NVIDIA Announces Nemotron Model Families to Advance Agentic AI​on January 7, 2025 at 2:30 am

Artificial intelligence is entering a new era — agentic AI — where teams of specialized agents can help people solve complex problems and automate repetitive tasks. With custom AI agents, enterprises across industries can manufacture intelligence and achieve unprecedented productivity. These advanced AI agents require a system of multiple generative AI models optimized for agentic
Read ArticleArtificial intelligence is entering a new era — agentic AI — where teams of specialized agents can help people solve complex problems and automate repetitive tasks. With custom AI agents, enterprises across industries can manufacture intelligence and achieve unprecedented productivity. These advanced AI agents require a system of multiple generative AI models optimized for agentic
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Artificial intelligence is entering a new era — agentic AI — where teams of specialized agents can help people solve complex problems and automate repetitive tasks.

With custom AI agents, enterprises across industries can manufacture intelligence and achieve unprecedented productivity. These advanced AI agents require a system of multiple generative AI models optimized for agentic AI functions and capabilities. This complexity means that the need for powerful, efficient, enterprise-grade models has never been greater.

To provide a foundation for enterprise agentic AI, NVIDIA today announced the Llama Nemotron family of open large language models (LLMs). Built with Llama, the models can help developers create and deploy AI agents across a range of applications — including customer support, fraud detection, and product supply chain and inventory management optimization.

To be effective, many AI agents need both language skills and the ability to perceive the world and respond with the appropriate action.

With new NVIDIA Cosmos Nemotron vision language models (VLMs) and NVIDIA NIM microservices for video search and summarization, developers can build agents that analyze and respond to images and video from autonomous machines, hospitals, stores and warehouses, as well as sports events, movies and news. For developers seeking to generate physics-aware videos for robotics and autonomous vehicles, NVIDIA today separately announced NVIDIA Cosmos world foundation models.

Open Llama Nemotron Models Optimize Compute Efficiency, Accuracy for AI Agents

Built with Llama foundation models — one of the most popular commercially viable open-source model collections, downloaded over 650 million times — NVIDIA Llama Nemotron models provide optimized building blocks for AI agent development. This builds on NVIDIA’s commitment to developing state-of-the-art models, such as Llama 3.1 Nemotron 70B, now available through the NVIDIA API catalog.

Llama Nemotron models are pruned and trained with NVIDIA’s latest techniques and high-quality datasets for enhanced agentic capabilities. They excel at instruction following, chat, function calling, coding and math, while being size-optimized to run on a broad range of NVIDIA accelerated computing resources.

“Agentic AI is the next frontier of AI development, and delivering on this opportunity requires full-stack optimization across a system of LLMs to deliver efficient, accurate AI agents,” said Ahmad Al-Dahle, vice president and head of GenAI at Meta. “Through our collaboration with NVIDIA and our shared commitment to open models, the NVIDIA Llama Nemotron family built on Llama can help enterprises quickly create their own custom AI agents.”

Leading AI agent platform providers including SAP and ServiceNow are expected to be among the first to use the new Llama Nemotron models.

“AI agents that collaborate to solve complex tasks across multiple lines of the business will unlock a whole new level of enterprise productivity beyond today’s generative AI scenarios,” said Philipp Herzig, chief AI officer at SAP. “Through SAP’s Joule, hundreds of millions of enterprise users will interact with these agents to accomplish their goals faster than ever before. NVIDIA’s new open Llama Nemotron model family will foster the development of multiple specialized AI agents to transform business processes.”

“AI agents make it possible for organizations to achieve more with less effort, setting new standards for business transformation,” said Jeremy Barnes, vice president of platform AI at ServiceNow. “The improved performance and accuracy of NVIDIA’s open Llama Nemotron models can help build advanced AI agent services that solve complex problems across functions, in any industry.”

The NVIDIA Llama Nemotron models use NVIDIA NeMo for distilling, pruning and alignment. Using these techniques, the models are small enough to run on a variety of computing platforms while providing high accuracy as well as increased model throughput.

The Llama Nemotron model family will be available as downloadable models and as NVIDIA NIM microservices that can be easily deployed on clouds, data centers, PCs and workstations. They offer enterprises industry-leading performance with reliable, secure and seamless integration into their agentic AI application workflows.

Customize and Connect to Business Knowledge With NVIDIA NeMo

The Llama Nemotron and Cosmos Nemotron model families are coming in Nano, Super and Ultra sizes to provide options for deploying AI agents at every scale.

• Nano: The most cost-effective model optimized for real-time applications with low latency, ideal for deployment on PCs and edge devices.
• Super: A high-accuracy model offering exceptional throughput on a single GPU.
• Ultra: The highest-accuracy model, designed for data-center-scale applications demanding the highest performance.

Enterprises can also customize the models for their specific use cases and domains with NVIDIA NeMo microservices to simplify data curation, accelerate model customization and evaluation, and apply guardrails to keep responses on track.

With NVIDIA NeMo Retriever, developers can also integrate retrieval-augmented generation capabilities to connect models to their enterprise data.

And using NVIDIA Blueprints for agentic AI, enterprises can quickly create their own applications using NVIDIA’s advanced AI tools and end-to-end development expertise. In fact, NVIDIA Cosmos Nemotron, NVIDIA Llama Nemotron and NeMo Retriever supercharge the new NVIDIA Blueprint for video search and summarization, announced separately today.

NeMo, NeMo Retriever and NVIDIA Blueprints are all available with the NVIDIA AI Enterprise software platform.

Availability

Llama Nemotron and Cosmos Nemotron models will be available soon as hosted application programming interfaces and for download on build.nvidia.com and Hugging Face. Access for development, testing and research is free for members of the NVIDIA Developer Program.

Enterprises can run Llama Nemotron and Cosmos Nemotron NIM microservices in production with the NVIDIA AI Enterprise software platform on accelerated data center and cloud infrastructure.

Sign up to get notified about Llama Nemotron and Cosmos Nemotron models, and join NVIDIA at CES.

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Artificial intelligence is entering a new era — agentic AI — where teams of specialized agents can help people solve complex problems and automate repetitive tasks. With custom AI agents, enterprises across industries can manufacture intelligence and achieve unprecedented productivity. These advanced AI agents require a system of multiple generative AI models optimized for agentic
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New GeForce RTX 50 Series GPUs Double Creative Performance in 3D, Video and Generative AI​on January 7, 2025 at 2:30 am

GeForce RTX 50 Series Desktop and Laptop GPUs, unveiled today at the CES trade show, are poised to power the next era of generative and agentic AI content creation — offering new tools and capabilities for video, livestreaming, 3D and more. Built on the NVIDIA Blackwell architecture, GeForce RTX 50 Series GPUs can run creative
Read ArticleGeForce RTX 50 Series Desktop and Laptop GPUs, unveiled today at the CES trade show, are poised to power the next era of generative and agentic AI content creation — offering new tools and capabilities for video, livestreaming, 3D and more. Built on the NVIDIA Blackwell architecture, GeForce RTX 50 Series GPUs can run creative
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GeForce RTX 50 Series Desktop and Laptop GPUs, unveiled today at the CES trade show, are poised to power the next era of generative and agentic AI content creation — offering new tools and capabilities for video, livestreaming, 3D and more.

Built on the NVIDIA Blackwell architecture, GeForce RTX 50 Series GPUs can run creative generative AI models up to 2x faster in a smaller memory footprint, compared with the previous generation. They feature ninth-generation NVIDIA encoders for advanced video editing and livestreaming, and come with NVIDIA DLSS 4 and up to 32GB of VRAM  to tackle massive 3D projects.

These GPUs come with various software updates, including two new AI-powered NVIDIA Broadcast effects, updates to RTX Video and RTX Remix, and NVIDIA NIM microservices — prepackaged and optimized models built to jumpstart AI content creation workflows on RTX AI PCs.

Built for the Generative AI Era

Generative AI can create sensational results for creators, but with models growing in both complexity and scale, generative AI can be difficult to run even on the latest hardware.

The GeForce RTX 50 Series adds FP4 support to help address this issue. FP4 is a lower quantization method, similar to file compression, that decreases model sizes. Compared with FP16 — the default method that most models feature — FP4 uses less than half of the memory and 50 Series GPUs provide over 2x performance compared to the previous generation. This can be done with virtually no loss in quality with advanced quantization methods offered by NVIDIA TensorRT Model Optimizer.

For example, Black Forest Labs’ FLUX.1 [dev] model at FP16 requires over 23GB of VRAM, meaning it can only be supported by the GeForce RTX 4090 and professional GPUs. With FP4, FLUX.1 [dev] requires less than 10GB, so it can run locally on more GeForce RTX GPUs.

With a GeForce RTX 4090 with FP16, the FLUX.1 [dev] model can generate images in 15 seconds with 30 steps. With a GeForce RTX 5090 with FP4, images can be generated in just over five seconds.

A new NVIDIA AI Blueprint for 3D-guided generative AI based on FLUX.1 [dev], which will be offered as an NVIDIA NIM microservice, offers artists greater control over text-based image generation. With this blueprint, creators can use simple 3D objects — created by hand or generated with AI — and lay them out in a 3D renderer like Blender to guide AI image generation.

A prepackaged workflow powered by the FLUX NIM microservice and ComfyUI can then generate high-quality images that match the 3D scene’s composition.

The NVIDIA Blueprint for 3D-guided generative AI is expected to be available through GitHub using a one-click installer in February.

Stability AI announced that its Stable Point Aware 3D, or SPAR3D, model will be available this month on RTX AI PCs. Thanks to RTX acceleration, the new model from Stability AI will help transform 3D design, delivering exceptional control over 3D content creation by enabling real-time editing and the ability to generate an object in less than a second from a single image.

Professional-Grade Video for All

GeForce RTX 50 Series GPUs deliver a generational leap in NVIDIA encoders and decoders with support for the 4:2:2 pro-grade color format, multiview-HEVC (MV-HEVC) for 3D and virtual reality (VR) video, and the new AV1 Ultra High Quality mode.

Most consumer cameras are confined to 4:2:0 color compression, which reduces the amount of color information. 4:2:0 is typically sufficient for video playback on browsers, but it can’t provide the color depth needed for advanced video editors to color grade videos. The 4:2:2 format provides double the color information with just a 1.3x increase in RAW file size — offering an ideal balance for video editing workflows.

Decoding 4:2:2 video can be challenging due to the increased file sizes. GeForce RTX 50 Series GPUs include 4:2:2 hardware support that can decode up to eight times the 4K 60 frames per second (fps) video sources per decoder, enabling smooth multi-camera video editing.

The GeForce RTX 5090 GPU is equipped with three encoders and two decoders, the GeForce RTX 5080 GPU includes two encoders and two decoders, the 5070 Ti GPUs has two encoders with a single decoder, and the GeForce RTX 5070 GPU includes a single encoder and decoder. These multi-encoder and decoder setups, paired with faster GPUs, enable the GeForce RTX 5090 to export video 60% faster than the GeForce RTX 4090 and at 4x speed compared with the GeForce RTX 3090.

GeForce RTX 50 Series GPUs also feature the ninth-generation NVIDIA video encoder, NVENC, that offers a 5% improvement in video quality on HEVC and AV1 encoding (BD-BR), as well as a new AV1 Ultra Quality mode that achieves 5% more compression at the same quality. They also include the sixth-generation NVIDIA decoder, with 2x the decode speed for H.264 video.

NVIDIA is collaborating with Adobe Premiere Pro, Blackmagic Design’s DaVinci Resolve, Capcut and Wondershare Filmora to integrate these technologies, starting in February.

3D video is starting to catch on thanks to the growth of VR, AR and mixed reality headsets. The new RTX 50 Series GPUs also come with support for MV-HEVC codecs to unlock such formats in the near future.

Livestreaming Enhanced

Livestreaming is a juggling act, where the streamer has to entertain the audience, produce a show and play a video game — all at the same time. Top streamers can afford to hire producers and moderators to share the workload, but most have to manage these responsibilities on their own and often in long shifts — until now.

Streamlabs, a Logitech brand and leading provider of broadcasting software and tools for content creators, is collaborating with NVIDIA and Inworld AI to create the Streamlabs Intelligent Streaming Assistant.

Streamlabs Intelligent Streaming Assistant is an AI agent that can act as a sidekick, producer and technical support. The sidekick that can join streams as a 3D avatar to answer questions, comment on gameplay or chats, or help initiate conversations during quiet periods. It can help produce streams, switching to the most relevant scenes and playing audio and video cues during interesting gameplay moments. It can even serve as an IT assistant that helps configure streams and troubleshoot issues.

Streamlabs Intelligent Streaming Assistant is powered by NVIDIA ACE technologies for creating digital humans and Inworld AI, an AI framework for agentic AI experiences. The assistant will be available later this year.

Millions have used the NVIDIA Broadcast app to turn offices and dorm rooms into home studios using AI-powered features that improve audio and video quality — without needing expensive, specialized equipment.

Two new AI-powered beta effects are being added to the NVIDIA Broadcast app.

The first, Studio Voice, enhances the sound of a user’s microphone to match that of a high-quality microphone. The other, Virtual Key Light, can relight a subject’s face to deliver even coverage as if it were well-lit by two lights.

Because they harness demanding AI models, these beta features are recommended for video conferencing or non-gaming livestreams using a GeForce RTX 5080 GPU or higher. NVIDIA is working to expand these features to more GeForce RTX GPUs in future updates.

The NVIDIA Broadcast upgrade also includes an updated user interface that allows users to apply more effects simultaneously, as well as improvements to the background noise removal, virtual background and eye contact effects.

The updated NVIDIA Broadcast app will be available in February.

Livestreamers can also benefit from NVENC — 5% BD-BR video quality improvement for HEVC and AV1 — in the latest beta of Twitch’s Enhanced Broadcast feature in OBS, and the improved AV1 encoder for streaming in Discord or YouTube.

RTX Video — an AI feature that enhances video playback on popular internet browsers like Google Chrome and Microsoft Edge, and locally with Video Super Resolution and HDR — is getting an update to decrease GPU usage by 30%, expanding the lineup of GeForce RTX GPUs that can run Video Super Resolution with higher quality.

The RTX Video update is slated for a future NVIDIA App release.

Unprecedented 3D Render Performance

The GeForce RTX 5090 GPU offers 32GB of GPU memory — the largest of any GeForce RTX GPU ever, marking a 33% increase over the GeForce RTX 4090 GPU. This lets 3D artists build larger, richer worlds while using multiple applications simultaneously. Plus, new RTX 50 Series fourth-generation RT Cores can run 3D applications 40% faster.

DLSS 4 debuts Multi Frame Generation to boost frame rates by using AI to generate up to three frames per rendered frame. This enables animators to smoothly navigate a scene with 4x as many frames, or render 3D content at 60 fps or more.

D5 Render and Chaos Vantage, two popular professional-grade 3D apps for architects and designers, will add support for DLSS 4 in February.

3D artists have adopted generative AI to boost productivity in generating draft 3D meshes, HDRi maps or even animations to prototype a scene. At CES, Stability AI announced SPAR3D, its new 3D model that can generate 3D meshes from images in seconds with RTX acceleration.

NVIDIA RTX Remix — a modding platform that lets modders capture game assets, automatically enhance materials with generative AI tools and create stunning RTX remasters with full ray tracing — supports DLSS 4, increasing graphical fidelity and frame rates to maximize realism and immersion during gameplay.

RTX Remix soon plans to support Neural Radiance Cache, a neural shader that uses AI to train on live game data and estimate per-pixel accurate indirect lighting. RTX Remix creators can also expect access to RTX Skin in their mods, the first ray-traced sub-surface scattering implementation in games. With RTX Skin, RTX Remix mods expect to feature characters with new levels of realism, as light will reflect and propagate through their skin, grounding them in the worlds they inhabit.

GeForce RTX 5090 and 5080 GPUs will be available for purchase starting Jan. 30 — followed by GeForce RTX 5070 Ti and 5070 GPUs in February and RTX 50 Series laptops in March.

All systems equipped with GeForce RTX GPUs include the NVIDIA Studio platform optimizations, with over 130 GPU-accelerated content creation apps, as well as NVIDIA Studio Drivers, tested extensively and released monthly to enhance performance and maximize stability in popular creative applications.

Stay tuned for more updates on the GeForce RTX 50 Series. Learn more about how the GeForce RTX 50 Series supercharges gaming, and check out all of NVIDIA’s announcements at CES. 

Every month brings new creative app updates and optimizations powered by the NVIDIA Studio 

Follow NVIDIA Studio on Instagram, X and Facebook. Access tutorials on the Studio YouTube channel and get updates directly in your inbox by subscribing to the Studio newsletter. 

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GeForce RTX 50 Series Desktop and Laptop GPUs, unveiled today at the CES trade show, are poised to power the next era of generative and agentic AI content creation — offering new tools and capabilities for video, livestreaming, 3D and more. Built on the NVIDIA Blackwell architecture, GeForce RTX 50 Series GPUs can run creative
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Now See This: NVIDIA Launches Blueprint for AI Agents That Can Analyze Video​on January 7, 2025 at 2:30 am

The next big moment in AI is in sight — literally. Today, more than 1.5 billion enterprise level cameras deployed worldwide are generating roughly 7 trillion hours of video per year. Yet, only a fraction of it gets analyzed. It’s estimated that less than 1% of video from industrial cameras is watched live by humans,
Read ArticleThe next big moment in AI is in sight — literally. Today, more than 1.5 billion enterprise level cameras deployed worldwide are generating roughly 7 trillion hours of video per year. Yet, only a fraction of it gets analyzed. It’s estimated that less than 1% of video from industrial cameras is watched live by humans,
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The next big moment in AI is in sight — literally.

Today, more than 1.5 billion enterprise level cameras deployed worldwide are generating roughly 7 trillion hours of video per year. Yet, only a fraction of it gets analyzed.

It’s estimated that less than 1% of video from industrial cameras is watched live by humans, meaning critical operational incidents can go largely unnoticed.

This comes at a high cost. For example, manufacturers are losing trillions of dollars annually to poor product quality or defects that they could’ve spotted earlier, or even predicted, by using AI agents that can perceive, analyze and help humans take action.

Interactive AI agents with built-in visual perception capabilities can serve as always-on video analysts, helping factories run more efficiently, bolster worker safety, keep traffic running smoothly and even up an athlete’s game.

To accelerate the creation of such agents, NVIDIA today announced early access to a new version of the NVIDIA AI Blueprint for video search and summarization. Built on top of the NVIDIA Metropolis platform — and now supercharged by NVIDIA Cosmos Nemotron vision language models (VLMs), NVIDIA Llama Nemotron large language models (LLMs) and NVIDIA NeMo Retriever — the blueprint provides developers with the tools to build and deploy AI agents that can analyze large quantities of video and image content.

The blueprint integrates the NVIDIA AI Enterprise software platform — which includes NVIDIA NIM microservices for VLMs, LLMs and advanced AI frameworks for retrieval-augmented generation — to enable batch video processing that’s 30x faster than watching it in real time.

The blueprint contains several agentic AI features — such as chain-of-thought reasoning, task planning and tool calling — that can help developers streamline the creation of powerful and diverse visual agents to solve a range of problems.

AI agents with video analysis abilities can be combined with other agents with different skill sets to enable even more sophisticated agentic AI services. Enterprises have the flexibility to build and deploy their AI agents from the edge to the cloud.

How Video Analyst AI Agents Can Help Industrial Businesses 

AI agents with visual perception and analysis skills can be fine-tuned to help businesses with industrial operations by:

• Increasing productivity and reducing waste: Agents can help ensure standard operating procedures are followed during complex industrial processes like product assembly. They can also be fine-tuned to carefully watch and understand nuanced actions, and the sequence in which they’re implemented.

• Boosting asset management efficiency through better space utilization: Agents can help optimize inventory storage in warehouses by performing 3D volume estimation and centralizing understanding across various camera streams.

• Improving safety through auto-generation of incident reports and summaries: Agents can process huge volumes of video and summarize it into contextually informative reports of accidents. They can also help ensure personal protective equipment compliance in factories, improving worker safety in industrial settings.

• Preventing accidents and production problems: AI agents can identify atypical activity to quickly mitigate operational and safety risks, whether in a warehouse, factory or airport, or at a traffic intersection or other municipal setting.

• Learning from the past: Agents can search through operations video archives, find relevant information from the past and use it to solve problems or create new processes.

Video Analysts for Sports, Entertainment and More

Another industry where video analysis AI agents stand to make a mark is sports — a $500 billion market worldwide, with hundreds of billions in projected growth over the next several years.

Coaches, teams and leagues — whether professional or amateur — rely on video analytics to evaluate and enhance player performance, prioritize safety and boost fan engagement through player analytics platforms and data visualization. With visually perceptive AI agents, athletes now have unprecedented access to deeper insights and opportunities for improvement.

During his CES opening keynote, NVIDIA founder and CEO Jensen Huang demonstrated an AI video analytics agent that assessed the fastball pitching skills of an amateur baseball player compared with a professional’s. Using video captured from the ceremonial first pitch that Huang threw for the San Francisco Giants baseball team, the video analytics AI agent was able to suggest areas for improvement.

The $3 trillion media and entertainment industry is also poised to benefit from video analyst AI agents. Through the NVIDIA Media2 initiative, these agents will help drive the creation of smarter, more tailored and more impactful content that can adapt to individual viewer preferences.

Worldwide Adoption and Availability 

Partners from around the world are integrating the blueprint for building AI agents for video analysis into their own developer workflows, including Accenture, Centific, Deloitte, EY, Infosys, Linker Vision, Pegatron, TATA Consultancy Services (TCS), Telit Cinterion and VAST.

Apply for early access to the NVIDIA Blueprint for video search and summarization.

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Editor’s note: Omdia is the source for 1.5 billion enterprise-level cameras deployed.   

The next big moment in AI is in sight — literally. Today, more than 1.5 billion enterprise level cameras deployed worldwide are generating roughly 7 trillion hours of video per year. Yet, only a fraction of it gets analyzed. It’s estimated that less than 1% of video from industrial cameras is watched live by humans,
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Building Smarter Autonomous Machines: NVIDIA Announces Early Access for Omniverse Sensor RTX​on January 7, 2025 at 2:30 am

Generative AI and foundation models let autonomous machines generalize beyond the operational design domains on which they’ve been trained. Using new AI techniques such as tokenization and large language and diffusion models, developers and researchers can now address longstanding hurdles to autonomy. These larger models require massive amounts of diverse data for training, fine-tuning and
Read ArticleGenerative AI and foundation models let autonomous machines generalize beyond the operational design domains on which they’ve been trained. Using new AI techniques such as tokenization and large language and diffusion models, developers and researchers can now address longstanding hurdles to autonomy. These larger models require massive amounts of diverse data for training, fine-tuning and
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Generative AI and foundation models let autonomous machines generalize beyond the operational design domains on which they’ve been trained. Using new AI techniques such as tokenization and large language and diffusion models, developers and researchers can now address longstanding hurdles to autonomy.

These larger models require massive amounts of diverse data for training, fine-tuning and validation. But collecting such data — including from rare edge cases and potentially hazardous scenarios, like a pedestrian crossing in front of an autonomous vehicle (AV) at night or a human entering a welding robot work cell — can be incredibly difficult and resource-intensive.

To help developers fill this gap, NVIDIA Omniverse Cloud Sensor RTX APIs enable physically accurate sensor simulation for generating datasets at scale. The application programming interfaces (APIs) are designed to support sensors commonly used for autonomy — including cameras, radar and lidar — and can integrate seamlessly into existing workflows to accelerate the development of autonomous vehicles and robots of every kind.

Omniverse Sensor RTX APIs are now available to select developers in early access. Organizations such as Accenture, Foretellix, MITRE and Mcity are integrating these APIs via domain-specific blueprints to provide end customers with the tools they need to deploy the next generation of industrial manufacturing robots and self-driving cars.

Powering Industrial AI With Omniverse Blueprints

In complex environments like factories and warehouses, robots must be orchestrated to safely and efficiently work alongside machinery and human workers. All those moving parts present a massive challenge when designing, testing or validating operations while avoiding disruptions.

Mega is an Omniverse Blueprint that offers enterprises a reference architecture of NVIDIA accelerated computing, AI, NVIDIA Isaac and NVIDIA Omniverse technologies. Enterprises can use it to develop digital twins and test AI-powered robot brains that drive robots, cameras, equipment and more to handle enormous complexity and scale.

Integrating Omniverse Sensor RTX, the blueprint lets robotics developers simultaneously render sensor data from any type of intelligent machine in a factory for high-fidelity, large-scale sensor simulation.

With the ability to test operations and workflows in simulation, manufacturers can save considerable time and investment, and improve efficiency in entirely new ways.

International supply chain solutions company KION Group and Accenture are using the Mega blueprint to build Omniverse digital twins that serve as virtual training and testing environments for industrial AI’s robot brains, tapping into data from smart cameras, forklifts, robotic equipment and digital humans.

The robot brains perceive the simulated environment with physically accurate sensor data rendered by the Omniverse Sensor RTX APIs. They use this data to plan and act, with each action precisely tracked with Mega, alongside the state and position of all the assets in the digital twin. With these capabilities, developers can continuously build and test new layouts before they’re implemented in the physical world.

Driving AV Development and Validation

Autonomous vehicles have been under development for over a decade, but barriers in acquiring the right training and validation data and slow iteration cycles have hindered large-scale deployment.

To address this need for sensor data, companies are harnessing the NVIDIA Omniverse Blueprint for AV simulation, a reference workflow that enables physically accurate sensor simulation. The workflow uses Omniverse Sensor RTX APIs to render the camera, radar and lidar data necessary for AV development and validation.

AV toolchain provider Foretellix has integrated the blueprint into its Foretify AV development toolchain to transform object-level simulation into physically accurate sensor simulation.

The Foretify toolchain can generate any number of testing scenarios simultaneously. By adding sensor simulation capabilities to these scenarios, Foretify can now enable  developers to evaluate the completeness of their AV development, as well as train and test at the levels of fidelity and scale needed to achieve large-scale and safe deployment. In addition, Foretellix will use the newly announced NVIDIA Cosmos platform to generate an even greater diversity of scenarios for verification and validation.

Nuro, an autonomous driving technology provider with one of the largest level 4 deployments in the U.S., is using the Foretify toolchain to train, test and validate its self-driving vehicles before deployment.

In addition, research organization MITRE is collaborating with the University of Michigan’s Mcity testing facility to build a digital AV validation framework for regulatory use, including a digital twin of Mcity’s 32-acre proving ground for autonomous vehicles. The project uses the AV simulation blueprint to render physically accurate sensor data at scale in the virtual environment, boosting training effectiveness.

The future of robotics and autonomy is coming into sharp focus, thanks to the power of high-fidelity sensor simulation. Learn more about these solutions at CES by visiting Accenture at Ballroom F at the Venetian and Foretellix booth 4016 in the West Hall of Las Vegas Convention Center.

Learn more about the latest in automotive and generative AI technologies by joining NVIDIA at CES.

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Generative AI and foundation models let autonomous machines generalize beyond the operational design domains on which they’ve been trained. Using new AI techniques such as tokenization and large language and diffusion models, developers and researchers can now address longstanding hurdles to autonomy. These larger models require massive amounts of diverse data for training, fine-tuning and
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NVIDIA Blackwell GeForce RTX 50 Series Opens New World of AI Computer Graphics​on January 7, 2025 at 3:06 am

NVIDIA today unveiled the most advanced consumer GPUs for gamers, creators and developers — the GeForce RTX™ 50 Series Desktop and Laptop GPUs.NVIDIA today unveiled the most advanced consumer GPUs for gamers, creators and developers — the GeForce RTX™ 50 Series Desktop and Laptop GPUs.  

Next Generation of GeForce RTX GPUs Deliver Stunning Visual Realism and 2x Performance Increase, Made Possible by AI, Neural Shaders and DLSS 4

NVIDIA today unveiled the most advanced consumer GPUs for gamers, creators and developers — the GeForce RTX™ 50 Series Desktop and Laptop GPUs.

NVIDIA Launches AI Foundation Models for RTX AI PCs​on January 7, 2025 at 3:25 am

NVIDIA today announced foundation models running locally on NVIDIA RTX™ AI PCs that supercharge digital humans, content creation, productivity and development.NVIDIA today announced foundation models running locally on NVIDIA RTX™ AI PCs that supercharge digital humans, content creation, productivity and development.  

NVIDIA NIM Microservices and AI Blueprints Help Developers and Enthusiasts Build AI Agents and Creative Workflows on PC

NVIDIA today announced foundation models running locally on NVIDIA RTX™ AI PCs that supercharge digital humans, content creation, productivity and development.

NVIDIA Launches Cosmos World Foundation Model Platform to Accelerate Physical AI Development​on January 7, 2025 at 3:41 am

NVIDIA today announced NVIDIA Cosmos™, a platform comprising state-of-the-art generative world foundation models, advanced tokenizers, guardrails and an accelerated video processing pipeline built to advance the development of physical AI systems such as autonomous vehicles (AVs) and robots.NVIDIA today announced NVIDIA Cosmos™, a platform comprising state-of-the-art generative world foundation models, advanced tokenizers, guardrails and an accelerated video processing pipeline built to advance the development of physical AI systems such as autonomous vehicles (AVs) and robots.  

• New State-of-the-Art Models, Video Tokenizers and an Accelerated Data Processing Pipeline, Optimized for NVIDIA Data Center GPUs, Are Purpose-Built for Developing Robots and Autonomous Vehicles
• First Wave of Open Models Available Now to Developer Community
• Global Physical AI Leaders 1X, Agile Robots, Agility, Figure AI, Foretellix, Uber, Waabi and XPENG Among First to Adopt

CES—NVIDIA today announced NVIDIA Cosmos™, a platform comprising state-of-the-art generative world foundation models, advanced tokenizers, guardrails and an accelerated video processing pipeline built to advance the development of physical AI systems such as autonomous vehicles (AVs) and robots.

Physical AI models are costly to develop, and require vast amounts of real-world data and testing. Cosmos world foundation models, or WFMs, offer developers an easy way to generate massive amounts of photoreal, physics-based synthetic data to train and evaluate their existing models. Developers can also build custom models by fine-tuning Cosmos WFMs.

Cosmos models will be available under an open model license to accelerate the work of the robotics and AV community. Developers can preview the first models on the NVIDIA API catalog, or download the family of models and fine-tuning framework from the NVIDIA NGC™ catalog or Hugging Face.

Leading robotics and automotive companies, including 1X, Agile Robots, Agility, Figure AI, Foretellix, Fourier, Galbot, Hillbot, IntBot, Neura Robotics, Skild AI, Virtual Incision, Waabi and XPENG, along with ridesharing giant Uber, are among the first to adopt Cosmos.

“The ChatGPT moment for robotics is coming. Like large language models, world foundation models are fundamental to advancing robot and AV development, yet not all developers have the expertise and resources to train their own,” said Jensen Huang, founder and CEO of NVIDIA. “We created Cosmos to democratize physical AI and put general robotics in reach of every developer.”

Open World Foundation Models to Accelerate the Next Wave of AI

NVIDIA Cosmos’ suite of open models means developers can customize the WFMs with datasets, such as video recordings of AV trips or robots navigating a warehouse, according to the needs of their target application.

Cosmos WFMs are purpose-built for physical AI research and development, and can generate physics-based videos from a combination of inputs, like text, image and video, as well as robot sensor or motion data. The models are built for physically based interactions, object permanence, and high-quality generation of simulated industrial environments — like warehouses or factories — and of driving environments, including various road conditions.

In his opening keynote at CES, NVIDIA founder and CEO Jensen Huang showcased ways physical AI developers can use Cosmos models, including for:

• Video search and understanding, enabling developers to easily find specific training scenarios, like snowy road conditions or warehouse congestion, from video data.
• Physics-based photoreal synthetic data generation, using Cosmos models to generate photoreal videos from controlled 3D scenarios developed in the NVIDIA Omniverse™ platform.
• Physical AI model development and evaluation, whether building a custom model on the foundation models, improving the models using Cosmos for reinforcement learning or testing how they perform given a specific simulated scenario.
• Foresight and “multiverse” simulation, using Cosmos and Omniverse to generate every possible future outcome an AI model could take to help it select the best and most accurate path.

Advanced World Model Development Tools

Building physical AI models requires petabytes of video data and tens of thousands of compute hours to process, curate and label that data. To help save enormous costs in data curation, training and model customization, Cosmos features:

• An NVIDIA AI and CUDA®-accelerated data processing pipeline, powered by NVIDIA NeMo™ Curator, that enables developers to process, curate and label 20 million hours of videos in 14 days using the NVIDIA Blackwell platform, instead of over three years using a CPU-only pipeline.
• NVIDIA Cosmos Tokenizer, a state-of-the-art visual tokenizer for converting images and videos into tokens. It delivers 8x more total compression and 12x faster processing than today’s leading tokenizers.
• The NVIDIA NeMo framework for highly efficient model training, customization and optimization.

World’s Largest Physical AI Industries Adopt Cosmos

Pioneers across the physical AI industry are already adopting Cosmos technologies.

1X, an AI and humanoid robot company, launched the 1X World Model Challenge dataset using Cosmos Tokenizer. XPENG will use Cosmos to accelerate the development of its humanoid robot. And Hillbot and Skild AI are using Cosmos to fast-track the development of their general-purpose robots.

“Data scarcity and variability are key challenges to successful learning in robot environments,” said Pras Velagapudi, chief technology officer at Agility. “Cosmos’ text-, image- and video-to-world capabilities allow us to generate and augment photorealistic scenarios for a variety of tasks that we can use to train models without needing as much expensive, real-world data capture.”

Transportation leaders are also using Cosmos to build physical AI for AVs:

• Waabi, a company pioneering generative AI for the physical world starting with autonomous vehicles, is evaluating Cosmos in the context of data curation for AV software development and simulation.
• Wayve, which is developing AI foundation models for autonomous driving, is evaluating Cosmos as a tool to search for edge and corner case driving scenarios used for safety and validation.
• AV toolchain provider Foretellix will use Cosmos, alongside NVIDIA Omniverse Sensor RTX APIs, to evaluate and generate high-fidelity testing scenarios and training data at scale.
• Global ridesharing giant Uber is partnering with NVIDIA to accelerate autonomous mobility. Rich driving datasets from Uber, combined with the features of the Cosmos platform and NVIDIA DGX Cloud™, can help AV partners build stronger AI models even more efficiently.

“Generative AI will power the future of mobility, requiring both rich data and very powerful compute,” said Dara Khosrowshahi, CEO of Uber. “By working with NVIDIA, we are confident that we can help supercharge the timeline for safe and scalable autonomous driving solutions for the industry.”

Developing Open, Safe and Responsible AI

NVIDIA Cosmos was developed in line with NVIDIA’s trustworthy AI principles, which prioritize privacy, safety, security, transparency and reducing unwanted bias.

Trustworthy AI is essential for fostering innovation within the developer community and maintaining user trust. NVIDIA is committed to safe and trustworthy AI, in line with the White House’s voluntary AI commitments and other global AI safety initiatives.

The open Cosmos platform includes guardrails designed to mitigate harmful text and images, and features a tool to enhance text prompts for accuracy. Videos generated with Cosmos autoregressive and diffusion models on the NVIDIA API catalog include invisible watermarks to identify AI-generated content, helping reduce the chances of misinformation and misattribution.

NVIDIA encourages developers to adopt trustworthy AI practices and further enhance guardrail and watermarking solutions for their applications.

Availability

Cosmos WFMs are now available under NVIDIA’s open model license on Hugging Face and the NVIDIA NGC catalog. Cosmos models will soon be available as fully optimized NVIDIA NIM microservices.

Developers can access NVIDIA NeMo Curator for accelerated video processing and customize their own world models with NVIDIA NeMo. NVIDIA DGX Cloud offers a fast and easy way to deploy these models, with enterprise support available through the NVIDIA AI Enterprise software platform.

NVIDIA also announced new NVIDIA Llama Nemotron large language models and NVIDIA Cosmos Nemotron vision language models that developers can use for enterprise AI use cases in healthcare, financial services, manufacturing and more.

NVIDIA today announced NVIDIA Cosmos™, a platform comprising state-of-the-art generative world foundation models, advanced tokenizers, guardrails and an accelerated video processing pipeline built to advance the development of physical AI systems such as autonomous vehicles (AVs) and robots.

NVIDIA Expands Omniverse With Generative Physical AI​on January 7, 2025 at 3:51 am

NVIDIA today announced generative AI models and blueprints that expand NVIDIA Omniverse™ integration further into physical AI applications such as robotics, autonomous vehicles and vision AI. Global leaders in software development and professional services are using Omniverse to develop new products and services that will accelerate the next era of industrial AI.NVIDIA today announced generative AI models and blueprints that expand NVIDIA Omniverse™ integration further into physical AI applications such as robotics, autonomous vehicles and vision AI. Global leaders in software development and professional services are using Omniverse to develop new products and services that will accelerate the next era of industrial AI.  

• New Models, Including Cosmos World Foundation Models, and Omniverse Mega Factory and Robotic Digital Twin Blueprint Lay the Foundation for Industrial AI

• Leading Developers Accenture, Altair, Ansys, Cadence, Microsoft and Siemens Among First to Adopt Platform Libraries

CES—NVIDIA today announced generative AI models and blueprints that expand NVIDIA Omniverse™ integration further into physical AI applications such as robotics, autonomous vehicles and vision AI. Global leaders in software development and professional services are using Omniverse to develop new products and services that will accelerate the next era of industrial AI.

Accenture, Altair, Ansys, Cadence, Foretellix, Microsoft and Neural Concept are among the first to integrate Omniverse into their next-generation software products and professional services. Siemens, a leader in industrial automation, announced today at the CES trade show the availability of Teamcenter Digital Reality Viewer — the first Siemens Xcelerator application powered by NVIDIA Omniverse libraries.

“Physical AI will revolutionize the $50 trillion manufacturing and logistics industries. Everything that moves — from cars and trucks to factories and warehouses — will be robotic and embodied by AI,” said Jensen Huang, founder and CEO at NVIDIA. “NVIDIA’s Omniverse digital twin operating system and Cosmos physical AI serve as the foundational libraries for digitalizing the world’s physical industries.”

New Models and Frameworks Accelerate World Building for Physical AI

Creating 3D worlds for physical AI simulation requires three steps: world building, labeling the world with physical attributes and making it photoreal.

NVIDIA offers generative AI models that accelerate each step. The USD Code and USD Search NVIDIA NIM™ microservices are now generally available, letting developers use text prompts to generate or search for OpenUSD assets. A new NVIDIA Edify SimReady generative AI model unveiled today can automatically label existing 3D assets with attributes like physics or materials, enabling developers to process 1,000 3D objects in minutes instead of over 40 hours manually.

NVIDIA Omniverse, paired with new NVIDIA Cosmos™ world foundation models, creates a synthetic data multiplication engine — letting developers easily generate massive amounts of controllable, photoreal synthetic data. Developers can compose 3D scenarios in Omniverse and render images or videos as outputs. These can then be used with text prompts to condition Cosmos models to generate countless synthetic virtual environments for physical AI training.

NVIDIA Omniverse Blueprints Speed Up Industrial, Robotic Workflows

During the CES keynote, NVIDIA also announced four new blueprints that make it easier for developers to build Universal Scene Description (OpenUSD)-based Omniverse digital twins for physical AI. The blueprints include:

• Mega, powered by Omniverse Sensor RTX APIs, for developing and testing robot fleets at scale in an industrial factory or warehouse digital twin before deployment in real-world facilities.
• Autonomous Vehicle (AV) Simulation, also powered by Omniverse Sensor RTX APIs, that lets AV developers replay driving data, generate new ground-truth data and perform closed-loop testing to accelerate their development pipelines.
• Omniverse Spatial Streaming to Apple Vision Pro that helps developers create applications for immersive streaming of large-scale industrial digital twins to Apple Vision Pro.
• Real-Time Digital Twins for Computer Aided Engineering (CAE), a reference workflow built on NVIDIA CUDA-X™ acceleration, physics AI and Omniverse libraries that enables real-time physics visualization.

New free Learn OpenUSD courses are also now available to help developers build OpenUSD-based worlds faster than ever.

Market Leaders Supercharge Industrial AI Using NVIDIA Omniverse

Global leaders in software development and professional services are using Omniverse to develop new products and services that are poised to accelerate the next era of industrial AI.

Building on its adoption of Omniverse libraries in its Reality Digital Twin data center digital twin platform, Cadence, a leader in electronic systems design, announced further integration of Omniverse into Allegro, its leading electronic computer-aided design application used by the world’s largest semiconductor companies.

Altair, a leader in computational intelligence, is adopting the Omniverse blueprint for real-time CAE digital twins for interactive computational fluid dynamics (CFD). Ansys is adopting Omniverse into Ansys Fluent, a leading CAE application. And Neural Concept is integrating Omniverse libraries into its next-generation software products, enabling real-time CFD and enhancing engineering workflows.

Accenture, a leading global professional services company, is using Mega to help German supply chain solutions leader KION by building next-generation autonomous warehouses and robotic fleets for their network of global warehousing and distribution customers.

AV toolchain provider Foretellix, a leader in data-driven autonomy development, is using the AV simulation blueprint to enable full 3D sensor simulation for optimized AV testing and validation. Research organization MITRE is also deploying the blueprint, in collaboration with the University of Michigan’s Mcity testing facility, to create an industry-wide AV validation platform.

Katana Studio is using the Omniverse spatial streaming workflow to create custom car configurators for Nissan and Volkswagen, allowing them to design and review car models in an immersive experience while improving the customer decision-making process.

Innoactive, an XR streaming platform for enterprises, used the workflow to add platform support for spatial streaming to Apple Vision Pro. The solution enables Volkswagen Group to conduct design and engineering project reviews at human-eye resolution. Innoactive also collaborated with Syntegon, a provider of processing and packaging technology solutions for pharmaceutical production, to enable Syntegon’s customers to walk through and review digital twins of custom installations before they are built.

NVIDIA today announced generative AI models and blueprints that expand NVIDIA Omniverse™ integration further into physical AI applications such as robotics, autonomous vehicles and vision AI. Global leaders in software development and professional services are using Omniverse to develop new products and services that will accelerate the next era of industrial AI.

Toyota, Aurora and Continental Join Growing List of NVIDIA Partners Rolling Out Next-Generation Highly Automated and Autonomous Vehicle Fleets​on January 7, 2025 at 3:53 am

CES—NVIDIA announced today that Toyota, Aurora and Continental have joined the list of global mobility leaders developing and building their consumer and commercial vehicle fleets on NVIDIA …CES—NVIDIA announced today that Toyota, Aurora and Continental have joined the list of global mobility leaders developing and building their consumer and commercial vehicle fleets on NVIDIA …  

CES—NVIDIA announced today that Toyota, Aurora and Continental have joined the list of global mobility leaders developing and building their consumer and commercial vehicle fleets on NVIDIA accelerated computing and AI.

Toyota, the world’s largest automaker, will build its next-generation vehicles on NVIDIA DRIVE AGX Orin™, running the safety-certified NVIDIA DriveOS operating system. These vehicles will offer functionally safe, advanced driving assistance capabilities. 

The majority of today’s auto manufacturers, truckmakers, robotaxi, and autonomous delivery vehicle companies, tier-one suppliers and mobility startups are developing on NVIDIA DRIVE AGX™ platform and technologies. With cutting-edge platforms spanning training in the cloud to simulation to compute in the car, NVIDIA’s automotive vertical business is expected to grow to approximately $5 billion in fiscal year 2026.

“The autonomous vehicle revolution has arrived, and automotive will be one of the largest AI and robotics industries,” said Jensen Huang, founder and CEO of NVIDIA. “NVIDIA is bringing two decades of automotive computing, safety expertise and its CUDA AV platform to transform the multitrillion dollar auto industry.”

Aurora, Continental and NVIDIA this week also announced a long-term strategic partnership to deploy driverless trucks at scale, powered by NVIDIA DRIVE. NVIDIA’s accelerated compute running DriveOS will be integrated into the Aurora Driver, an SAE level 4 autonomous-driving system that Continental plans to mass-manufacture in 2027.

Other mobility companies adopting NVIDIA DRIVE AGX for their next-generation advanced driver-assistance systems and autonomous vehicle roadmaps include BYD, JLR, Li Auto, Lucid, Mercedes-Benz, NIO, Nuro, Rivian, Volvo Cars, Waabi, Wayve, Xiaomi, ZEEKR, Zoox and many more.

NVIDIA offers three core computing systems and the AI software essential for end-to-end autonomous vehicle development. NVIDIA DRIVE AGX is the in-vehicle computer. NVIDIA DGX™ processes the data from the fleet and trains AI models, and NVIDIA Omniverse™ and NVIDIA Cosmos™ running on NVIDIA OVX™ systems test and validate self-driving systems in simulation.

Learn more about NVIDIA’s automotive and safety milestones at CES by tuning in to Huang’s opening keynote.

CES—NVIDIA announced today that Toyota, Aurora and Continental have joined the list of global mobility leaders developing and building their consumer and commercial vehicle fleets on NVIDIA …

NVIDIA DRIVE Hyperion Platform Achieves Critical Automotive Safety and Cybersecurity Milestones for AV Development​on January 7, 2025 at 3:56 am

NVIDIA today announced that its autonomous vehicle (AV) platform, NVIDIA DRIVE AGX™ Hyperion, has passed industry-safety assessments by TÜV SÜD and TÜV Rheinland — two of the industry’s…NVIDIA today announced that its autonomous vehicle (AV) platform, NVIDIA DRIVE AGX™ Hyperion, has passed industry-safety assessments by TÜV SÜD and TÜV Rheinland — two of the industry’s…  

Adopted and Backed by Automotive Manufacturers and Safety Authorities, Latest Iteration to Feature DRIVE Thor on NVIDIA Blackwell Running NVIDIA DriveOS

NVIDIA today announced that its autonomous vehicle (AV) platform, NVIDIA DRIVE AGX™ Hyperion, has passed industry-safety assessments by TÜV SÜD and TÜV Rheinland — two of the industry’s…

NVIDIA Puts Grace Blackwell on Every Desk and at Every AI Developer’s Fingertips​on January 7, 2025 at 4:10 am

CES—NVIDIA today unveiled NVIDIA® Project DIGITS, a personal AI supercomputer that provides AI researchers, data scientists and students worldwide with access to the power of the NVIDIA Grace …CES—NVIDIA today unveiled NVIDIA® Project DIGITS, a personal AI supercomputer that provides AI researchers, data scientists and students worldwide with access to the power of the NVIDIA Grace …  

NVIDIA Project DIGITS With New GB10 Superchip Debuts as World’s Smallest AI Supercomputer Capable of Running 200B-Parameter Models

CES—NVIDIA today unveiled NVIDIA® Project DIGITS, a personal AI supercomputer that provides AI researchers, data scientists and students worldwide with access to the power of the NVIDIA Grace …

NVIDIA Unveils ‘Mega’ Omniverse Blueprint for Building Industrial Robot Fleet Digital Twins​on January 7, 2025 at 4:15 am

According to Gartner, the worldwide end-user spending on all IT products for 2024 was $5 trillion. This industry is built on a computing fabric of electrons, is fully software-defined, accelerated — and now generative AI-enabled. While huge, it’s a fraction of the larger physical industrial market that relies on the movement of atoms. Today’s 10
Read ArticleAccording to Gartner, the worldwide end-user spending on all IT products for 2024 was $5 trillion. This industry is built on a computing fabric of electrons, is fully software-defined, accelerated — and now generative AI-enabled. While huge, it’s a fraction of the larger physical industrial market that relies on the movement of atoms. Today’s 10
Read Article  

According to Gartner, the worldwide end-user spending on all IT products for 2024 was $5 trillion. This industry is built on a computing fabric of electrons, is fully software-defined, accelerated — and now generative AI-enabled. While huge, it’s a fraction of the larger physical industrial market that relies on the movement of atoms.

Today’s 10 million factories, nearly 200,000 warehouses and 40 million miles of highways form the “computing” fabric of our physical world. But that vast network of production facilities and distribution centers is still laboriously and manually designed, operated and optimized.

In warehousing and distribution, operators face highly complex decision optimization problems — matrices of variables and interdependencies across human workers, robotic and agentic systems and equipment. Unlike the IT industry, the physical industrial market is still waiting for its own software-defined moment.

That moment is coming.

NVIDIA today at CES announced “Mega,” an Omniverse Blueprint for developing, testing and optimizing physical AI and robot fleets at scale in a digital twin before deployment into real-world facilities.

Advanced warehouses and factories use fleets of hundreds of autonomous mobile robots, robotic arm manipulators and humanoids working alongside people. With implementations of increasingly complex systems of sensor and robot autonomy, it requires coordinated training in simulation to optimize operations, help ensure safety and avoid disruptions.

Mega offers enterprises a reference architecture of NVIDIA accelerated computing, AI, NVIDIA Isaac and NVIDIA Omniverse technologies to develop and test digital twins for testing AI-powered robot brains that drive robots, video analytics AI agents, equipment and more for handling enormous complexity and scale. The new framework brings software-defined capabilities to physical facilities, enabling continuous development, testing, optimization and deployment.

Developing AI Brains With World Simulator for Autonomous Orchestration

With Mega-driven digital twins, including a world simulator that coordinates all robot activities and sensor data, enterprises can continuously update facility robot brains for intelligent routes and tasks for operational efficiencies.

The blueprint uses Omniverse Cloud Sensor RTX APIs that enable robotics developers to render sensor data from any type of intelligent machine in the factory, simultaneously, for high-fidelity large-scale sensor simulation. This allows robots to be tested in an infinite number of scenarios within the digital twin, using synthetic data in a software-in–the-loop pipeline with NVIDIA Isaac ROS.

Supply chain solutions company KION Group is collaborating with Accenture and NVIDIA as the first to adopt Mega for optimizing operations in retail, consumer packaged goods, parcel services and more.

Jensen Huang, founder and CEO of NVIDIA, offered a glimpse into the future of this collaboration on stage at CES, demonstrating how enterprises can navigate a complex web of decisions using the Mega Omniverse Blueprint.

“At KION, we leverage AI-driven solutions as an integral part of our strategy to optimize our customers’ supply chains and increase their productivity,” said Rob Smith, CEO of KION GROUP AG. “With NVIDIA’s AI leadership and Accenture’s expertise in digital technologies, we are reinventing warehouse automation. Bringing these strong partners together, we are creating a vision for future warehouses that are part of a smart agile system, evolve with the world around them and can handle nearly any supply chain challenge.”

Creating Operational Efficiencies With Mega Omniverse Blueprint

Creating operational efficiencies, KION and Accenture are embracing the Mega Omniverse Blueprint to build next-generation supply chains for KION and its customers. KION can capture and digitalize a warehouse digital twin in Omniverse by using computer-aided design files, video, lidar, image and AI-generated data.

KION uses the Omniverse digital twin as a virtual training and testing environment for its industrial AI’s robot brains, powered by NVIDIA Isaac, tapping into smart cameras, forklifts, robotic equipment and digital humans. Integrating the Omniverse digital twin, KION’s warehouse management software can create and assign missions for robot brains, like moving a load from one place to another.

These simulated robots can carry out tasks by perceiving and reasoning in environments, and they’re capable of planning next motions and then taking actions that are simulated in the digital twin. The robot brains perceive the results deciding the next action, and this cycle continues with Mega precisely tracking the state and position of all the assets in the digital twin.

Delivering Services With Mega for Facilities Everywhere

Accenture, global leader in professional services, is adopting Mega as part of its AI Refinery for Simulation and Robotics, built on NVIDIA AI and Omniverse, to help organizations use AI simulation to reinvent factory and warehouse design and ongoing operations.

With the blueprint, Accenture is delivering new services — including Custom Robotics and Manufacturing Foundation Model Training and Finetuning; Intelligent Humanoid Robotics; and AI-Powered Industrial Manufacturing and Logistics Simulation and Optimization — to expand the power of physical AI and  simulation to the world’s factories and warehouse operators.  Now, for example, an organization can explore numerous options for their warehouse before choosing and implementing the best one.

“As organizations enter the age of industrial AI, we are helping them use AI-powered simulation and autonomous robots to reinvent the process of designing new facilities and optimizing existing operations,” said Julie Sweet, chair and CEO of Accenture. “Our collaboration with NVIDIA and KION will help our clients plan their operations in digital twins, where they can run hundreds of options and quickly select the best for current or changing market conditions, such as seasonal market demand or workforce availability.  This represents a new frontier of value for our clients to achieve using technology, data and AI.”

Join NVIDIA at CES. 

See notice regarding software product information.

According to Gartner, the worldwide end-user spending on all IT products for 2024 was $5 trillion. This industry is built on a computing fabric of electrons, is fully software-defined, accelerated — and now generative AI-enabled. While huge, it’s a fraction of the larger physical industrial market that relies on the movement of atoms. Today’s 10
Read Article

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