Posts in category: nVidia AI

The rapid evolution of generative AI has created countless opportunities for innovation across industry and research. As is often the case with state-of-the-art technology, this evolution has also shifted the landscape of cybersecurity threats, creating new security requirements. Critical infrastructure cybersecurity is advancing to thwart the next wave of emerging threats in the AI era.

Leading operational technology (OT) providers today showcased at the S4 conference for industrial control systems (ICS) and OT cybersecurity how they’re adopting the NVIDIA cybersecurity AI platform to deliver real-time threat detection and critical infrastructure protection.

Armis, Check Point, CrowdStrike, Deloitte and World Wide Technology (WWT) are integrating the platform to help customers bolster critical infrastructure, such as energy, utilities and manufacturing facilities, against cyber threats.

Critical infrastructure operates in highly complex environments, where the convergence of IT and OT, often accelerated by digital transformation, creates a perfect storm of vulnerabilities. Traditional cybersecurity measures are no longer sufficient to address these emerging threats.

By harnessing NVIDIA’s cybersecurity AI platform, these partners can provide exceptional visibility into critical infrastructure environments, achieving robust and adaptive security while delivering operational continuity.

The platform integrates NVIDIA’s accelerated computing and AI, featuring NVIDIA BlueField-3 DPUs, NVIDIA DOCA and the NVIDIA Morpheus AI cybersecurity framework, part of the NVIDIA AI Enterprise. This combination enables real-time threat detection, empowering cybersecurity professionals to respond swiftly at the edge and across networks.

Unlike conventional solutions that depend on intrusive methods or software agents, BlueField-3 DPUs function as a virtual security overlay. They inspect network traffic and safeguard host integrity without disrupting operations. Acting as embedded sensors within each server, they stream telemetry data to NVIDIA Morpheus, enabling detailed monitoring of host activities, network traffic and application behaviors — seamlessly and without operational impact.

Driving Cybersecurity Innovation Across Industries

Integrating Armis Centrix, Armis’ AI-powered cyber exposure management platform, with NVIDIA cybersecurity AI helps secure critical infrastructure like energy, manufacturing, healthcare and transportation.

“OT environments are increasingly targeted by sophisticated cyber threats, requiring robust solutions that ensure both security and operational continuity,” said Nadir Izrael, chief technology officer and cofounder of Armis. “Combining Armis’ unmatched platform for OT security and cyber exposure management with NVIDIA BlueField-3 DPUs enables organizations to comprehensively protect cyber-physical systems without disrupting operations.”

CrowdStrike is helping secure critical infrastructure such as ICS and OT by deploying its CrowdStrike Falcon security agent on BlueField-3 DPUs to boost real-time AI-powered threat detection and response.

“OT environments are under increasing threat, demanding AI-powered security that adapts in real time,” said Raj Rajamani, head of products at CrowdStrike. “By integrating NVIDIA BlueField-3 DPUs with the CrowdStrike Falcon platform, we’re extending industry-leading protection to critical infrastructure without disrupting operations — delivering unified protection at the edge and helping organizations stay ahead of modern threats.”

Deloitte is driving customers’ digital transformation, enabled by NVIDIA’s cybersecurity AI platform, to help meet the demands of breakthrough technologies that require real-time, granular visibility into data center networks to defend against increasingly sophisticated threats.

“Protecting OT and ICS systems is becoming increasingly challenging as organizations embrace digital transformation and interconnected technologies,” said Dmitry Dudorov, an AI security leader at Deloitte U.K. “Harnessing NVIDIA’s cybersecurity AI platform can enable organizations to determine threat detection, enhance resilience and safeguard their infrastructure to accelerate their efforts.”

A Safer Future, Powered by AI

NVIDIA’s cybersecurity AI platform, combined with the expertise of ecosystem partners, offers a powerful and scalable solution to protect critical infrastructure environments against evolving threats. Bringing NVIDIA AI and accelerated computing to the forefront of OT security can help organizations protect what matters most — now and in the future.

Learn more by attending the NVIDIA GTC global AI conference, running March 17-21, where Armis, Check Point and CrowdStrike  cybersecurity leaders will host sessions about their collaborations with NVIDIA.

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NVIDIA’s contributions to accelerating medical imaging, genomics, computational chemistry and AI-powered robotics were honored Friday at the Precision Medicine World Conference in Santa Clara, California, where NVIDIA founder and CEO Jensen Huang received a Luminary award.

The Precision Medicine World Conference brings together healthcare leaders, top global researchers and innovators across biotechnology. Its Luminary award recognizes people transforming healthcare by advancing precision medicine in the clinic.

For nearly two decades, NVIDIA has advanced computing in healthcare — working with researchers and industry leaders to build instruments that enable scientists to better understand life sciences, medical imaging and genomics.

“We built, if you will, a computational instrument. Not a gene sequencer and all the incredible scientific instruments that you all talk about here — in our case, it was a programmable scientific instrument,” Huang said in his acceptance speech. “We built it in service of researchers and scientists as you strive to better understand life in our universe.”

The first use of accelerated computing in life sciences was in the 2000s — and the introduction of the NVIDIA CUDA parallel computing platform in 2006 paved the path for researchers to demonstrate how NVIDIA GPUs could be used in medical imaging applications like CT reconstruction.

“NVIDIA developed and continues to develop GPUs that are at the heart of AI and machine learning that are changing the world, including precision medicine,” said Dr. Gad Getz, an internationally acclaimed leader in cancer genomics and the director of bioinformatics at the Massachusetts General Hospital, as he presented the award.

Today, NVIDIA AI and accelerated computing is “impacting analysis, interpretation and translation of sequencing data, new sequencing technologies, imaging data, spatial technologies, single-cell genomics, proteomics, molecular dynamics and drug development, as well as the large language models that can be used by doctors, patients, students and teachers to learn this field,” Getz said.

Advancing Precision Medicine With Accelerated Computing 

Huang spoke about the ways AI will support the work of doctors, scientists and researchers advancing medicine. By investing in AI, he explained, research organizations and businesses can set up a powerful flywheel that continuously improves in accuracy, efficiency and insights by integrating additional data and feedback from every expert who interacts with it over time.

“Even though people say you want humans in the loop with AI, in fact, the opposite is true. You want AI in the loop with humans,” Huang said. “The reason for that is because when the AI is in the loop with humans, it codifies our life experience. If there’s an AI in the loop with every single researcher, scientist, engineer and marketer — every single employee in your company — that AI in the loop codifies that life experience and keeps it in the company.”

Looking ahead, Huang said that “in the coming years, AI will advance with incredible speed and revolutionize the healthcare industry. AI will help doctors predict, diagnose and treat disease in ways we never thought possible. It will scan a patient’s genome in seconds, identifying risks before symptoms even appear. AI will build a digital twin of us and model how a tumor evolves, predicting which treatments will work best.”

“I wouldn’t be surprised if before 2030, within this decade, we’re representing basically all cells,” said Huang. “We have a representation of it, we understand the language of it, and we can predict what happens.”

Huang predicts that surgical robots will perform minimally invasive procedures with unparalleled precision, robotic caregivers will assist nurses and other healthcare professionals, and robotic labs will run experiments around the clock, accelerating drug discovery. AI assistants, he said, will let doctors focus on what matters most to them: patients.

In his talk, Huang also thanked the medical research community and highlighted how great breakthroughs come from partnerships between technology companies, researchers, biotech firms and healthcare leaders. Over 4,000 healthcare companies are part of the NVIDIA Inception program designed to help startups evolve faster.

Learn more about accelerated computing in healthcare at NVIDIA GTC, a global AI conference taking place March 17-21 in San Jose, California.

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Tara Chklovski has spent much of her career inspiring young women to take on some of the world’s biggest challenges using technology.

The founder and CEO of education nonprofit Technovation joined the AI Podcast in 2019 to discuss the AI Family Challenge. Now, she returns to explain how inclusive AI makes the world a better and, crucially, less boring place.

In this episode of the NVIDIA AI Podcast, Chklovski and Anshita Saini, a Technovation alumna and member of the technical staff at OpenAI, explore how the nonprofit empowers girls worldwide through technology education.

They discuss the organization’s growth from its early days to its current focus on AI education and real-world problem-solving.

In addition, Saini shares her journey from creating an app that helped combat a vaping crisis at her high school, to her first exposure to AI, through to her current role working on ChatGPT. She also talks about Wiser AI, an initiative she recently founded to support women leaders and other underrepresented voices in artificial intelligence.

Technovation is preparing the next generation of female leaders in AI and technology. Learn about the opportunity to mentor a team of girls for the 2025 season.

And learn more about the latest technological advancements by registering for NVIDIA GTC, the conference for the era of AI, taking place March 17-21.

Time Stamps

2:21 – Recognizing AI’s revolutionary potential in 2016.

5:39 – Technovation’s pioneering approach to incorporating ChatGPT in education.

12:17 – Saini builds an app through Technovation that addressed a real problem at her high school.

29:12 – The importance of having women represented on software development teams.

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NVIDIA’s Louis Stewart on How AI Is Shaping Workforce Development

Louis Stewart, head of strategic initiatives for NVIDIA’s global developer ecosystem, discusses why workforce development is crucial for maximizing AI benefits. He emphasizes the importance of AI education, inclusivity and public-private partnerships in preparing the global workforce for the future. Engaging with AI tools and understanding their impact on the workforce landscape is vital to ensuring these changes benefit everyone.

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 AI Can Enhance Disability Inclusion and Education

U.S. Special Advisor on International Disability Rights at the U.S. Department of State Sara Minkara and Timothy Shriver, chairman of the board of Special Olympics, discuss AI’s potential to enhance disability inclusion and education. They discuss the need to hear voices from the disability community in conversations about AI development and policy. They also cover why building an inclusive future is good for society’s collective cultural, financial and social well-being.

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Every year, venomous snakes kill over 100,000 people and leave 300,000 more with devastating injuries — amputations, paralysis and permanent disabilities. The victims are often farmers, herders and children in rural communities across sub-Saharan Africa, South Asia and Latin America. For them, a snakebite isn’t just a medical crisis — it’s an economic catastrophe.

Treatment hasn’t changed in over a century. Antivenoms — derived from the blood of immunized animals — are expensive, difficult to manufacture and often ineffective against the deadliest toxins. Worse, they require refrigeration and trained medical staff, making them unreachable for many who need them most.

Now, a team led by Susana Vázquez Torres, a computational biologist working in Nobel Prize winner David Baker’s renowned protein design lab at the University of Washington, has used AI to create entirely new proteins that neutralize lethal snake venom in laboratory tests — faster, cheaper and more effectively than traditional antivenoms. Their research, published in Nature, introduces a new class of synthetic proteins that successfully protect animals from otherwise lethal doses of snake venom toxins.

How AI Cracked the Code on Venom

For over a century, antivenom production has relied on animal immunization, requiring thousands of snake milkings and plasma extractions. Torres and her team hope to replace this with AI-driven protein design, compressing years of work into weeks.

Using NVIDIA Ampere and L40 GPUs, the Baker Lab used its deep learning models, including RFdiffusion and ProteinMPNN, to generate millions of potential antitoxin structures ‘in silico,’ or in computer simulations. Instead of screening a vast number of these proteins in a lab, they used AI tools to predict how the designer proteins would interact with snake venom toxins, rapidly homing in on the most promising designs.

The results were remarkable:

• Newly designed proteins bound tightly to three-finger toxins (3FTx), the deadliest components of elapid venom, effectively neutralizing their toxic effects.
• Lab tests confirmed their high stability and neutralization capability.
• Mouse studies showed an 80-100% survival rate following exposure to lethal neurotoxins.
• The AI-designed proteins were small, heat-resistant and easy to manufacture — no cold storage required.

A Lifeline for the Most Neglected Victims

Unlike traditional antivenoms, which cost hundreds of dollars per dose, it may be possible to mass-produce these AI-designed proteins at low cost, making life-saving treatment available where it’s needed most.

Many snakebite victims can’t afford antivenom or delay seeking care due to cost and accessibility barriers. In some cases, the financial burden of treatment can push entire families deeper into poverty. With an accessible, affordable and shelf-stable antidote, millions of lives — and livelihoods — could be saved.

Beyond Snakebites: The Future of AI-Designed Medicine

This research isn’t just about snakebites. The same AI-driven approach could be used to design precision treatments for viral infections, autoimmune diseases and other hard-to-treat conditions, according to the researchers.

By replacing trial-and-error drug development with algorithmic precision, researchers using AI to design proteins are working to make life-saving medicines more affordable and accessible worldwide.

Torres and her collaborators — including researchers from the Technical University of Denmark, University of Northern Colorado and Liverpool School of Tropical Medicine — are now focused on preparing these venom-neutralizing proteins for clinical testing and large-scale production.

If successful, this AI-driven advancement could save lives, and uplift families and communities around the world.

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AI built for speech is now decoding the language of earthquakes.

A team of researchers from the Earth and environmental sciences division at Los Alamos National Laboratory repurposed Meta’s Wav2Vec-2.0, an AI model designed for speech recognition, to analyze seismic signals from Hawaii’s 2018 Kīlauea volcano collapse.

Their findings, published in Nature Communications, suggest that faults emit distinct signals as they shift — patterns that AI can now track in real time. While this doesn’t mean AI can predict earthquakes, the study marks an important step toward understanding how faults behave before a slip event.

“Seismic records are acoustic measurements of waves passing through the solid Earth,” said Christopher Johnson, one of the study’s lead researchers. “From a signal processing perspective, many similar techniques are applied for both audio and seismic waveform analysis.”

Big earthquakes don’t just shake the ground — they upend economies. In the past five years, quakes in Japan, Turkey and California have caused tens of billions of dollars in damage and displaced millions of people.

That’s where AI comes in. Led by Johnson, along with Kun Wang and Paul Johnson, the Los Alamos team tested whether speech-recognition AI could make sense of fault movements — deciphering the tremors like words in a sentence.

To test their approach, the team used data from the dramatic 2018 collapse of Hawaii’s Kīlauea caldera, which triggered a series of earthquakes over three months.

The AI analyzed seismic waveforms and mapped them to real-time ground movement, revealing that faults might “speak” in patterns resembling human speech.

Speech recognition models like Wav2Vec-2.0 are well-suited for this task because they excel at identifying complex, time-series data patterns — whether involving human speech or the Earth’s tremors.

The AI model outperformed traditional methods, such as gradient-boosted trees, which struggle with the unpredictable nature of seismic signals. Gradient-boosted trees build multiple decision trees in sequence, refining predictions by correcting previous errors at each step.

However, these models struggle with highly variable, continuous signals like seismic waveforms. In contrast, deep learning models like Wav2Vec-2.0 excel at identifying underlying patterns.

How AI Was Trained to Listen to the Earth

Unlike previous machine learning models that required manually labeled training data, the researchers used a self-supervised learning approach to train Wav2Vec-2.0. The model was pretrained on continuous seismic waveforms and then fine-tuned using real-world data from Kīlauea’s collapse sequence.

NVIDIA accelerated computing played a crucial role in processing vast amounts of seismic waveform data in parallel. High-performance NVIDIA GPUs accelerated training, enabling the AI to efficiently extract meaningful patterns from continuous seismic signals.

What’s Still Missing: Can AI Predict Earthquakes?

While the AI showed promise in tracking real-time fault shifts, it was less effective at forecasting future displacement. Attempts to train the model for near-future predictions — essentially, asking it to anticipate a slip event before it happens — yielded inconclusive results.

“We need to expand the training data to include continuous data from other seismic networks that contain more variations in naturally occurring and anthropogenic signals,” he explained.

A Step Toward Smarter Seismic Monitoring

Despite the challenges in forecasting, the results mark an intriguing advancement in earthquake research. This study suggests that AI models designed for speech recognition may be uniquely suited to interpreting the intricate, shifting signals faults generate over time.

“This research, as applied to tectonic fault systems, is still in its infancy,” Johnson. “The study is more analogous to data from laboratory experiments than large earthquake fault zones, which have much longer recurrence intervals. Extending these efforts to real-world forecasting will require further model development with physics-based constraints.”

So, no, speech-based AI models aren’t predicting earthquakes yet. But this research suggests they could one day — if scientists can teach it to listen more carefully.

Read the full paper, “Automatic Speech Recognition Predicts Contemporaneous Earthquake Fault Displacement,” to dive deeper into the science behind this groundbreaking research.

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GeForce NOW celebrates its fifth anniversary this February with a lineup of five major releases. The month kicks off with Kingdom Come: Deliverance II. Prepare for a journey back in time — Warhorse Studios’ newest medieval role-playing game (RPG) comes to GeForce NOW on its launch day, bringing 15th-century Bohemia to devices everywhere.

Experience the highly anticipated sequel’s stunning open world at GeForce RTX quality in the cloud, available to stream across devices at launch. It leads seven games joining the GeForce NOW library of over 2,000 titles, along with MARVEL vs. CAPCOM Fighting Collection: Arcade Classics.

Chainmail Meets the Cloud

Kingdom Come: Deliverance II continues the epic, open-world RPG saga set in the brutal and realistic medieval world of Bohemia. Continue the story of Henry, a blacksmith’s son turned warrior, as he navigates political intrigue and warfare. Explore a world twice the size of the original, whether in the bustling streets of Kuttenberg or the picturesque Bohemian Paradise.

The game builds on its predecessor’s realistic combat system by introducing crossbows, early firearms and a host of new weapons, while refining its already sophisticated melee combat mechanics. Navigate a complex narrative full of difficult decisions, forge alliances with powerful figures, engage in tactical large-scale battles and face moral dilemmas that impact both the journey and fate of the kingdom — all while experiencing a historically rich environment faithful to the period.

The game also features enhanced graphics powered by GeForce RTX, making it ideal to stream on GeForce NOW even without a game-ready rig. Experience all the medieval action at up to 4K and 120 frames per second with eight-hour sessions using an Ultimate membership, or 1440p and 120 fps with six-hour sessions using a Performance membership. Enjoy seamless gameplay, stunning visuals and smooth performance throughout the vast, immersive world of Bohemia.

Sound the Alarm for New Games

Experience every aspect of a paramedic’s life in Ambulance Life: A Paramedic Simulator from Nacon Games. Quickly reach the accident site, take care of the injured and apply first aid. Each accident is different. It’s up to players to adapt and make the right choices while being fast and efficient. Explore three different Districts containing a variety of environments. At each accident site, analyze the situation to precisely determine the right treatment for each patient. Build a reputation, unlock new tools and get assigned to new districts with thrilling new situations.

Look for the following games available to stream in the cloud this week:

• Kingdom Come: Deliverance II (New release on Steam, Feb. 4)
• Sid Meier’s Civilization VII (New release on Steam and Epic Games Store, Advanced access on Feb. 5)
• Ambulance Life: A Paramedic Simulator (New Release on Steam, Feb. 6)
• SWORN (New release on Steam, Feb. 6)
• Alan Wake (Xbox, available on the Microsoft Store)
• Ashes of the Singularity: Escalation (Xbox, available on the Microsoft Store)
• Far Cry: New Dawn (New release on PC Game Pass, Feb. 4)

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

Ultimate medieval game weapon – go!

— NVIDIA GeForce NOW (@NVIDIAGFN) February 5, 2025

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When non-technical users can create and deploy reliable AI workflows, organizations can do more to serve their clientele

Platforms for developing no- and low-code solutions are bridging the gap between powerful AI models and everyone who’d like to harness them.

Gooey.AI, a member of the NVIDIA Inception program for cutting-edge startups, offers one such platform, enabling teams to tap into multiple AI tools to improve productivity for frontline workers across the globe. Cofounders Sean Blagsvedt and Archana Prasad join the NVIDIA AI Podcast to discuss how the startup’s platform is making AI development accessible to developers and non-coders alike.

The founders detail Gooey.AI’s evolution from a British Council-funded arts project to a comprehensive, open-source, cloud-hosted platform serving over 1 million users in diverse industries like agriculture, healthcare and frontline services. The company’s vision centers on democratizing AI development through shareable AI recipes, as well as helping ensure responsible implementation and representation of historically underserved communities in AI model-building.

Prasad and Blagsvedt discuss unique applications, such as multilingual chatbots that support African farmers via messaging apps and AI assistants that help heating, ventilation, and air conditioning technicians access technical documentation.

Given the rapid adoption of low-code AI platforms is helping organizations of all sizes and charters overcome technical barriers while improving access to expertise, Blagsvedt noted, “You can’t [create] good technology that changes the world just by focusing on the technology — you have to find the problem worth solving.”

Learn more about the latest advancements in AI by registering for NVIDIA GTC, the conference for the era of AI, taking place March 17-21.

Time Stamps

00:31 – How a development platform began life as a British Council arts project called Dara.network.

17:53 – Working with the Gates Foundation, DigitalGreen and Opportunity International on agricultural chatbots.

33:21 – The influence of HTML standards and Kubernetes on Gooey.AI’s approach.

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NVIDIA’s Louis Stewart on How AI Is Shaping Workforce Development

Louis Stewart, head of strategic initiatives for NVIDIA’s global developer ecosystem, discusses why workforce development is crucial for maximizing AI benefits. He emphasizes the importance of AI education, inclusivity and public-private partnerships in preparing the global workforce for the future. Engaging with AI tools and understanding their impact on the workforce landscape is vital for ensuring these changes benefit everyone.

Living Optics CEO Robin Wang on Democratizing Hyperspectral Imaging

Step into the realm of the unseen with Robin Wang, CEO of Living Optics. Living Optics’ hyperspectral imaging camera, which can capture visual data across 96 colors, reveals details invisible to the human eye. Potential applications are as diverse as monitoring plant health to detecting cracks in bridges. Living Optics aims to empower users across industries to gain new insights from richer, more informative datasets fueled by hyperspectral imaging technology.

Yotta CEO Sunil Gupta on Supercharging India’s Fast-Growing AI Market 

India’s AI market is expected to be massive. Yotta Data Services is setting its sights on supercharging it. Sunil Gupta, cofounder, managing director and CEO of Yotta Data Services, details the company’s Shakti Cloud offering, which provides scalable GPU services for enterprises of all sizes. Yotta is the first Indian cloud services provider in the NVIDIA Partner Network, and its Shakti Cloud is India’s fastest AI supercomputing infrastructure, with 16 exaflops of compute capacity supported by over 16,000 NVIDIA H100 GPUs.

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NVIDIA’s GeForce RTX 5090 and 5080 GPUs — which are based on the groundbreaking NVIDIA Blackwell architecture —offer up to 8x faster frame rates with NVIDIA DLSS 4 technology, lower latency with NVIDIA Reflex 2 and enhanced graphical fidelity with NVIDIA RTX neural shaders.

These GPUs were built to accelerate the latest generative AI workloads, delivering up to 3,352 AI trillion operations per second (TOPS), enabling incredible experiences for AI enthusiasts, gamers, creators and developers.

To help AI developers and enthusiasts harness these capabilities, NVIDIA at the CES trade show last month unveiled NVIDIA NIM and AI Blueprints for RTX. NVIDIA NIM microservices are prepackaged generative AI models that let developers and enthusiasts easily get started with generative AI, iterate quickly and harness the power of RTX for accelerating AI on Windows PCs. NVIDIA AI Blueprints are reference projects that show developers how to use NIM microservices to build the next generation of AI experiences.

NIM and AI Blueprints are optimized for GeForce RTX 50 Series GPUs. These technologies work together seamlessly to help developers and enthusiasts build, iterate and deliver cutting-edge AI experiences on AI PCs.

NVIDIA NIM Accelerates Generative AI on PCs

While AI model development is rapidly advancing, bringing these innovations to PCs remains a challenge for many people. Models posted on platforms like Hugging Face must be curated, adapted and quantized to run on PC. They also need to be integrated into new AI application programming interfaces (APIs) to ensure compatibility with existing tools, and converted to optimized inference backends for peak performance.

NVIDIA NIM microservices for RTX AI PCs and workstations can ease the complexity of this process by providing access to community-driven and NVIDIA-developed AI models. These microservices are easy to download and connect to via industry-standard APIs and span the key modalities essential for AI PCs. They are also compatible with a wide range of AI tools and offer flexible deployment options, whether on PCs, in data centers, or in the cloud.

NIM microservices include everything needed to run optimized models on PCs with RTX GPUs, including prebuilt engines for specific GPUs, the NVIDIA TensorRT software development kit (SDK), the open-source NVIDIA TensorRT-LLM library for accelerated inference using Tensor Cores, and more.

Microsoft and NVIDIA worked together to enable NIM microservices and AI Blueprints for RTX in Windows Subsystem for Linux (WSL2). With WSL2, the same AI containers that run on data center GPUs can now run efficiently on RTX PCs, making it easier for developers to build, test and deploy AI models across platforms.

In addition, NIM and AI Blueprints harness key innovations of the Blackwell architecture that the GeForce RTX 50 series is built on, including fifth-generation Tensor Cores and support for FP4 precision.

Tensor Cores Drive Next-Gen AI Performance

AI calculations are incredibly demanding and require vast amounts of processing power. Whether generating images and videos or understanding language and making real-time decisions, AI models rely on hundreds of trillions of mathematical operations to be completed every second. To keep up, computers need specialized hardware built specifically for AI.

In 2018, NVIDIA GeForce RTX GPUs changed the game by introducing Tensor Cores — dedicated AI processors designed to handle these intensive workloads. Unlike traditional computing cores, Tensor Cores are built to accelerate AI by performing calculations faster and more efficiently. This breakthrough helped bring AI-powered gaming, creative tools and productivity applications into the mainstream.

Blackwell architecture takes AI acceleration to the next level. The fifth-generation Tensor Cores in Blackwell GPUs deliver up to 3,352 AI TOPS to handle even more demanding AI tasks and simultaneously run multiple AI models. This means faster AI-driven experiences, from real-time rendering to intelligent assistants, that pave the way for greater innovation in gaming, content creation and beyond.

FP4 — Smaller Models, Bigger Performance

Another way to optimize AI performance is through quantization, a technique that reduces model sizes, enabling the models to run faster while reducing the memory requirements.

Enter FP4 — an advanced quantization format that allows AI models to run faster and leaner without compromising output quality. Compared with FP16, it reduces model size by up to 60% and more than doubles performance, with minimal degradation.

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

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

FP4 is natively supported by the Blackwell architecture, making it easier than ever to deploy high-performance AI on local PCs. It’s also integrated into NIM microservices, effectively optimizing models that were previously difficult to quantize. By enabling more efficient AI processing, FP4 helps to bring faster, smarter AI experiences for content creation.

AI Blueprints Power Advanced AI Workflows on RTX PCs

NVIDIA AI Blueprints, built on NIM microservices, provide prepackaged, optimized reference implementations that make it easier to develop advanced AI-powered projects — whether for digital humans, podcast generators or application assistants.

At CES, NVIDIA demonstrated PDF to Podcast, a blueprint that allows users to convert a PDF into a fun podcast, and even create a Q&A with the AI podcast host afterwards. This workflow integrates seven different AI models, all working in sync to deliver a dynamic, interactive experience.

With AI Blueprints, users can quickly go from experimenting with to developing AI on RTX PCs and workstations.

NIM and AI Blueprints Coming Soon to RTX PCs and Workstations

Generative AI is pushing the boundaries of what’s possible across gaming, content creation and more. With NIM microservices and AI Blueprints, the latest AI advancements are no longer limited to the cloud — they’re now optimized for RTX PCs. With RTX GPUs, developers and enthusiasts can experiment, build and deploy AI locally, right from their PCs and workstations.

NIM microservices and AI Blueprints are coming soon, with initial hardware support for GeForce RTX 50 Series, GeForce RTX 4090 and 4080, and NVIDIA RTX 6000 and 5000 professional GPUs. Additional GPUs will be supported in the future.

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The financial services industry is reaching an important milestone with AI, as organizations move beyond testing and experimentation to successful AI implementation, driving business results.

NVIDIA’s fifth annual State of AI in Financial Services report shows how financial institutions have consolidated their AI efforts to focus on core applications, signaling a significant increase in AI capability and proficiency.

AI Helps Drive Revenue and Save Costs 

Companies investing in AI are seeing tangible benefits, including increased revenue and cost savings.

Nearly 70% of respondents report that AI has driven a revenue increase of 5% or more, with a dramatic rise in those seeing a 10-20% revenue boost. In addition, more than 60% of respondents say AI has helped reduce annual costs by 5% or more. Nearly a quarter of respondents are planning to use AI to create new business opportunities and revenue streams.

The top generative AI use cases in terms of return on investment (ROI) are trading and portfolio optimization, which account for 25% of responses, followed by customer experience and engagement at 21%. These figures highlight the practical, measurable benefits of AI as it transforms key business areas and drives financial gains.

Overcoming Barriers to AI Success

Half of management respondents said they’ve deployed their first generative AI service or application, with an additional 28% planning to do so within the next six months. A 50% decline in the number of respondents reporting a lack of AI budget suggests increasing dedication to AI development and resource allocation.

The challenges associated with early AI exploration are also diminishing. The survey revealed fewer companies reporting data issues and privacy concerns, as well as reduced concern over insufficient data for model training. These improvements reflect growing expertise and better data management practices within the industry.

As financial services firms allocate budget and grow more savvy at data management, they can better position themselves to harness AI for enhanced operational efficiency, security and innovation across business functions.

Generative AI Powers More Use Cases  

After data analytics, generative AI has emerged as the second-most-used AI workload in the financial services industry. The applications of the technology have expanded significantly, from enhancing customer experience to optimizing trading and portfolio management.

Notably, the use of generative AI for customer experience, particularly via chatbots and virtual assistants, has more than doubled, rising from 25% to 60%. This surge is driven by the increasing availability, cost efficiency and scalability of generative AI technologies for powering more sophisticated and accurate digital assistants that can enhance customer interactions.

More than half of the financial professionals surveyed are now using generative AI to enhance the speed and accuracy of critical tasks like document processing and report generation.

Financial institutions are also poised to benefit from agentic AI — systems that harness vast amounts of data from various sources and use sophisticated reasoning to autonomously solve complex, multistep problems. Banks and asset managers can use agentic AI systems to enhance risk management, automate compliance processes, optimize investment strategies and personalize customer services.

Advanced AI Drives Innovation

Recognizing the transformative potential of AI, companies are taking proactive steps to build AI factories — specially built accelerated computing platforms equipped with full-stack AI software — through cloud providers or on premises. This strategic focus on implementing high-value AI use cases is crucial to enhancing customer service, boosting revenue and reducing costs.

By tapping into advanced infrastructure and software, companies can streamline the development and deployment of AI models and position themselves to harness the power of agentic AI.

With industry leaders predicting at least 2x ROI on AI investments, financial institutions remain highly motivated to implement their highest-value AI use cases to drive efficiency and innovation.

Download the full report to learn more about how financial services companies are using accelerated computing and AI to transform services and business operations.

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AI reasoning models and agents are set to transform industries, but delivering their full potential at scale requires massive compute and optimized software. The “reasoning” process involves multiple models, generating many additional tokens, and demands infrastructure with a combination of high-speed communication, memory and compute to ensure real-time, high-quality results.

To meet this demand, CoreWeave has launched NVIDIA GB200 NVL72-based instances, becoming the first cloud service provider to make the NVIDIA Blackwell platform generally available.

With rack-scale NVIDIA NVLink across 72 NVIDIA Blackwell GPUs and 36 NVIDIA Grace CPUs, scaling to up to 110,000 GPUs with NVIDIA Quantum-2 InfiniBand networking, these instances provide the scale and performance needed to build and deploy the next generation of AI reasoning models and agents.

NVIDIA GB200 NVL72 on CoreWeave 

NVIDIA GB200 NVL72 is a liquid-cooled, rack-scale solution with a 72-GPU NVLink domain, which enables the six dozen GPUs to act as a single massive GPU.

NVIDIA Blackwell features many technological breakthroughs that accelerate inference token generation, boosting performance while reducing service costs. For example, fifth-generation NVLink enables 130TB/s of GPU bandwidth in one 72-GPU NVLink domain, and the second-generation Transformer Engine enables FP4 for faster AI performance while maintaining high accuracy.

CoreWeave’s portfolio of managed cloud services is purpose-built for Blackwell. CoreWeave Kubernetes Service optimizes workload orchestration by exposing NVLink domain IDs, ensuring efficient scheduling within the same rack. Slurm on Kubernetes (SUNK) supports the topology block plug-in, enabling intelligent workload distribution across GB200 NVL72 racks. In addition, CoreWeave’s Observability Platform provides real-time insights into NVLink performance, GPU utilization and temperatures.

CoreWeave’s GB200 NVL72 instances feature NVIDIA Quantum-2 InfiniBand networking that delivers 400Gb/s bandwidth per GPU for clusters up to 110,000 GPUs. NVIDIA BlueField-3 DPUs also provide accelerated multi-tenant cloud networking, high-performance data access and GPU compute elasticity for these instances.

Full-Stack Accelerated Computing Platform for Enterprise AI 

NVIDIA’s full-stack AI platform pairs cutting-edge software with Blackwell-powered infrastructure to help enterprises build fast, accurate and scalable AI agents.

NVIDIA Blueprints provides pre-defined, customizable, ready-to-deploy reference workflows to help developers create real-world applications. NVIDIA NIM is a set of easy-to-use microservices designed for secure, reliable deployment of high-performance AI models for inference. NVIDIA NeMo includes tools for training, customization and continuous improvement of AI models for modern enterprise use cases. Enterprises can use NVIDIA Blueprints, NIM and NeMo to build and fine-tune models for their specialized AI agents.

These software components, all part of the NVIDIA AI Enterprise software platform, are key enablers to delivering agentic AI at scale and can readily be deployed on CoreWeave.

Bringing Next-Generation AI to the Cloud 

The general availability of NVIDIA GB200 NVL72-based instances on CoreWeave underscores the latest in the companies’ collaboration, focused on delivering the latest accelerated computing solutions to the cloud. With the launch of these instances, enterprises now have access to the scale and performance needed to power the next wave of AI reasoning models and agents.

Customers can start provisioning GB200 NVL72-based instances through CoreWeave Kubernetes Service in the US-WEST-01 region using the gb200-4x instance ID. To get started, contact CoreWeave.

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