Posts in category: nVidia AI

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.

See notice regarding software product information.

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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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More than 96% of all manufactured goods — ranging from everyday products, like laundry detergent and food packaging, to advanced industrial components, such as semiconductors, batteries and solar panels — rely on chemicals that cannot be replaced with alternative materials.

With AI and the latest technological advancements, researchers and developers are studying ways to create novel materials that could address the world’s toughest challenges, such as energy storage and environmental remediation.

Announced today at the Supercomputing 2024 conference in Atlanta, the NVIDIA ALCHEMI NIM microservice accelerates such research by optimizing AI inference for chemical simulations that could lead to more efficient and sustainable materials to support the renewable energy transition.

It’s one of the many ways NVIDIA is supporting researchers, developers and enterprises to boost energy and resource efficiency in their workflows, including to meet requirements aligned with the global Net Zero Initiative.

NVIDIA ALCHEMI for Material and Chemical Simulations

Exploring the universe of potential materials, using the nearly infinite combinations of chemicals — each with unique characteristics — can be extremely complex and time consuming. Novel materials are typically discovered through laborious, trial-and-error synthesis and testing in a traditional lab.

Many of today’s plastics, for example, are still based on material discoveries made in the mid-1900s.

More recently, AI has emerged as a promising accelerant for chemicals and materials innovation.

With the new ALCHEMI NIM microservice, researchers can test chemical compounds and material stability in simulation, in a virtual AI lab, which reduces costs, energy consumption and time to discovery.

For example, running MACE-MP-0, a pretrained foundation model for materials chemistry, on an NVIDIA H100 Tensor Core GPU, the new NIM microservice speeds evaluations of a potential composition’s simulated long-term stability 100x. The below figure shows a 25x speedup from using the NVIDIA Warp Python framework for high-performance simulation, followed by a 4x speedup with in-flight batching. All in all, evaluating 16 million structures would have taken months — with the NIM microservice, it can be done in just hours.

By letting scientists examine more structures in less time, the NIM microservice can boost research on materials for use with solar and electric batteries, for example, to bolster the renewable energy transition.

NVIDIA also plans to release NIM microservices that can be used to simulate the manufacturability of novel materials — to determine how they might be brought from test tubes into the real world in the form of batteries, solar panels, fertilizers, pesticides and other essential products that can contribute to a healthier, greener planet.

SES AI, a leading developer of lithium-metal batteries, is using the NVIDIA ALCHEMI NIM microservice with the AIMNet2 model to accelerate the identification of electrolyte materials used for electric vehicles.

“SES AI is dedicated to advancing lithium battery technology through AI-accelerated material discovery, using our Molecular Universe Project to explore and identify promising candidates for lithium metal electrolyte discovery,” said Qichao Hu, CEO of SES AI. “Using the ALCHEMI NIM microservice with AIMNet2 could drastically improve our ability to map molecular properties, reducing time and costs significantly and accelerating innovation.”

SES AI recently mapped 100,000 molecules in half a day, with the potential to achieve this in under an hour using ALCHEMI. This signals how the microservice is poised to have a transformative impact on material screening efficiency.

Looking ahead, SES AI aims to map the properties of up to 10 billion molecules within the next couple of years, pushing the boundaries of AI-driven, high-throughput discovery.

The new microservice will soon be available for researchers to test for free through the NVIDIA NGC catalog — be notified of ALCHEMI’s launch. It will also be downloadable from build.nvidia.com, and the production-grade NIM microservice will be offered through the NVIDIA AI Enterprise software platform.

Learn more about the NVIDIA ALCHEMI NIM microservice, and hear the latest on how AI and supercomputing are supercharging researchers and developers’ workflows by joining NVIDIA at SC24, running through Friday, Nov. 22.

See notice regarding software product information.

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Cloud-native technologies have become crucial for developers to create and implement scalable applications in dynamic cloud environments.

This week at KubeCon + CloudNativeCon North America 2024, one of the most-attended conferences focused on open-source technologies, Chris Lamb, vice president of computing software platforms at NVIDIA, delivered a keynote outlining the benefits of open source for developers and enterprises alike — and NVIDIA offered nearly 20 interactive sessions with engineers and experts.

The Cloud Native Computing Foundation (CNCF), part of the Linux Foundation and host of KubeCon, is at the forefront of championing a robust ecosystem to foster collaboration among industry leaders, developers and end users.

As a member of CNCF since 2018, NVIDIA is working across the developer community to contribute to and sustain cloud-native open-source projects. Our open-source software and more than 750 NVIDIA-led open-source projects help democratize access to tools that accelerate AI development and innovation.

Empowering Cloud-Native Ecosystems

NVIDIA has benefited from the many open-source projects under CNCF and has made contributions to dozens of them over the past decade. These actions help developers as they build applications and microservice architectures aligned with managing AI and machine learning workloads.

Kubernetes, the cornerstone of cloud-native computing, is undergoing a transformation to meet the challenges of AI and machine learning workloads. As organizations increasingly adopt large language models and other AI technologies, robust infrastructure becomes paramount.

NVIDIA has been working closely with the Kubernetes community to address these challenges. This includes:

• Work on dynamic resource allocation (DRA) that allows for more flexible and nuanced resource management. This is crucial for AI workloads, which often require specialized hardware. NVIDIA engineers played a key role in designing and implementing this feature.
• Leading efforts in KubeVirt, an open-source project extending Kubernetes to manage virtual machines alongside containers. This provides a unified, cloud-native approach to managing hybrid infrastructure.
• Development of NVIDIA GPU Operator, which automates the lifecycle management of NVIDIA GPUs in Kubernetes clusters. This software simplifies the deployment and configuration of GPU drivers, runtime and monitoring tools, allowing organizations to focus on building AI applications rather than managing infrastructure.

The company’s open-source efforts extend beyond Kubernetes to other CNCF projects:

• NVIDIA is a key contributor to Kubeflow, a comprehensive toolkit that makes it easier for data scientists and engineers to build and manage ML systems on Kubernetes. Kubeflow reduces the complexity of infrastructure management and allows users to focus on developing and improving ML models.
• NVIDIA has contributed to the development of CNAO, which manages the lifecycle of host networks in Kubernetes clusters.
• NVIDIA has also added to Node Health Check, which provides virtual machine high availability.

And NVIDIA has assisted with projects that address the observability, performance and other critical areas of cloud-native computing, such as:

• Prometheus: Enhancing monitoring and alerting capabilities
• Envoy: Improving distributed proxy performance
• OpenTelemetry: Advancing observability in complex, distributed systems
• Argo: Facilitating Kubernetes-native workflows and application management

Community Engagement 

NVIDIA engages the cloud-native ecosystem by participating in CNCF events and activities, including:

• Collaboration with cloud service providers to help them onboard new workloads.
• Participation in CNCF’s special interest groups and working groups on AI discussions.
• Participation in industry events such as KubeCon + CloudNativeCon, where it shares insights on GPU acceleration for AI workloads.
• Work with CNCF-adjacent projects in the Linux Foundation as well as many partners.

This translates into extended benefits for developers, such as improved efficiency in managing AI and ML workloads; enhanced scalability and performance of cloud-native applications; better resource utilization, which can lead to cost savings; and simplified deployment and management of complex AI infrastructures.

As AI and machine learning continue to transform industries, NVIDIA is helping advance cloud-native technologies to support compute-intensive workloads. This includes facilitating the migration of legacy applications and supporting the development of new ones.

These contributions to the open-source community help developers harness the full potential of AI technologies and strengthen Kubernetes and other CNCF projects as the tools of choice for AI compute workloads.

Check out NVIDIA’s keynote at KubeCon + CloudNativeCon North America 2024 delivered by Chris Lamb, where he discusses the importance of CNCF projects in building and delivering AI in the cloud and NVIDIA’s contributions to the community to push the AI revolution forward.

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Grab a pitchfork and fire up the tractor — the fields of GeForce NOW are about to get a whole lot greener with Farming Simulator 25.

Whether looking for a time-traveling adventure, cozy games or epic action, GeForce NOW has something for everyone with over 2,000 games in its cloud library. Nine titles arrive this week, including the new 4X historical grand strategy game Ara: History Untold from Oxide Games and Xbox Game Studios.

And in this season of giving, GeForce NOW will offer members new rewards and more this month. This week, GeForce NOW is spreading cheer with a new reward for members that’s sure to delight Throne and Liberty fans. Get ready to add a dash of mischief and a sprinkle of wealth to the epic adventures in the sprawling world of this massively multiplayer online role-playing game.

Plus, the NVIDIA app is officially released for download this week. GeForce users can use it to access GeForce NOW to play their games with RTX performance when they’re away from their gaming rigs or don’t want to wait around for their games to update and patch.

A Cloud Gaming Bounty

Get ready to plow the fields and tend to crops anywhere with GeForce NOW.

Farming Simulator 25 from Giants Software launched in the cloud for members to stream, bringing a host of new features and improvements — including the introduction of rice as a crop type, complete with specialized machinery and techniques for planting, flooding fields and harvesting.

This expansion into rice farming is accompanied by a new Asian-themed map that offers players a lush landscape filled with picturesque rice paddies to cultivate. The game will also include two other diverse environments: a spacious North American setting and a scenic Central European location, allowing farmers to build their agricultural empires in varied terrains. Don’t forget about the addition of water buffaloes and goats, as well as the introduction of animal offspring for a new layer of depth to farm management.

Be the cream of the crop streaming with a Performance or Ultimate membership. Performance members get up to 1440p 60 frames per second and Ultimate streams at up to 4K and 120 fps for the most incredible levels of realism and variety. Whether tackling agriculture, forestry and animal husbandry single-handedly or together with friends in cooperative multiplayer mode, experience farming life like never before with GeForce NOW.

Mischief Managed

Whether new to the game or a seasoned adventurer, GeForce NOW members can claim a special PC-exclusive reward to use in Amazon Games’ hit title Throne and Liberty. The reward includes 200 Ornate Coins and a PC-exclusive mischievous youngster named Gneiss Amitoi that will enhance the Throne and Liberty journey as members forge alliances, wage epic battles and uncover hidden treasures.

Ornate Coins allow players to acquire morphs for animal shapeshifting, autonomous pets named Amitois, exclusive cosmetic items, experience boosters and inventory expansions. Gneiss Youngster Amitoi is a toddler-aged prankster that randomly targets players and non-playable characters with its tricks. While some of its mischief can be mean-spirited, it just wants attention, and will pout and roll back to its adventurer’s side if ignored, adding an entertaining dynamic to the journey through the world of Throne and Liberty.

Members who’ve opted in to GeForce NOW’s Rewards program can check their email for instructions on how to redeem the reward. Ultimate and Performance members can start redeeming the reward today, while free members will be able to claim it starting tomorrow, Nov. 15. It’s available through Tuesday, Dec. 10, first come, first served.

Rewriting History

Explore, build, lead and conquer a nation in Ara: History Untold, where every choice will shape the world and define a player’s legacy. It’s now available for GeForce NOW members to stream.

Ara: History Untold offers a fresh take on 4X historical grand strategy games. Players will prove their worth by guiding their citizens through history to the pinnacles of human achievement. Explore new lands, develop arts and culture, and engage in diplomacy — or combat — with other nations, before ultimately claiming the mantle of the greatest nation of all time.

Members can craft their own unique story of triumph and achievement by streaming the game across devices from the cloud. GeForce NOW Performance and Ultimate members can enjoy longer gaming sessions and faster access to servers than free users, perfect for crafting sprawling empires and engaging in complex diplomacy without worrying about local hardware limitations.

New Games Are Knocking

GeForce NOW brings the new Wuthering Waves update “When the Night Knocks” for members this week. Version 1.4 brings a wealth of new content, including two new Resonators, Camellya and Lumi, along with powerful new weapons, including the five-star Red Spring and the four-star event weapon Somnoire Anchor. Dive into the Somnoire Adventure Event, Somnium Labyrinth, and enjoy a variety of log-in rewards, combat challenges and exploration activities. The update also includes Camellya’s companion story, a new Phantom Echo and introduces the exciting Weapon Projection feature.

Members can look for the following games available to stream in the cloud this week:

• Farming Simulator 25 (New release on Steam, Nov. 12)
• Sea Power: Naval Combat in the Missile Age (New release on Steam, Nov. 12)
• Industry Giant 4.0 (New release Steam, Nov. 15)
• Ara: History Untold (Steam and Xbox, available on PC Game Pass)
• Call of Duty: Black Ops Cold War (Steam and Battle.net)
• Call of Duty: Vanguard (Steam and Battle.net)
• Magicraft (Steam)
• Crash Bandicoot N. Sane Trilogy (Steam and Xbox, available on PC Game Pass)
• Spyro Reignited Trilogy (Steam and Xbox, available on PC Game Pass)

What are you planning to play this weekend? Let us know on X or in the comments below.

the last thing your left hand touched is your video game weapon – what was it?

— NVIDIA GeForce NOW (@NVIDIAGFN) November 13, 2024

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Diabetics — or others monitoring their sugar intake — may look at a cookie and wonder, “How will eating this affect my glucose levels?” A generative AI model can now predict the answer.

Researchers from the Weizmann Institute of Science, Tel Aviv-based startup Pheno.AI and NVIDIA led the development of GluFormer, an AI model that can predict an individual’s future glucose levels and other health metrics based on past glucose monitoring data.

Data from continuous glucose monitoring could help more quickly diagnose patients with prediabetes or diabetes, according to Harvard Health Publishing and NYU Langone Health. GluFormer’s AI capabilities can further enhance the value of this data, helping clinicians and patients spot anomalies, predict clinical trial outcomes and forecast health outcomes up to four years in advance.

The researchers showed that, after adding dietary intake data into the model, GluFormer can also predict how a person’s glucose levels will respond to specific foods and dietary changes, enabling precision nutrition.

Accurate predictions of glucose levels for those at high risk of developing diabetes could enable doctors and patients to adopt preventative care strategies sooner, improving patient outcomes and reducing the economic impact of diabetes, which could reach $2.5 trillion globally by 2030.

AI tools like GluFormer have the potential to help the hundreds of millions of adults with diabetes. The condition currently affects around 10% of the world’s adults — a figure that could potentially double by 2050 to impact over 1.3 billion people. It’s one of the 10 leading causes of death globally, with side effects including kidney damage, vision loss and heart problems.

GluFormer is a transformer model, a kind of neural network architecture that tracks relationships in sequential data. It’s the same architecture as OpenAI’s GPT models — in this case generating glucose levels instead of text.

“Medical data, and continuous glucose monitoring in particular, can be viewed as sequences of diagnostic tests that trace biological processes throughout life,” said Gal Chechik, senior director of AI research at NVIDIA. “We found that the transformer architecture, developed for long text sequences, can take a sequence of medical tests and predict the results of the next test. In doing so, it learns something about how the diagnostic measurements develop over time.”

The model was trained on 14 days of glucose monitoring data from over 10,000 non-diabetic study participants, with data collected every 15 minutes through a wearable monitoring device. The data was collected as part of the Human Phenotype Project, an initiative by Pheno.AI, a startup that aims to improve human health through data collection and analysis.

“Two important factors converged at the same time to enable this research: the maturing of generative AI technology powered by NVIDIA and the collection of large-scale health data by the Weizmann Institute,” said the paper’s lead author, Guy Lutsker, an NVIDIA researcher and Ph.D. student at the Weizmann Institute of Science. “It put us in the unique position to extract interesting medical insights from the data.”

The research team validated GluFormer across 15 other datasets and found it generalizes well to predict health outcomes for other groups, including those with prediabetes, type 1 and type 2 diabetes, gestational diabetes and obesity.

They used a cluster of NVIDIA Tensor Core GPUs to accelerate model training and inference.

Beyond glucose levels, GluFormer can predict medical values including visceral adipose tissue, a measure of the amount of body fat around organs like the liver and pancreas; systolic blood pressure, which is associated with diabetes risk; and apnea-hypopnea index, a measurement for sleep apnea, which is linked to type 2 diabetes.

Read the GluFormer research paper on Arxiv.

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