput for AI tokens across hybrid and multi-cloud environments, significantly reducing infrastructure complexity and operational overhead.By providing a full-stack platform that integrates hardware, software, ecosystem partners and reference architectures, NVIDIA is helping enterprises build AI factories that are cost effective, scalable and high-performing equipping them to meet the next industrial revolution.Learn more about NVIDIA AI factories.See notice regarding software product information.

As generative AI capabilities expand, NVIDIA is equipping developers with the tools to seamlessly integrate AI into creative projects, applications and games to unlock groundbreaking experiences on NVIDIA RTX AI PCs and workstations.At the NVIDIA GTC global AI conference this week, NVIDIA introduced the NVIDIA RTX PRO Blackwell series, a new generation of workstation and server GPUs built for complex AI-driven workloads, technical computing and high-performance graphics.Alongside the new hardware, NVIDIA announced a suite of AI-powered tools, libraries and software development kits designed to accelerate AI development on PCs and workstations. With NVIDIA CUDA-X libraries for data science, developers can significantly accelerate data processing and machine learning tasks, enabling faster exploratory data analysis, feature engineering and model development with zero code changes. And with NVIDIA NIM microservices, developers can more seamlessly build AI assistants, productivity plug-ins and advanced content-creation workflows with peak performance.AI at the Speed of NIM With RTX PRO Series GPUsThe RTX PRO Blackwell series is built to handle the most demanding AI-driven workflows, powering applications like AI agents, simulation, extended reality, 3D design and high-end visual effects. Whether for designing and engineering complex systems or creating sophisticated and immersive content, RTX PRO GPUs deliver the performance, efficiency and scalability professionals need.The new lineup includes:Desktop GPUs: NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition, NVIDIA RTX PRO 5000 Blackwell, NVIDIA RTX PRO 4500 Blackwell and NVIDIA RTX PRO 4000 BlackwellLaptop GPUs: NVIDIA RTX PRO 5000 Blackwell, NVIDIA RTX PRO 4000 Blackwell, NVIDIA RTX PRO 3000 Blackwell, NVIDIA RTX PRO 2000 Blackwell, NVIDIA RTX PRO 1000 Blackwell and NVIDIA RTX PRO 500 Blackwell Laptop GPUsData center GPU: NVIDIA RTX PRO 6000 Blackwell Server EditionAs AI and data science evolve, the ability to rapidly process and analyze massive datasets will become a key differentiator to enable breakthroughs across industries.NVIDIA CUDA-X libraries, built on CUDA, is a collection of libraries that deliver dramatically higher performance compared with CPU-only alternatives. With cuML 25.02 now available in open beta data scientists and researchers can accelerate scikit-learn, UMAP and HDBSCAN algorithms with zero code changes, unlocking new levels of performance and efficiency in machine learning tasks. This release extends the zero-code-change acceleration paradigm established by cuDF-pandas for DataFrame operations to machine learning, reducing iterations from hours to seconds.Optimized AI software unlocks even greater possibilities. NVIDIA NIM microservices are prepackaged, high-performance AI models optimized across NVIDIA GPUs, from RTX-powered PCs and workstations to the cloud. Developers can use NIM microservices to build AI-powered app assistants, productivity tools and content-creation workflows that seamlessly integrate with RTX PRO GPUs. This makes AI more accessible and powerful than ever.NIM microservices integrate top community and NVIDIA-built models, spanning capabilities and modalities important for PC and workstation use cases, including large language models (LLMs), images, speech and retrieval-augmented generation (RAG).Announced at the CES trade show in January, NVIDIA AI Blueprints are advanced AI reference workflows built on NVIDIA NIM. With AI Blueprints, developers can create podcasts from PDF documents, generate stunning 4K images controlled and guided by 3D scenes, and incorporate digital humans into AI-powered use cases.Coming soon to build.nvidia.com, the blueprints are extensible and provide everything needed to build and customize them for different use cases. These resources include source code, sample data, a demo application and documentation.From cutting-edge hardware to optimized AI models and reference workflows, the RTX PRO series is redefining AI-powered computing enabling professionals to push the limits of creativity, productivity and innovation. Learn about all the GTC announcements and the RTX PRO Blackwell series GPUs for laptops and workstations.Create NIMble AI Chatbots With ChatRTXAI-powered chatbots are changing how people interact with their content.ChatRTX is a demo app that personalizes a LLM connected to a users content, whether documents, notes, images or other data. Using RAG, the NVIDIA TensorRT-LLM library and RTX acceleration, a user can query a custom chatbot to get contextually relevant answers. And because it all runs locally on Windows RTX PCs or RTX PRO workstations, they get fast and private results.Today, the latest version of ChatRTX introduces support for NVIDIA NIM microservices, giving users access to new foundational models. NIM is expected to soon be available in additional top AI ecosystem apps. Download ChatRTX today.Game OnHalf-Life 2 owners can now download a free Half-Life 2 RTX demo from Steam, built with RTX Remix and featuring the latest neural rendering enhancements. RTX Remix supports a host of AI tools, including NVIDIA DLSS 4, RTX Neural Radiance Cache and the new community-published AI model PBRFusion 3, which upscales textures and generates high-quality normal, roughness and height maps for physically based materials.The March NVIDIA Studio Driver is also now available for download, supporting recent app updates including last weeks RTX Remix launch. For automatic Studio Driver notifications, download the NVIDIA app.In addition, NVIDIA RTX Kit, a suite of neural rendering technologies for game developers, is receiving major updates with Unreal Engine 5 support for the RTX Mega Geometry and RTX Hair features.Learn more about the NVIDIA RTX PRO Blackwell GPUs by watching a replay of NVIDIA founder and CEO Jensen Huangs GTC keynote and register to attend sessions from NVIDIA and industry leaders at the show, which runs through March 21.Follow NVIDIA AI PC on Facebook, Instagram, TikTok and X and stay informed by subscribing to the RTX AI PC newsletter.Follow NVIDIA Workstation on LinkedIn and X.See notice regarding software product information.

Physical AI is unlocking new possibilities at the intersection of autonomy and robotics accelerating, in particular, the development of autonomous vehicles (AVs). The right technology and frameworks are crucial to ensuring the safety of drivers, passengers and pedestrians.Thats why NVIDIA today announced NVIDIA Halos a comprehensive safety system bringing together NVIDIAs lineup of automotive hardware and software safety solutions with its cutting-edge AI research in AV safety.Halos spans chips and software to tools and services to help ensure safe development of AVs from the cloud to the car, with a focus on AI-based, end-to-end AV stacks.With the launch of Halos, were empowering partners and developers to choose the state-of-the-art technology elements they need to build their own unique offerings, driving forward a shared mission to create safe and reliable autonomous vehicles, said Riccardo Mariani, vice president of industry safety at NVIDIA. Halos complements existing safety practices and can potentially accelerate standardization and regulatory compliance.At the Heart of HalosHalos is a holistic safety system on three different but complementary levels.At the technology level, it spans platform, algorithmic and ecosystem safety. At the development level, it includes design-time, deployment-time and validation-time guardrails. And at the computational level, it spans AI training to deployment, using three powerful computers NVIDIA DGX for AI training, NVIDIA Omniverse and NVIDIA Cosmos running on NVIDIA OVX for simulation, and NVIDIA DRIVE AGX for deployment.Halos holistic approach to safety is particularly critical in a setting where companies want to harness the power of generative AI for increasingly capable AV systems developed end to end, which preclude traditional compositional design and verification, said Marco Pavone, lead AV researcher at NVIDIA.AI Systems Inspection LabServing as an entry point to Halos is the NVIDIA AI Systems Inspection Lab, which allows automakers and developers to verify the safe integration of their products with NVIDIA technology.The AI Systems Inspection Lab, announced at the CES trade show earlier this year, is the first worldwide program to be accredited by the ANSI National Accreditation Board for an inspection plan integrating functional safety, cybersecurity, AI safety and regulations into a unified safety framework.Inaugural members of the AI Systems Inspection Lab include Ficosa, OMNIVISION, onsemi and Continental.Being a member of the AI Systems Inspection Lab means working at the forefront of automotive systems innovation and integrity, said Cristian Casorran Hontiyuelo, advanced driver-assistance system engineering and product manager at Ficosa.Cars are so much more than just transportation, said Paul Wu, head of product marketing for automotive at OMNIVISION. Theyve also become our entertainment and information hubs. Vehicles must continually evolve in their ability to keep us safe. We are pleased to join NVIDIAs new AI Systems Safety Lab as a demonstration of our commitment to achieving the highest levels of safety in our product offerings.We are delighted to be working with NVIDIA and included in the launch of the NVIDIA AI Systems Inspection Lab, said Geoff Ballew, general manager of the automotive sensing division at onsemi. This unique initiative will improve road safety in an innovative way. We look forward to the advancements it will bring.We are pleased to participate in the newly launched NVIDIA Drive AI Systems Inspection Lab and to further intensify the fruitful, ongoing collaboration between our two companies, said Nobert Hammerschmidt, head of components business at Continental.Key Elements of HalosHalos is built on three focus areas: platform safety, algorithmic safety and ecosystem safety.Platform SafetyHalos features a safety-assessed system-on-a-chip (SoC) with hundreds of built-in safety mechanisms.It also includes NVIDIA DriveOS software, a safety-certified operating system that extends from CPU to GPU; a safety-assessed base platform that delivers the foundational computer needed to enable safe systems for all types of applications; and DRIVE AGX Hyperion, a hardware platform that connects SoC, DriveOS and sensors in an electronic control unit architecture.Algorithmic SafetyHalos includes libraries for safety data loading and accelerators, and application programming interfaces for safety data creation, curation and reconstruction to filter out, for example, undesirable behaviors and biases before training.It also features rich training, simulation and validation environments harnessing the NVIDIA Omniverse Blueprint for AV simulation with NVIDIA Cosmos world foundation models to train, test and validate AVs. In addition, it boasts a diverse AV stack combining modular components with end-to-end AI models to ensure safety with cutting-edge AI models in the loop.Ecosystem SafetyHalos includes safety datasets with diverse, unbiased data, as well as safe deployment workflows, comprising triaging workflows and automated safety evaluations, along with a data flywheel for continual safety improvements demonstrating leadership in AV safety standardization and regulation.Safety Track RecordHalos brings together a vast amount of safety-focused technology research, development, deployment, partnerships and collaborations by NVIDIA, including:15,000+ engineering years invested in vehicle safety10,000+ hours of contributions to international standards committees1,000+ AV-safety patents filed240+ AV-safety research papers published30+ safety and cybersecurity certificatesIt also dovetails with recent significant safety certifications and assessments of NVIDIA automotive products, including:The NVIDIA DriveOS 6.0 operating system conforms with ISO 26262 automotive safety integrity level (ASIL D) standards.TV SD granted the ISO/SAE 21434 Cybersecurity Process certification to NVIDIA for its automotive SoC, platform and software engineering processes.TV Rheinland performed an independent safety assessment of NVIDIA DRIVE AV for the United Nations Economic Commission for Europe related to safety requirements for complex electronic systems.To learn more about NVIDIAs approach to automotive safety, attend AV Safety Day today at NVIDIA GTC, a global AI conference running through Friday, March 21.See notice regarding software product information.

AI is now mainstream and driving unprecedented demand for AI factories purpose-built infrastructure dedicated to AI training and inference and the production of intelligence.Many of these AI factories will be gigawatt-scale. Bringing up a single gigawatt AI factory is an extraordinary act of engineering and logistics requiring tens of thousands of workers across suppliers, architects, contractors and engineers to build, ship and assemble nearly 5 billion components and over 210,000 miles of fiber cable.To help design and optimize these AI factories, NVIDIA today unveiled at GTC the NVIDIA Omniverse Blueprint for AI factory design and operations.During his GTC keynote, NVIDIA founder and CEO Jensen Huang showcased how NVIDIAs data center engineering team developed an application on the Omniverse Blueprint to plan, optimize and simulate a 1 gigawatt AI factory. Connected to leading simulation tools such as Cadence Reality Digital Twin Platform and ETAP, the engineering teams can test and optimize power, cooling and networking long before construction starts.Engineering AI Factories: A Simulation-First ApproachThe NVIDIA Omniverse Blueprint for AI factory design and operations uses OpenUSD libraries that enable developers to aggregate 3D data from disparate sources such as the building itself, NVIDIA accelerated computing systems and power or cooling units from providers such as Schneider Electric and Vertiv.By unifying the design and simulation of billions of components, the blueprint helps engineers address complex challenges like:Component integration and space optimization Unifying the design and simulation of NVIDIA DGX SuperPODs, GB300 NVL72 systems and their 5 billion components.Cooling system performance and efficiency Using Cadence Reality Digital Twin Platform, accelerated by NVIDIA CUDA and Omniverse libraries, to simulate and evaluate hybrid air- and liquid-cooling solutions from Vertiv and Schneider Electric.Power distribution and reliability Designing scalable, redundant electrical systems with ETAP to simulate power-block efficiency and reliability.Networking topology and logic Fine-tuning high-bandwidth infrastructure with NVIDIA Spectrum-X networking and the NVIDIA Air platform.Breaking Down Engineering Silos With OmniverseOne of the biggest challenges in AI factory construction is that different teams power, cooling and networking operate in silos, leading to inefficiencies and potential failures.Using the blueprint, engineers can now:Collaborate in full context Multiple disciplines can iterate in parallel, sharing live simulations that reveal how changes in one domain affect another.Optimize energy usage Real-time simulation updates enable teams to find the most efficient designs for AI workloads.Eliminate failure points By validating redundancy configurations before deployment, organizations reduce the risk of costly downtime.Model real-world conditions Predict and test how different AI workloads will impact cooling, power stability and network congestion.By integrating real-time simulation across disciplines, the blueprint allows engineering teams to explore various configurations to model cost of ownership and optimize power utilization.Real-Time Simulations for Faster Decision-MakingIn Huangs demo, engineers adjust AI factory configurations in real time and instantly see the impact.For example, a small tweak in cooling layout significantly improved efficiency a detail that could have been missed on paper. And instead of waiting hours for simulation results, teams could test and refine strategies in just seconds.Once an optimal design was finalized, Omniverse streamlined communication with suppliers and construction teams ensuring that what gets built matches the model, down to the last detail.Future-Proofing AI FactoriesAI workloads arent static. The next wave of AI applications will push power, cooling and networking demands even further. The Omniverse Blueprint for AI factory design and operations helps ensure AI factories are ready by offering:Workload-aware simulation Predict how changes in AI workloads will affect power and cooling at data center scale.Failure scenario testing Model grid failures, cooling leaks and power spikes to ensure resilience.Scalable upgrades Plan for AI factory expansions and estimate infrastructure needs years ahead.And when planning for retrofits and upgrades, users can easily test and simulate cost and downtime delivering a future-proof AI factory.For AI factory operators, staying ahead isnt just about efficiency its about preventing infrastructure failures that could cost millions of dollars per day.For a 1 gigawatt AI factory, every day of downtime can cost over $100 million. By solving infrastructure challenges in advance, the blueprint reduces both risk and time to deployment.Road to Agentic AI for AI Factory OperationNVIDIA is working on the next evolution of the blueprint to expand into AI-enabled operations, working with key companies such as Vertech and Phaidra.Vertech is collaborating with the NVIDIA data center engineering team on NVIDIAs advanced AI factory control system, which integrates IT and operational technology data to enhance resiliency and operational visibility.Phaidra is working with NVIDIA to integrate reinforcement-learning AI agents into Omniverse. These agents optimize thermal stability and energy efficiency through real-time scenario simulation, creating digital twins that continuously adapt to changing hardware and environmental conditions.The AI Data Center BoomAI is reshaping the global data center landscape. With $1 trillion projected for AI-driven data center upgrades, digital twin technology is no longer optional its essential.The NVIDIA Omniverse Blueprint for AI factory design and operations is poised to help NVIDIA and its ecosystem of partners lead this transformation letting AI factory operators stay ahead of ever-evolving AI workloads, minimize downtime and maximize efficiency.Learn more about NVIDIA Omniverse, watch the GTC keynote, register for Cadences GTC session to see the Omniverse Blueprint in action and read more about AI factories.See notice regarding software product information.

Under the hood of every AI application are algorithms that churn through data in their own language, one based on a vocabulary of tokens.Tokens are tiny units of data that come from breaking down bigger chunks of information. AI models process tokens to learn the relationships between them and unlock capabilities including prediction, generation and reasoning. The faster tokens can be processed, the faster models can learn and respond.AI factories a new class of data centers designed to accelerate AI workloads efficiently crunch through tokens, converting them from the language of AI to the currency of AI, which is intelligence.With AI factories, enterprises can take advantage of the latest full-stack computing solutions to process more tokens at lower computational cost, creating additional value for customers. In one case, integrating software optimizations and adopting the latest generation NVIDIA GPUs reduced cost per token by 20x compared to unoptimized processes on previous-generation GPUs delivering 25x more revenue in just four weeks.By efficiently processing tokens, AI factories are manufacturing intelligence the most valuable asset in the new industrial revolution powered by AI.What Is Tokenization?Whether a transformer AI model is processing text, images, audio clips, videos or another modality, it will translate the data into tokens. This process is known as tokenization.Efficient tokenization helps reduce the amount of computing power required for training and inference. There are numerous tokenization methods and tokenizers tailored for specific data types and use cases can require a smaller vocabulary, meaning there are fewer tokens to process.For large language models (LLMs), short words may be represented with a single token, while longer words may be split into two or more tokens.The word darkness, for example, would be split into two tokens, dark and ness, with each token bearing a numerical representation, such as 217 and 655. The opposite word, brightness, would similarly be split into bright and ness, with corresponding numerical representations of 491 and 655.In this example, the shared numerical value associated with ness can help the AI model understand that the words may have something in common. In other situations, a tokenizer may assign different numerical representations for the same word depending on its meaning in context.For example, the word lie could refer to a resting position or to saying something untruthful. During training, the model would learn the distinction between these two meanings and assign them different token numbers.For visual AI models that process images, video or sensor data, a tokenizer can help map visual inputs like pixels or voxels into a series of discrete tokens.Models that process audio may turn short clips into spectrograms visual depictions of sound waves over time that can then be processed as images. Other audio applications may instead focus on capturing the meaning of a sound clip containing speech, and use another kind of tokenizer that captures semantic tokens, which represent language or context data instead of simply acoustic information.How Are Tokens Used During AI Training?Training an AI model starts with the tokenization of the training dataset.Based on the size of the training data, the number of tokens can number in the billions or trillions and, per the pretraining scaling law, the more tokens used for training, the better the quality of the AI model.As an AI model is pretrained, its tested by being shown a sample set of tokens and asked to predict the next token. Based on whether or not its prediction is correct, the model updates itself to improve its next guess. This process is repeated until the model learns from its mistakes and reaches a target level of accuracy, known as model convergence.After pretraining, models are further improved by post-training, where they continue to learn on a subset of tokens relevant to the use case where theyll be deployed. These could be tokens with domain-specific information for an application in law, medicine or business or tokens that help tailor the model to a specific task, like reasoning, chat or translation. The goal is a model that generates the right tokens to deliver a correct response based on a users query a skill better known as inference.How Are Tokens Used During AI Inference and Reasoning?During inference, an AI receives a prompt which, depending on the model, may be text, image, audio clip, video, sensor data or even gene sequence that it translates into a series of tokens. The model processes these input tokens, generates its response as tokens and then translates it to the users expected format.Input and output languages can be different, such as in a model that translates English to Japanese, or one that converts text prompts into images.To understand a complete prompt, AI models must be able to process multiple tokens at once. Many models have a specified limit, referred to as a context window and different use cases require different context window sizes.A model that can process a few thousand tokens at once might be able to process a single high-resolution image or a few pages of text. With a context length of tens of thousands of tokens, another model might be able to summarize a whole novel or an hourlong podcast episode. Some models even provide context lengths of a million or more tokens, allowing users to input massive data sources for the AI to analyze.Reasoning AI models, the latest advancement in LLMs, can tackle more complex queries by treating tokens differently than before. Here, in addition to input and output tokens, the model generates a host of reasoning tokens over minutes or hours as it thinks about how to solve a given problem.These reasoning tokens allow for better responses to complex questions, just like how a person can formulate a better answer given time to work through a problem. The corresponding increase in tokens per prompt can require over 100x more compute compared with a single inference pass on a traditional LLM an example of test-time scaling, aka long thinking.How Do Tokens Drive AI Economics?During pretraining and post-training, tokens equate to investment into intelligence, and during inference, they drive cost and revenue. So as AI applications proliferate, new principles of AI economics are emerging.AI factories are built to sustain high-volume inference, manufacturing intelligence for users by turning tokens into monetizable insights. Thats why a growing number of AI services are measuring the value of their products based on the number of tokens consumed and generated, offering pricing plans based on a models rates of token input and output.Some token pricing plans offer users a set number of tokens shared between input and output. Based on these token limits, a customer could use a short text prompt that uses just a few tokens for the input to generate a lengthy, AI-generated response that took thousands of tokens as the output. Or a user could spend the majority of their tokens on input, providing an AI model with a set of documents to summarize into a few bullet points.To serve a high volume of concurrent users, some AI services also set token limits, the maximum number of tokens per minute generated for an individual user.Tokens also define the user experience for AI services. Time to first token, the latency between a user submitting a prompt and the AI model starting to respond, and inter-token or token-to-token latency, the rate at which subsequent output tokens are generated, determine how an end user experiences the output of an AI application.There are tradeoffs involved for each metric, and the right balance is dictated by use case.For LLM-based chatbots, shortening the time to first token can help improve user engagement by maintaining a conversational pace without unnatural pauses. Optimizing inter-token latency can enable text generation models to match the reading speed of an average person, or video generation models to achieve a desired frame rate. For AI models engaging in long thinking and research, more emphasis is placed on generating high-quality tokens, even if it adds latency.Developers have to strike a balance between these metrics to deliver high-quality user experiences with optimal throughput, the number of tokens an AI factory can generate.To address these challenges, the NVIDIA AI platform offers a vast collection of software, microservices and blueprints alongside powerful accelerated computing infrastructure a flexible, full-stack solution that enables enterprises to evolve, optimize and scale AI factories to generate the next wave of intelligence across industries.Understanding how to optimize token usage across different tasks can help developers, enterprises and even end users reap the most value from their AI applications.Learn more in this ebook and get started at build.nvidia.com.

Whats next in AI is at GTC 2025. Not only the technology, but the people and ideas that are pushing AI forward creating new opportunities, novel solutions and whole new ways of thinking. For all of that, this is the place.Heres where to find the news, hear the discussions, see the robots and ponder the just-plain mind-blowing. From the keynote to the final session, check back for live coverage kicking off when the doors open on Monday, March 17, in San Jose, California.The Future Rolls Into San JoseAnyone whos been in downtown San Jose lately has seen it happening. The banners are up. The streets are shifting. The whole city is getting a fresh coat of NVIDIA green.From March 17-21, San Jose will become a crossroads for the thinkers, tinkerers and true enthusiasts of AI, robotics and accelerated computing. The conversations will be sharp, fast-moving and sometimes improbable but thats the point.At the center of it all? NVIDIA founder and CEO Jensen Huangs keynote, offering a glimpse into the future. Itll take place at the SAP Center on Tuesday, March 18, at 10 a.m. PT. Expect big ideas, a few surprises, some roars of laughter and the occasional moment that leaves the room silent.But GTC isnt just what happens on stage. Its a conference that refuses to stay inside its walls. It spills out into sessions at McEnery Convention Center, hands-on demos at the Tech Interactive Museum, late-night conversations at the Plaza de CsarChvez night market and more. San Jose isnt just hosting GTC. Its becoming it.The speakers are a mix of visionaries and builders the kind of people who make you rethink whats possible: Yann LeCun chief AI scientist at Meta, professor, New York University Frances Arnold Nobel Laureate, Caltech RJ Scaringe founder and CEO of Rivian Pieter Abbeel robotics pioneer, UC Berkeley Arthur Mensch CEO of Mistral AI Joe Park chief digital and technology officer of Yum! Brands Noam Brown research scientist at OpenAISome are pushing the limits of AI itself; others are weaving it into the world around us. Want in? Register now.Check back here for what to watch, read and play and what it all means. Tune in to all the big moments, the small surprises and the ideas thatll stick for years to come.See you in San Jose. #GTC25

Bundle up GeForce NOW is bringing a flurry of Blizzard titles to its ever-expanding library.Prepare to weather epic gameplay in the cloud, tackling the genres of real-time strategy (RTS), multiplayer online battle arena (MOBA) and more. Classic Blizzard titles join GeForce NOW, including Heroes of the Storm, Warcraft Rumble and three titles from the Warcraft: Remastered series.Theyre all part of 11 games joining the cloud this week, atop the latest update for hit game Zenless Zone Zero from miHoYo.Blizzard Heats Things UpHeroes (and save data) never die in the cloud.Heroes of the Storm, Blizzards unique take on the MOBA genre, offers fast-paced team battles across diverse battlegrounds. The game features a roster of iconic Blizzard franchise characters, each with customizable talents and abilities. Heroes of the Storm emphasizes team-based gameplay with shared experiences and objectives, making it more accessible to newcomers while providing depth for experienced players.The cloud is rumbling.In Warcraft Rumble, a mobile action-strategy game set in the Warcraft universe, players collect and deploy miniature versions of the series beloved characters. The game offers a blend of tower defense and RTS elements as players battle across various modes, including a single-player campaign, player vs. player matches and cooperative dungeons.Old-school cool, new-school graphics.The Warcraft Remastered collection gives the classic RTS titles a modern twist with updated visuals and quality-of-life improvements. Warcraft: Remastered and Warcraft II: Remastered offer enhanced graphics while maintaining the original gameplay, allowing players to toggle between classic and updated visuals. Warcraft III: Reforged includes new graphics options and multiplayer features. Both these remasters provide nostalgia for long-time fans and an ideal opportunity for new players to experience the iconic strategy games that shaped the genre.New Games, No WaitNew agents, new adventures.The popular Zenless Zone Zero gets its 1.6 update, Among the Forgotten Ruins, now available for members to stream without waiting around for updates or downloads. This latest update brings three new playable agents: Soldier 0-Anby, Pulchra and Trigger. Players can explore two new areas, Port Elpis and Reverb Arena, as well as try out the Hollow Zero-Lost Void mode. The update also introduces a revamped Decibel system for more strategic gameplay.Look for the following games available to stream in the cloud this week:Citizen Sleeper 2: Starward Vector (Xbox, available on PC Game Pass)City Transport Simulator: Tram (Steam)Dave the Diver (Steam)Heroes of the Storm (Battle.net)Microtopia (Steam)Orcs Must Die Deathtrap (Xbox, available on PC Game Pass)Potion Craft: Alchemist Simulator (Steam)Warcraft I Remastered (Battle.net)Warcraft II Remastered (Battle.net)Warcraft III: Reforged (Battle.net)Warcraft Rumble (Battle.net)What are you planning to play this weekend? Let us know on X or in the comments below.You wake up in the last game you played how are things going? NVIDIA GeForce NOW (@NVIDIAGFN) March 11, 2025

AI is leveling up the worlds most beloved games, as the latest advancements in neural rendering, NVIDIA RTX and digital human technologies equip game developers to take innovative leaps in their work.At this years GDC conference, running March 17-21 in San Francisco, NVIDIA is revealing new AI tools and technologies to supercharge the next era of graphics in games.Key announcements include new neural rendering advancements with Unreal Engine 5 and Microsoft DirectX; NVIDIA DLSS 4 now available in over 100 games and apps, making it the most rapidly adopted NVIDIA game technology of all time; and a Half-Life 2 RTX demo coming Tuesday, March 18.Plus, the open-source NVIDIA RTX Remix modding platform has now been released, and NVIDIA ACE technology enhancements are bringing to life next-generation digital humans and AI agents for games.Neural Shaders Enable Photorealistic, Living Worlds With AIThe next era of computer graphics will be based on NVIDIA RTX Neural Shaders, which allow the training and deployment of tiny neural networks from within shaders to generate textures, materials, lighting, volumes and more. This results in dramatic improvements in game performance, image quality and interactivity, delivering new levels of immersion for players.At the CES trade show earlier this year, NVIDIA introduced RTX Kit, a comprehensive suite of neural rendering technologies for building AI-enhanced, ray-traced games with massive geometric complexity and photorealistic characters.Now, at GDC, NVIDIA is expanding its powerful lineup of neural rendering technologies, including with Microsoft DirectX support and plug-ins for Unreal Engine 5.NVIDIA is partnering with Microsoft to bring neural shading support to the DirectX 12 Agility software development kit preview in April, providing game developers with access to RTX Tensor Cores to accelerate the performance of applications powered by RTX Neural Shaders.Plus, Unreal Engine developers will be able to get started with RTX Kit features such as RTX Mega Geometry and RTX Hair through the experimental NVIDIA RTX branch of Unreal Engine 5. These enable the rendering of assets with dramatic detail and fidelity, bringing cinematic-quality visuals to real-time experiences.Now available, NVIDIAs Zorah technology demo has been updated with new incredibly detailed scenes filled with millions of triangles, complex hair systems and cinematic lighting in real time all by tapping into the latest technologies powering neural rendering, including:ReSTIR Path TracingReSTIR Direct IlluminationRTX Mega GeometryRTX HairAnd the first neural shader, Neural Radiance Cache, is now available in RTX Remix.Over 100 DLSS 4 Games and Apps Out NowDLSS 4 debuted with the release of GeForce RTX 50 Series GPUs. Over 100 games and apps now feature support for DLSS 4. This milestone has been reached two years quicker than with DLSS 3, making DLSS 4 the most rapidly adopted NVIDIA game technology of all time.DLSS 4 introduced Multi Frame Generation, which uses AI to generate up to three additional frames per traditionally rendered frame, working with the complete suite of DLSS technologies to multiply frame rates by up to 8x over traditional brute-force rendering.This massive performance improvement on GeForce RTX 50 Series graphics cards and laptops enables gamers to max out visuals at the highest resolutions and play at incredible frame rates.In addition, Lost Soul Aside, Mecha BREAK, Phantom Blade Zero, Stellar Blade, Tides of Annihilation and Wild Assault will launch with DLSS 4, giving GeForce RTX gamers the definitive PC experience in each title. Learn more.Developers can get started with DLSS 4 through the DLSS 4 Unreal Engine plug-in.Half-Life 2 RTX Demo Launch, RTX Remix Official ReleaseHalf-Life 2 RTX is a community-made remaster of the iconic first-person shooter Half-Life 2.A playable Half-Life 2 RTX demo will be available on Tuesday, March 18, for free download from Steam for Half-Life 2 owners. The demo showcases Orbifold Studios work in the eerily sensational maps of Ravenholm and Nova Prospekt, with significantly improved assets and textures, full ray tracing, DLSS 4 with Multi Frame Generation and RTX neural rendering technologies.Half-Life 2 RTX was made possible by NVIDIA RTX Remix, an open-source platform officially released today for modders to create stunning RTX remasters of classic games.Use the platform now to join the 30,000+ modders whove experimented with enhancing hundreds of classic titles since its beta release last year, enabling over 1 million gamers to experience astonishing ray-traced mods.NVIDIA ACE Technologies Enhance Game Characters With AIThe NVIDIA ACE suite of RTX-accelerated digital human technologies brings game characters to life with generative AI.NVIDIA ACE autonomous game characters add autonomous teammates, nonplayer characters (NPCs) and self-learning enemies to games, creating new narrative possibilities and enhancing player immersion.ACE autonomous game characters are debuting in two titles this month:In inZOI, Smart Zoi NPCs will respond more realistically and intelligently to their environment based on their personalities. The game launches with NVIDIA ACE-based characters on Friday, March 28.And in NARAKA: BLADEPOINT MOBILE PC VERSION, on-device NVIDIA ACE-powered teammates will help players battle enemies, hunt for loot and fight for victory starting Thursday, March 27.Developers can start building with ACE today.Join NVIDIA at GDC.See notice regarding software product information.

PC game modding is massive, with over 5 billion mods downloaded annually. Mods push graphics forward with each GPU generation, extend a games lifespan with new content and attract new players.NVIDIA RTX Remix is a modding platform for RTX AI PCs that lets modders capture game assets, automatically enhance materials with generative AI tools and create stunning RTX remasters with full ray tracing. Today, RTX Remix exited beta and fully launched with new NVIDIA GeForce RTX 50 Series neural rendering technology and many community-requested upgrades.Since its initial beta release, RTX Remix has been experimented with by over 30,000 modders, bringing ray-traced mods of hundreds of classic titles to over 1 million gamers.RTX Remix supports a host of AI tools, including NVIDIA DLSS 4, RTX Neural Radiance Cache and the community-published AI model PBRFusion 3.Modders can build 4K physically based rendering (PBR) assets by hand or use generative AI to accelerate their workflows. And with a few additional clicks, RTX Remix mods support DLSS 4 with Multi Frame Generation. DLSS new transformer model and the first neural shader, Neural Radiance Cache, provide enhanced neural rendering performance, meaning classic games look and play better than ever.Generative AI Texture ToolsRTX Remixs built-in generative AI texture tools analyze low-resolution textures from classic games, generate physically accurate materials including normal and roughness maps and upscale the resolution by up to 4x. Many RTX Remix mods have been created incorporating generative AI.Earlier this month, RTX Remix modder NightRaven published PBRFusion 3 a new AI model that upscales textures and generates high-quality normal, roughness and height maps for physically-based materials.PBRFusion 3 consists of two custom-trained models: a PBR model and a diffusion-based upscaler. PBRFusion 3 can also use the RTX Remix application programming interface to connect with ComfyUI in an integrated flow. NightRaven has packaged all the relevant pieces to make it easy to get started.The PBRFusion3 page features a plug-and-play package that includes the relevant ComfyUI graphs and nodes. Once installed, remastering is easy. Select a number of textures in RTX Remixs Viewport and hit process in ComfyUI. This integrated flow enables extensive remasters of popular games to be completed by small hobbyist mod teams.RTX Remix and REST APIRTX Remix Toolkit capabilities are accessible via REST API, allowing modders to livelink RTX Remix to digital content creation tools such as Blender, modding tools such as Hammer and generative AI apps such as ComfyUI.For example, through REST API integration, modders can seamlessly export all game textures captured in RTX Remix to ComfyUI and enhance them in one big batch before automatically bringing them back into the game. ComfyUI is RTX-accelerated and includes thousands of generative AI models to try, helping reduce the time to remaster a game scene and providing many ways to process textures.Modders have many super resolution and PBR models to choose from, including ones that feature metallic and height maps unlocking 8x or more resolution increases. Additionally, ComfyUI enables modders to use text prompts to generate new details in textures, or make grand stylistic departures by changing an entire scenes look with a single text prompt.Half-Life 2 RTX DemoHalf-Life 2 owners can download a free Half-Life 2 RTX demo from Steam, built with RTX Remix, starting March 18. The demo showcases Orbifold Studios work in Ravenholm and Nova Prospekt ahead of the full games release at a later date.Half-Life 2 RTX showcases the expansive capabilities of RTX Remix and NVIDIAs neural rendering technologies. DLSS 4 with Multi Frame Generation multiplies frame rates by up to 10x at 4K. Neural Radiance Cache further accelerates ray-traced lighting. RTX Skin enhances Father Grigori, headcrabs and zombies with one of the first implementations of subsurface scattering in ray-traced gaming. RTX Volumetrics add realistic smoke effects and fog. And everything interplays and interacts with the fully ray-traced lighting.Whats Next in AI Starts HereFrom the keynote by NVIDIA founder and CEO Jensen Huang on Tuesday, March 18, to over 1,000 inspiring sessions, 300+ exhibits, technical hands-on training and tons of unique networking events NVIDIAs own GTC is set to put a spotlight on AI and all its benefits.Experts from across the AI ecosystem will share insights on deploying AI locally, optimizing models and harnessing cutting-edge hardware and software to enhance AI workloads highlighting key advancements in RTX AI PCs and workstations. RTX AI Garage will be there to share highlights of the latest advancements coming to the RTX AI platform.Follow NVIDIA AI PC on Facebook, Instagram, TikTok and X and stay informed by subscribing to the RTX AI PC newsletter

Re @DylserX @maingear Absolutely stunning!