Posts in category: nVidia AI

Starting with the release of CUDA in 2006, NVIDIA has driven advancements in AI and accelerated computing — and the most recent TOP500 list of the world’s most powerful supercomputers highlights the culmination of the company’s achievements in the field.

This year, 384 systems on the TOP500 list are powered by NVIDIA technologies. Among the 53 new to the list, 87% — 46 systems — are accelerated. Of those accelerated systems, 85% use NVIDIA Hopper GPUs, driving advancements in areas like climate forecasting, drug discovery and quantum simulation.

Accelerated computing is much more than floating point operations per second (FLOPS). It requires full-stack, application-specific optimization. At SC24 this week, NVIDIA announced the release of cuPyNumeric, an NVIDIA CUDA-X library that enables over 5 million developers to seamlessly scale to powerful computing clusters without modifying their Python code.

At the conference, NVIDIA also revealed significant updates to the NVIDIA CUDA-Q development platform, which empowers quantum researchers to simulate quantum devices at a scale previously thought computationally impossible.

A New Era of Scientific Discovery With Mixed Precision and AI

Mixed-precision floating-point operations and AI have become the tools of choice for researchers grappling with the complexities of modern science. They offer greater speed, efficiency and adaptability than traditional methods, without compromising accuracy.

This shift isn’t just theoretical — it’s already happening. At SC24, two Gordon Bell finalist projects revealed how using AI and mixed precision helped advance genomics and protein design.

In his paper titled “Using Mixed Precision for Genomics,” David Keyes, a professor at King Abdullah University of Science and Technology, used 0.8 exaflops of mixed precision to explore relationships between genomes and their generalized genotypes, and then to the prevalence of diseases to which they are subject.

Similarly, Arvind Ramanathan, a computational biologist from the Argonne National Laboratory, harnessed 3 exaflops of AI performance on the NVIDIA Grace Hopper-powered Alps system to speed up protein design.

To further advance AI-driven drug discovery and the development of lifesaving therapies, researchers can use NVIDIA BioNeMo, powerful tools designed specifically for pharmaceutical applications. Now in open source, the BioNeMo Framework can accelerate AI model creation, customization and deployment for drug discovery and molecular design.

Across the TOP500, the widespread use of AI and mixed-precision floating-point operations reflects a global shift in computing priorities. A total of 249 exaflops of AI performance are now available to TOP500 systems, supercharging innovations and discoveries across industries.

NVIDIA-accelerated TOP500 systems excel across key metrics like AI and mix-precision system performance. With over 190 exaflops of AI performance and 17 exaflops of single-precision (FP32), NVIDIA’s accelerated computing platform is the new engine of scientific computing. NVIDIA also delivers 4 exaflops of double-precision (FP64) performance for certain scientific calculations that still require it.

Accelerated Computing Is Sustainable Computing

As the demand for computational capacity grows, so does the need for sustainability.

In the Green500 list of the world’s most energy-efficient supercomputers, systems with NVIDIA accelerated computing rank among eight of the top 10. The JEDI system at EuroHPC/FZJ, for example, achieves a staggering 72.7 gigaflops per watt, setting a benchmark for what’s possible when performance and sustainability align.

For climate forecasting, NVIDIA announced at SC24 two new NVIDIA NIM microservices for NVIDIA Earth-2, a digital twin platform for simulating and visualizing weather and climate conditions. The CorrDiff NIM and FourCastNet NIM microservices can accelerate climate change modeling and simulation results by up to 500x.

In a world increasingly conscious of its environmental footprint, NVIDIA’s innovations in accelerated computing balance high performance with energy efficiency to help realize a brighter, more sustainable future.

Watch the replay of NVIDIA’s special address at SC24 and learn more about the company’s news in the SC24 online press kit.

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As COP29 attendees gather in Baku, Azerbaijan, to tackle climate change, the role AI plays in environmental sustainability is front and center.

A panel hosted by Deloitte brought together industry leaders to explore ways to reduce AI’s environmental footprint and align its growth with climate goals.

Experts from Crusoe Energy Systems, EON, the International Energy Agency (IEA) and NVIDIA sat down for a conversation about the energy efficiency of AI.

The Environmental Impact of AI

Deloitte’s recent report, “Powering Artificial Intelligence: A study of AI’s environmental footprint,” shows AI’s potential to drive a climate-neutral economy. The study looks at how organizations can achieve “Green AI” in the coming decades and addresses AI’s energy use.

Deloitte analysis predicts that AI adoption will fuel data center power demand, likely reaching 1,000 terawatt-hours (TWh) by 2030, and potentially climbing to 2,000 TWh by 2050. This will account for 3% of global electricity consumption, indicating faster growth than in other uses like electric cars and green hydrogen production.

While data centers currently consume around 2% of total electricity, and AI is a small fraction of that, the discussion at COP29 emphasized the need to meet rising energy demands with clean energy sources to support global climate goals.

Energy Efficiency From the Ground Up

NVIDIA is prioritizing energy-efficient data center operations with innovations like liquid-cooled GPUs. Direct-to-chip liquid cooling allows data centers to cool systems more effectively than traditional air conditioning, consuming less power and water.

“We see a very rapid trend toward direct-to-chip liquid cooling, which means water demands in data centers are dropping dramatically right now,” said Josh Parker, senior director of legal – corporate sustainability at NVIDIA.

As AI continues to scale, the future of data centers will hinge on designing for energy efficiency from the outset. By prioritizing energy efficiency from the ground up, data centers can meet the growing demands of AI while contributing to a more sustainable future.

Parker emphasized that existing data center infrastructure is becoming dated and less efficient. “The data shows that it’s 10x more efficient to run workloads on accelerated computing platforms than on traditional data center platforms,” he said. “There’s a huge opportunity for us to reduce the energy consumed in existing infrastructures.”

The Path to Green Computing

AI has the potential to play a large role in moving toward climate-neutral economies, according to Deloitte’s study. This approach, often called Green AI, involves reducing the environmental impact of AI throughout the value chain with practices like purchasing renewable energy and improving hardware design.

Until now, Green AI has mostly been led by industry leaders. Take accelerated computing, for instance, which is all about doing more with less. It uses special hardware — like GPUs — to perform tasks faster and with less energy than general-purpose servers that use CPUs, which handle a task at a time.

That’s why accelerated computing is sustainable computing.

“Accelerated computing is actually the most energy-efficient platform that we’ve seen for AI but also for a lot of other computing applications,” said Parker.

“The trend in energy efficiency for accelerated computing over the last several years shows a 100,000x reduction in energy consumption. And just in the past 2 years, we’ve become 25x more efficient for AI inference. That’s a 96% reduction in energy for the same computational workload,” he said.

Reducing Energy Consumption Across Sectors

Innovations like the NVIDIA Blackwell and Hopper architectures significantly improve energy efficiency with each new generation. NVIDIA Blackwell is 25x more energy-efficient for large language models, and the NVIDIA H100 Tensor Core GPU is 20x more efficient than CPUs for complex workloads.

“AI has the potential to make other sectors much more energy efficient,” said Parker. Murex, a financial services firm, achieved a 4x reduction in energy use and 7x faster performance with the NVIDIA Grace Hopper Superchip.

“In manufacturing, we’re seeing around 30% reductions in energy requirements if you use AI to help optimize the manufacturing process through digital twins,” he said.

For example, manufacturing company Wistron improved energy efficiency using digital twins and NVIDIA Omniverse, a platform for developing OpenUSD applications for industrial digitalization and physical AI simulation. The company reduced its electricity consumption by 120,000 kWh and carbon emissions by 60,000 kg annually.

A Tool for Energy Management

Deloitte reports that AI can help optimize resource use and reduce emissions, playing a crucial role in energy management. This means it has the potential to lower the impact of industries beyond its own carbon footprint.

Combined with digital twins, AI is transforming energy management systems by improving the reliability of renewable sources like solar and wind farms. It’s also being used to optimize facility layouts, monitor equipment, stabilize power grids and predict climate patterns, aiding in global efforts to reduce carbon emissions.

COP29 discussions emphasized the importance of powering AI infrastructure with renewables and setting ethical guidelines. By innovating with the environment in mind, industries can use AI to build a more sustainable world.

Watch a replay of the on-demand COP29 panel discussion.

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The U.S. Department of Energy oversees national energy policy and production. As big a job as that is, the DOE also does so much more — enough to have earned the nickname “Department of Everything.”

In this episode of the NVIDIA AI Podcast, Helena Fu, director of the DOE’s Office of Critical and Emerging Technologies (CET) and DOE’s chief AI officer, talks about the department’s latest AI efforts. With initiatives touching national security, infrastructure and utilities, and oversight of 17 national labs and 34 scientific user facilities dedicated to scientific discovery and industry innovation, DOE and CET are central to AI-related research and development throughout the country.

Hear more from Helena Fu by watching the on-demand session, AI for Science, Energy and Security, from AI Summit DC. And learn more about software-defined infrastructure for power and utilities.

Time Stamps

2:20: Four areas of focus for the CET include AI, microelectronics, quantum information science and biotechnology.

10:55: Introducing AI-related initiatives within the DOE, including FASST, or Frontiers in AI for Science, Security and Technology.

16:30: Discussing future applications of AI, large language models and more.

19:35: The opportunity of AI applied to materials discovery and applications across science, energy and national security.

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NVIDIA’s Josh Parker on How AI and Accelerated Computing Drive Sustainability – Ep. 234

AI isn’t just about building smarter machines. It’s about building a greener world. AI and accelerated computing are helping industries tackle some of the world’s toughest environmental challenges. Joshua Parker, senior director of corporate sustainability at NVIDIA, explains how these technologies are powering a new era of energy efficiency.

Currents of Change: ITIF’s Daniel Castro on Energy-Efficient AI and Climate Change

AI is everywhere. So, too, are concerns about advanced technology’s environmental impact. Daniel Castro, vice president of the Information Technology and Innovation Foundation and director of its Center for Data Innovation, discusses his AI energy use report that addresses misconceptions about AI’s energy consumption. He also talks about the need for continued development of energy-efficient technology.

How the Ohio Supercomputer Center Drives the Future of Computing – Ep. 213

The Ohio Supercomputer Center’s Open OnDemand program empowers the state’s educational institutions and industries with computational services, training and educational programs. They’ve even helped NASCAR simulate race car designs. Alan Chalker, the director of strategic programs at the OSC, talks about all things supercomputing.

Anima Anandkumar on Using Generative AI to Tackle Global Challenges – Ep. 204

Anima Anandkumar, Bren Professor at Caltech and former senior director of AI research at NVIDIA, speaks to generative AI’s potential to make splashes in the scientific community, from accelerating drug and vaccine research to predicting extreme weather events like hurricanes or heat waves.

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NVIDIA and Microsoft today unveiled product integrations designed to advance full-stack NVIDIA AI development on Microsoft platforms and applications.

At Microsoft Ignite, Microsoft announced the launch of the first cloud private preview of the Azure ND GB200 V6 VM series, based on the NVIDIA Blackwell platform. The Azure ND GB200 v6 will be a new AI-optimized virtual machine (VM) series and combines the NVIDIA GB200 NVL72 rack design with NVIDIA Quantum InfiniBand networking.

In addition, Microsoft revealed that Azure Container Apps now supports NVIDIA GPUs, enabling simplified and scalable AI deployment. Plus, the NVIDIA AI platform on Azure includes new reference workflows for industrial AI and an NVIDIA Omniverse Blueprint for creating immersive, AI-powered visuals.

At Ignite, NVIDIA also announced multimodal small language models (SLMs) for RTX AI PCs and workstations, enhancing digital human interactions and virtual assistants with greater realism.

NVIDIA Blackwell Powers Next-Gen AI on Microsoft Azure

Microsoft’s new Azure ND GB200 V6 VM series will harness the powerful performance of NVIDIA GB200 Grace Blackwell Superchips, coupled with advanced NVIDIA Quantum InfiniBand networking. This offering is optimized for large-scale deep learning workloads to accelerate breakthroughs in natural language processing, computer vision and more.

The Blackwell-based VM series complements previously announced Azure AI clusters with ND H200 V5 VMs, which provide increased high-bandwidth memory for improved AI inferencing. The ND H200 V5 VMs are already being used by OpenAI to enhance ChatGPT.

Azure Container Apps Enables Serverless AI Inference With NVIDIA Accelerated Computing

Serverless computing provides AI application developers increased agility to rapidly deploy, scale and iterate on applications without worrying about underlying infrastructure. This enables them to focus on optimizing models and improving functionality while minimizing operational overhead.

The Azure Container Apps serverless containers platform simplifies deploying and managing microservices-based applications by abstracting away the underlying infrastructure.

Azure Container Apps now supports NVIDIA-accelerated workloads with serverless GPUs, allowing developers to use the power of accelerated computing for real-time AI inference applications in a flexible, consumption-based, serverless environment. This capability simplifies AI deployments at scale while improving resource efficiency and application performance without the burden of infrastructure management.

Serverless GPUs allow development teams to focus more on innovation and less on infrastructure management. With per-second billing and scale-to-zero capabilities, customers pay only for the compute they use, helping ensure resource utilization is both economical and efficient. NVIDIA is also working with Microsoft to bring NVIDIA NIM microservices to serverless NVIDIA GPUs in Azure to optimize AI model performance.

NVIDIA Unveils Omniverse Reference Workflows for Advanced 3D Applications

NVIDIA announced reference workflows that help developers to build 3D simulation and digital twin applications on NVIDIA Omniverse and Universal Scene Description (OpenUSD)  — accelerating industrial AI and advancing AI-driven creativity.

A reference workflow for 3D remote monitoring of industrial operations is coming soon to enable developers to connect physically accurate 3D models of industrial systems to real-time data from Azure IoT Operations and Power BI.

These two Microsoft services integrate with applications built on NVIDIA Omniverse and OpenUSD to provide solutions for industrial IoT use cases. This helps remote operations teams accelerate decision-making and optimize processes in production facilities.

The Omniverse Blueprint for precise visual generative AI enables developers to create applications that let nontechnical teams generate AI-enhanced visuals while preserving brand assets. The blueprint supports models like SDXL and Shutterstock Generative 3D to streamline the creation of on-brand, AI-generated images.

Leading creative groups, including Accenture Song, Collective, GRIP, Monks and WPP, have adopted this NVIDIA Omniverse Blueprint to personalize and customize imagery across markets.

Accelerating Gen AI for Windows With RTX AI PCs 

NVIDIA’s collaboration with Microsoft extends to bringing AI capabilities to personal computing devices.

At Ignite, NVIDIA announced its new multimodal SLM, NVIDIA Nemovision-4B Instruct, for understanding visual imagery in the real world and on screen. It’s  coming soon to RTX AI PCs and workstations and will pave the way for more sophisticated and lifelike digital human interactions.

Plus, updates to NVIDIA TensorRT Model Optimizer (ModelOpt) offer Windows developers a path to optimize a model for ONNX Runtime deployment. TensorRT ModelOpt enables developers to create AI models for PCs that are faster and more accurate when accelerated by RTX GPUs. This enables large models to fit within the constraints of PC environments, while making it easy for developers to deploy across the PC ecosystem with ONNX runtimes.

RTX AI-enabled PCs and workstations offer enhanced productivity tools, creative applications and immersive experiences powered by local AI processing.

Full-Stack Collaboration for AI Development

NVIDIA’s extensive ecosystem of partners and developers brings a wealth of AI and high-performance computing options to the Azure platform.

SoftServe, a global IT consulting and digital services provider, today announced the availability of SoftServe Gen AI Industrial Assistant, based on the NVIDIA AI Blueprint for multimodal PDF data extraction, on the Azure marketplace. The assistant addresses critical challenges in manufacturing by using AI to enhance equipment maintenance and improve worker productivity.

At Ignite, AT&T will showcase how it’s using NVIDIA AI and Azure to enhance operational efficiency, boost employee productivity and drive business growth through retrieval-augmented generation and autonomous assistants and agents.

Learn more about NVIDIA and Microsoft’s collaboration and sessions at Ignite.

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Generative AI-powered laptops and PCs are unlocking advancements in gaming, content creation, productivity and development. Today, over 600 Windows apps and games are already running AI locally on more than 100 million GeForce RTX AI PCs worldwide, delivering fast, reliable and low-latency performance.

At Microsoft Ignite, NVIDIA and Microsoft announced tools to help Windows developers quickly build and optimize AI-powered apps on RTX AI PCs, making local AI more accessible. These new tools enable application and game developers to harness powerful RTX GPUs to accelerate complex AI workflows for applications such as AI agents, app assistants and digital humans.

RTX AI PCs Power Digital Humans With Multimodal Small Language Models

NVIDIA ACE is a suite of digital human technologies that brings life to agents, assistants and avatars. To achieve a higher level of understanding so that they can respond with greater context-awareness, digital humans must be able to visually perceive the world like humans do.

Enhancing digital human interactions with greater realism demands technology that enables perception and understanding of their surroundings with greater nuance. To achieve this, NVIDIA developed multimodal small language models that can process both text and imagery, excel in role-playing and are optimized for rapid response times.

The NVIDIA Nemovision-4B-Instruct model, soon to be available, uses the latest NVIDIA VILA and NVIDIA NeMo framework for distilling, pruning and quantizing to become small enough to perform on RTX GPUs with the accuracy developers need.

The model enables digital humans to understand visual imagery in the real world and on the screen to deliver relevant responses. Multimodality serves as the foundation for agentic workflows and offers a sneak peek into a future where digital humans can reason and take action with minimal assistance from a user.

NVIDIA is also introducing the Mistral NeMo Minitron 128k Instruct family, a suite of large-context small language models designed for optimized, efficient digital human interactions, coming soon. Available in 8B-, 4B- and 2B-parameter versions, these models offer flexible options for balancing speed, memory usage and accuracy on RTX AI PCs. They can handle large datasets in a single pass, eliminating the need for data segmentation and reassembly. Built in the GGUF format, these models enhance efficiency on low-power devices and support compatibility with multiple programming languages.

Turbocharge Gen AI With NVIDIA TensorRT Model Optimizer for Windows 

When bringing models to PC environments, developers face the challenge of limited memory and compute resources for running AI locally. And they want to make models available to as many people as possible, with minimal accuracy loss.

Today, NVIDIA announced updates to NVIDIA TensorRT Model Optimizer (ModelOpt) to offer Windows developers an improved way to optimize models for ONNX Runtime deployment.

With the latest updates, TensorRT ModelOpt enables models to be optimized into an ONNX checkpoint for deploying the model within ONNX runtime environments — using GPU execution providers such as CUDA, TensorRT and DirectML.

TensorRT-ModelOpt includes advanced quantization algorithms, such as INT4-Activation Aware Weight Quantization. Compared to other tools such as Olive, the new method reduces the memory footprint of the model and improves throughput performance on RTX GPUs.

During deployment, the models can have up to 2.6x reduced memory footprint compared to FP16 models. This results in faster throughput, with minimal accuracy degradation, allowing them to run on a wider range of PCs.

Learn more about how developers on Microsoft systems, from Windows RTX AI PCs to NVIDIA Blackwell-powered Azure servers, are transforming how users interact with AI on a daily basis.

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NVIDIA kicked off SC24 in Atlanta with a wave of AI and supercomputing tools set to revolutionize industries like biopharma and climate science.

The announcements, delivered by NVIDIA founder and CEO Jensen Huang and Vice President of Accelerated Computing Ian Buck, are rooted in the company’s deep history in transforming computing.

“Supercomputers are among humanity’s most vital instruments, driving scientific breakthroughs and expanding the frontiers of knowledge,” Huang said. “Twenty-five years after creating the first GPU, we have reinvented computing and sparked a new industrial revolution.”

NVIDIA’s journey in accelerated computing began with CUDA in 2006 and the first GPU for scientific computing, Huang said.

Milestones like Tokyo Tech’s Tsubame supercomputer in 2008, the Oak Ridge National Laboratory’s Titan supercomputer in 2012 and the AI-focused NVIDIA DGX-1 delivered to OpenAI in 2016 highlight NVIDIA’s transformative role in the field.

“Since CUDA’s inception, we’ve driven down the cost of computing by a millionfold,” Huang said. “For some, NVIDIA is a computational microscope, allowing them to see the impossibly small. For others, it’s a telescope exploring the unimaginably distant. And for many, it’s a time machine, letting them do their life’s work within their lifetime.”

At SC24, NVIDIA’s announcements spanned tools for next-generation drug discovery, real-time climate forecasting and quantum simulations.

Central to the company’s advancements are CUDA-X libraries, described by Huang as “the engines of accelerated computing,” which power everything from AI-driven healthcare breakthroughs to quantum circuit simulations.

Huang and Buck highlighted examples of real-world impact, including Nobel Prize-winning breakthroughs in neural networks and protein prediction, powered by NVIDIA technology.

“AI will accelerate scientific discovery, transforming industries and revolutionizing every one of the world’s $100 trillion markets,” Huang said.

CUDA-X Libraries Power New Frontiers

At SC24, NVIDIA announced the new cuPyNumeric library, a GPU-accelerated implementation of NumPy, designed to supercharge applications in data science, machine learning and numerical computing.

With over 400 CUDA-X libraries, including cuDNN for deep learning and cuQuantum for quantum circuit simulations, NVIDIA continues to lead in enhancing computing capabilities across various industries.

Real-Time Digital Twins With Omniverse Blueprint

NVIDIA unveiled the NVIDIA Omniverse Blueprint for real-time computer-aided engineering digital twins, a reference workflow designed to help developers create interactive digital twins for industries like aerospace, automotive, energy and manufacturing.

Built on NVIDIA acceleration libraries, physics-AI frameworks and interactive, physically based rendering, the blueprint accelerates simulations by up to 1,200x, setting a new standard for real-time interactivity.

Early adopters, including Siemens, Altair, Ansys and Cadence, are already using the blueprint to optimize workflows, cut costs and bring products to market faster.

Quantum Leap With CUDA-Q

NVIDIA’s focus on real-time, interactive technologies extends across fields, from engineering to quantum simulations.

In partnership with Google, NVIDIA’s CUDA-Q now powers detailed dynamical simulations of quantum processors, reducing weeks-long calculations to minutes.

Buck explained that with CUDA-Q, developers of all quantum processors can perform larger simulations and explore more scalable qubit designs.

AI Breakthroughs in Drug Discovery and Chemistry

With the open-source release of BioNeMo Framework, NVIDIA is advancing AI-driven drug discovery as researchers gain powerful tools tailored specifically for pharmaceutical applications.

BioNeMo accelerates training by 2x compared to other AI software, enabling faster development of lifesaving therapies.

NVIDIA also unveiled DiffDock 2.0, a breakthrough tool for predicting how drugs bind to target proteins — critical for drug discovery.

Powered by the new cuEquivariance library, DiffDock 2.0 is 6x faster than before, enabling researchers to screen millions of molecules with unprecedented speed and accuracy.

And the NVIDIA ALCHEMI NIM microservice, NVIDIA introduces generative AI to chemistry, allowing researchers to design and evaluate novel materials with incredible speed.

Scientists start by defining the properties they want — like strength, conductivity, low toxicity or even color, Buck explained.

A generative model suggests thousands of potential candidates with the desired properties. Then the ALCHEMI NIM sorts candidate compounds for stability by solving for their lowest energy states using NVIDIA Warp.

This microservice is a game-changer for materials discovery, helping developers tackle challenges in renewable energy and beyond.

These innovations demonstrate how NVIDIA is harnessing AI to drive breakthroughs in science, transforming industries and enabling faster solutions to global challenges.

Earth-2 NIM Microservices: Redefining Climate Forecasts in Real Time

Buck also announced two new microservices — CorrDiff NIM and FourCastNet NIM — to accelerate climate change modeling and simulation results by up to 500x in the NVIDIA Earth-2 platform.

Earth-2, a digital twin for simulating and visualizing weather and climate conditions, is designed to empower weather technology companies with advanced generative AI-driven capabilities.

These tools deliver higher-resolution and more accurate predictions, enabling the forecasting of extreme weather events with unprecedented speed and energy efficiency.

With natural disasters causing $62 billion in insured losses in the first half of this year — 70% higher than the 10-year average — NVIDIA’s innovations address a growing need for precise, real-time climate forecasting. These tools highlight NVIDIA’s commitment to leveraging AI for societal resilience and climate preparedness.

Expanding Production With Foxconn Collaboration

As demand for AI systems like the Blackwell supercomputer grows, NVIDIA is scaling production through new Foxconn facilities in the U.S., Mexico and Taiwan.

Foxconn is building the production and testing facilities using NVIDIA Omniverse to bring up the factories as fast as possible.

Scaling New Heights With Hopper

NVIDIA also announced the general availability of the NVIDIA H200 NVL, a PCIe GPU based on the NVIDIA Hopper architecture optimized for low-power, air-cooled data centers.

The H200 NVL offers up to 1.7x faster large language model inference and 1.3x more performance on HPC applications, making it ideal for flexible data center configurations.

It supports a variety of AI and HPC workloads, enhancing performance while optimizing existing infrastructure.

And the GB200 Grace Blackwell NVL4 Superchip integrates four NVIDIA NVLink-connected Blackwell GPUs unified with two Grace CPUs over NVLink-C2C, Buck said. It provides up to 2x performance for scientific computing, training and inference applications over the prior generation. |

The GB200 NVL4 superchip will be available in the second half of 2025.

The talk wrapped up with an invitation to attendees to visit NVIDIA’s booth at SC24 to interact with various demos, including James, NVIDIA’s digital human, the world’s first real-time interactive wind tunnel and the Earth-2 NIM microservices for climate modeling.

Learn more about how NVIDIA’s innovations are shaping the future of science at SC24.

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NVIDIA today at SC24 announced two new NVIDIA NIM microservices that can accelerate climate change modeling simulation results by 500x in NVIDIA Earth-2.

Earth-2 is a digital twin platform for simulating and visualizing weather and climate conditions. The new NIM microservices offer climate technology application providers advanced generative AI-driven capabilities to assist in forecasting extreme weather events.

NVIDIA NIM microservices help accelerate the deployment of foundation models while keeping data secure.

Extreme weather incidents are increasing in frequency, raising concerns over disaster safety and preparedness, and possible financial impacts.

Natural disasters were responsible for roughly $62 billion of insured losses during the first half of this year. That’s about 70% more than the 10-year average, according to a report in Bloomberg.

NVIDIA is releasing the CorrDiff NIM and FourCastNet NIM microservices to help weather technology companies more quickly develop higher-resolution and more accurate predictions. The NIM microservices also deliver leading energy efficiency compared with traditional systems.

New CorrDiff NIM Microservices for Higher-Resolution Modeling

NVIDIA CorrDiff is a generative AI model for kilometer-scale super resolution. Its capability to super-resolve typhoons over Taiwan was recently shown at GTC 2024. CorrDiff was trained on the Weather Research and Forecasting (WRF) model’s numerical simulations to generate weather patterns at 12x higher resolution.

High-resolution forecasts capable of visualizing within the fewest kilometers are essential to meteorologists and industries. The insurance and reinsurance industries rely on detailed weather data for assessing risk profiles. But achieving this level of detail using traditional numerical weather prediction models like WRF or High-Resolution Rapid Refresh is often too costly and time-consuming to be practical.

The CorrDiff NIM microservice is 500x faster and 10,000x more energy-efficient than traditional high-resolution numerical weather prediction using CPUs. Also, CorrDiff is now operating at 300x larger scale. It is super-resolving — or increasing the resolution of lower-resolution images or videos — for the entire United States and predicting precipitation events, including snow, ice and hail, with visibility in the kilometers.

Enabling Large Sets of Forecasts With New FourCastNet NIM Microservice

Not every use case requires high-resolution forecasts. Some applications benefit more from larger sets of forecasts at coarser resolution.

State-of-the-art numerical models like IFS and GFS are limited to 50 and 20 sets of forecasts, respectively, due to computational constraints.

The FourCastNet NIM microservice, available today, offers global, medium-range coarse forecasts. By using the initial assimilated state from operational weather centers such as European Centre for Medium-Range Weather Forecasts or National Oceanic and Atmospheric Administration, providers can generate forecasts for the next two weeks, 5,000x faster than traditional numerical weather models.

This opens new opportunities for climate tech providers to estimate risks related to extreme weather at a different scale, enabling them to predict the likelihood of low-probability events that current computational pipelines overlook.

Learn more about CorrDiff and FourCastNet NIM microservices on ai.nvidia.com.

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Whether they’re looking at nanoscale electron behaviors or starry galaxies colliding millions of light years away, many scientists share a common challenge — they must comb through petabytes of data to extract insights that can advance their fields.

With the NVIDIA cuPyNumeric accelerated computing library, researchers can now take their data-crunching Python code and effortlessly run it on CPU-based laptops and GPU-accelerated workstations, cloud servers or massive supercomputers. The faster they can work through their data, the quicker they can make decisions about promising data points, trends worth investigating and adjustments to their experiments.

To make the leap to accelerated computing, researchers don’t need expertise in computer science. They can simply write code using the familiar NumPy interface or apply cuPyNumeric to existing code, following best practices for performance and scalability.

Once cuPyNumeric is applied, they can run their code on one or thousands of GPUs with zero code changes.

The latest version of cuPyNumeric, now available on Conda and GitHub, offers support for the NVIDIA GH200 Grace Hopper Superchip, automatic resource configuration at run time and improved memory scaling. It also supports HDF5, a popular file format in the scientific community that helps efficiently manage large, complex data.

Researchers at the SLAC National Accelerator Laboratory, Los Alamos National Laboratory, Australia National University, UMass Boston, the Center for Turbulence Research at Stanford University and the National Payments Corporation of India are among those who have integrated cuPyNumeric to achieve significant improvements in their data analysis workflows.

Less Is More: Limitless GPU Scalability Without Code Changes

Python is the most common programming language for data science, machine learning and numerical computing, used by millions of researchers in scientific fields including astronomy, drug discovery, materials science and nuclear physics. Tens of thousands of packages on GitHub depend on the NumPy math and matrix library, which had over 300 million downloads last month. All of these applications could benefit from accelerated computing with cuPyNumeric.

Many of these scientists build programs that use NumPy and run on a single CPU-only node — limiting the throughput of their algorithms to crunch through increasingly large datasets collected by instruments like electron microscopes, particle colliders and radio telescopes.

cuPyNumeric helps researchers keep pace with the growing size and complexity of their datasets by providing a drop-in replacement for NumPy that can scale to thousands of GPUs. cuPyNumeric doesn’t require code changes when scaling from a single GPU to a whole supercomputer. This makes it easy for researchers to run their analyses on accelerated computing systems of any size.

Solving the Big Data Problem, Accelerating Scientific Discovery

Researchers at SLAC National Accelerator Laboratory, a U.S. Department of Energy lab operated by Stanford University, have found that cuPyNumeric helps them speed up X-ray experiments conducted at the Linac Coherent Light Source.

A SLAC team focused on materials science discovery for semiconductors found that cuPyNumeric accelerated its data analysis application by 6x, decreasing run time from minutes to seconds. This speedup allows the team to run important analyses in parallel when conducting experiments at this highly specialized facility.

By using experiment hours more efficiently, the team anticipates it will be able to discover new material properties, share results and publish work more quickly.

Other institutions using cuPyNumeric include: 

• Australia National University, where researchers used cuPyNumeric to scale the Levenberg-Marquardt optimization algorithm to run on multi-GPU systems at the country’s National Computational Infrastructure. While the algorithm can be used for many applications, the researchers’ initial target is large-scale climate and weather models.
• Los Alamos National Laboratory, where researchers are applying cuPyNumeric to accelerate data science, computational science and machine learning algorithms. cuPyNumeric will provide them with additional tools to effectively use the recently launched Venado supercomputer, which features over 2,500 NVIDIA GH200 Grace Hopper Superchips.
• Stanford University’s Center for Turbulence Research, where researchers are developing Python-based computational fluid dynamics solvers that can run at scale on large accelerated computing clusters using cuPyNumeric. These solvers can seamlessly integrate large collections of fluid simulations with popular machine learning libraries like PyTorch, enabling complex applications including online training and reinforcement learning.
• UMass Boston, where a research team is accelerating linear algebra calculations to analyze microscopy videos and determine the energy dissipated by active materials. The team used cuPyNumeric to decompose a matrix of 16 million rows and 4,000 columns.
• National Payments Corporation of India, the organization behind a real-time digital payment system used by around 250 million Indians daily and expanding globally. NPCI uses complex matrix calculations to track transaction paths between payers and payees. With current methods, it takes about 5 hours to process data for a one-week transaction window on CPU systems. A trial showed that applying cuPyNumeric to accelerate the calculations on multi-node NVIDIA DGX systems could speed up matrix multiplication by 50x, enabling NPCI to process larger transaction windows in less than an hour and detect suspected money laundering in near real time.

To learn more about cuPyNumeric, see a live demo in the NVIDIA booth at the Supercomputing 2024 conference in Atlanta, join the theater talk in the expo hall and participate in the cuPyNumeric workshop.   

Watch the NVIDIA special address at SC24.

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Since its introduction, the NVIDIA Hopper architecture has transformed the AI and high-performance computing (HPC) landscape, helping enterprises, researchers and developers tackle the world’s most complex challenges with higher performance and greater energy efficiency.

During the Supercomputing 2024 conference, NVIDIA announced the availability of the NVIDIA H200 NVL PCIe GPU — the latest addition to the Hopper family. H200 NVL is ideal for organizations with data centers looking for lower-power, air-cooled enterprise rack designs with flexible configurations to deliver acceleration for every AI and HPC workload, regardless of size.

According to a recent survey, roughly 70% of enterprise racks are 20kW and below and use air cooling. This makes PCIe GPUs essential, as they provide granularity of node deployment, whether using one, two, four or eight GPUs — enabling data centers to pack more computing power into smaller spaces. Companies can then use their existing racks and select the number of GPUs that best suits their needs. 

Enterprises can use H200 NVL to accelerate AI and HPC applications, while also improving energy efficiency through reduced power consumption. With a 1.5x memory increase and 1.2x bandwidth increase over NVIDIA H100 NVL, companies can use H200 NVL to fine-tune LLMs within a few hours and deliver up to 1.7x faster inference performance. For HPC workloads, performance is boosted up to 1.3x over H100 NVL and 2.5x over the NVIDIA Ampere architecture generation. 

Complementing the raw power of the H200 NVL is NVIDIA NVLink technology. The latest generation of NVLink provides GPU-to-GPU communication 7x faster than fifth-generation PCIe — delivering higher performance to meet the needs of HPC, large language model inference and fine-tuning. 

The NVIDIA H200 NVL is paired with powerful software tools that enable enterprises to accelerate applications from AI to HPC. It comes with a five-year subscription for NVIDIA AI Enterprise, a cloud-native software platform for the development and deployment of production AI. NVIDIA AI Enterprise includes NVIDIA NIM microservices for the secure, reliable deployment of high-performance AI model inference. 

Companies Tapping Into Power of H200 NVL

With H200 NVL, NVIDIA provides enterprises with a full-stack platform to develop and deploy their AI and HPC workloads. 

Customers are seeing significant impact for multiple AI and HPC use cases across industries, such as visual AI agents and chatbots for customer service, trading algorithms for finance, medical imaging for improved anomaly detection in healthcare, pattern recognition for manufacturing, and seismic imaging for federal science organizations. 

Dropbox is harnessing NVIDIA accelerated computing for its services and infrastructure.

“Dropbox handles large amounts of content, requiring advanced AI and machine learning capabilities,” said Ali Zafar, VP of Infrastructure at Dropbox. “We’re exploring H200 NVL to continually improve our services and bring more value to our customers.”

The University of New Mexico has been using NVIDIA accelerated computing in various research and academic applications. 

“As a public research university, our commitment to AI enables the university to be on the forefront of scientific and technological advancements,” said Prof. Patrick Bridges, director of the UNM Center for Advanced Research Computing. “As we shift to H200 NVL, we’ll be able to accelerate a variety of applications, including data science initiatives, bioinformatics and genomics research, physics and astronomy simulations, climate modeling and more.”

H200 NVL Available Across Ecosystem

Dell Technologies, Hewlett Packard Enterprise, Lenovo and Supermicro are expected to deliver a wide range of configurations supporting H200 NVL. 

Additionally, H200 NVL will be available in platforms from Aivres, ASRock Rack, ASUS, GIGABYTE, Ingrasys, Inventec, MSI, Pegatron, QCT, Wistron and Wiwynn.

Some systems are based on the NVIDIA MGX modular architecture, which enables computer makers to quickly and cost-effectively build a vast array of data center infrastructure designs.

Platforms with H200 NVL will be available from NVIDIA’s global systems partners beginning in December. To complement availability from leading global partners, NVIDIA is also developing an Enterprise Reference Architecture for H200 NVL systems. 

The reference architecture will incorporate NVIDIA’s expertise and design principles, so partners and customers can design and deploy high-performance AI infrastructure based on H200 NVL at scale. This includes full-stack hardware and software recommendations, with detailed guidance on optimal server, cluster and network configurations. Networking is optimized for the highest performance with the NVIDIA Spectrum-X Ethernet platform.

NVIDIA technologies will be showcased on the showroom floor at SC24, taking place at the Georgia World Congress Center through Nov. 22. To learn more, watch NVIDIA’s special address.

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To meet demand for Blackwell, now in full production, Foxconn, the world’s largest electronics manufacturer, is using NVIDIA Omniverse. The platform for developing industrial AI simulation applications is helping bring facilities in the U.S., Mexico and Taiwan online faster than ever.

Foxconn uses NVIDIA Omniverse to virtually integrate their facility and equipment layouts, NVIDIA Isaac Sim for autonomous robot testing and simulation, and NVIDIA Metropolis for vision AI.

Omniverse enables industrial developers to maximize efficiency through test and optimization in a digital twin before deploying costly change-orders to the physical world. Foxconn expects its Mexico facility alone to deliver significant cost savings and a reduction in kilowatt-hour usage of more than 30% annually.

World’s Largest Electronics Maker Plans With Omniverse and AI

To meet demands at Foxconn, factory planners are building physical AI-powered robotic factories with Omniverse and NVIDIA AI.

The company has built digital twins with Omniverse that allow their teams  to virtually integrate facility and equipment information from leading industry applications, such as Siemens Teamcenter X and Autodesk Revit. Floor plan layouts are optimized first in the digital twin, and planners can locate optimal camera positions that help measure and identify ways to streamline operations with Metropolis visual AI agents.

In the construction process, the Foxconn teams use the Omniverse digital twin as the source of truth to communicate and validate the accurate layout and placement of equipment.

Virtual integration on Omniverse offers significant advantages, potentially saving factory planners millions by reducing costly change orders in real-world operations.

Delivering Robotics for Manufacturing With Omniverse Digital Twin

Once the digital twin of the factory is built, it becomes a virtual gym for Foxconn’s fleets of autonomous robots including industrial manipulators and autonomous mobile robots. Foxconn’s robot developers can simulate, test and validate their AI robot models in NVIDIA Isaac Sim before deploying to their real world robots.

Using Omniverse, Foxconn can simulate robot AIs before deploying to NVIDIA Jetson-driven autonomous mobile robots.

On assembly lines, they can simulate with Isaac Manipulator libraries and AI models for automated optical inspection, object identification, defect detection and trajectory planning.

Omniverse also enables their facility planners to test and optimize intelligent camera placement before installing in the physical world – ensuring they have complete coverage of the factory floor to support worker safety, and provide the foundation for visual AI agent frameworks.

Creating Efficiencies While Building Resilient Supply Chains

Using NVIDIA Omniverse and AI, Foxconn plans to replicate its precision production lines across the world. This will enable it to quickly deploy high-quality production facilities that meet unified standards, increasing the company’s competitive edge and adaptability in the market.

Foxconn’s ability to rapidly replicate will accelerate its global deployments and enhance its resilience in the supply chain in the face of disruptions, as it can quickly adjust production strategies and reallocate resources to ensure continuity and stability to meet changing demands.

Foxconn’s Mexico facility will begin production early next year and the Taiwan location will begin production in December.

Learn more about Blackwell and Omniverse.

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