Posts in category: nVidia AI

The air is crisp, the pumpkins are waiting to be carved, and GFN Thursday is ready to deliver some gaming thrills.

GeForce NOW is unleashing a monster mash of gaming goodness this October with 22 titles joining the cloud, with five available for members to stream this week. From pulse-pounding action to immersive role-playing games, members’ cloud gaming cauldrons are about to bubble over with excitement. Plus, a new account portal update lets members take a look at their playtime details and history on GeForce NOW.

October Treats in Store

GeForce NOW is offering plenty of treats for members this month, starting with the launch of THRONE AND LIBERTY this week.

THRONE AND LIBERTY is a free-to-play massively multiplayer online role-playing game that takes place in the vast open world of Solisium. Scale expansive mountain ranges for new vantage points, scan open skies, traverse sprawling plains and explore a land full of depth and opportunity.

Adapt to survive and thrive through strategic decisions in player vs. player or player vs. environment combat modes while navigating evolving battlefields impacted by weather, time of day and other players. There’s no single path to victory to defeat Kazar and claim the throne while keeping rival guilds at bay.

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

• THRONE AND LIBERTY (New release on Steam, Oct. 1)
• Sifu (Available on PC Game Pass, Oct. 2)
• Bear and Breakfast (Free on Epic Games Store, Oct. 3)
• Monster Jam Showdown (Steam)
• TerraTech Worlds (Steam)

Here’s what members can expect for the rest of October:

• Europa (New release on Steam, Oct. 11)
• Neva (New release on Steam, Oct. 15)
• MechWarrior 5: Clans (New release on Steam and Xbox, Oct. 16)
• A Quiet Place: The Road Ahead (New release on Steam, Oct. 17)
• Worshippers of Cthulhu (New release on Steam, Oct. 21)
• No More Room in Hell 2 (New release on Steam, Oct. 22)
• Romancing SaGa 2: Revenge of the Seven (New release on Steam, Oct. 24)
• Call of Duty: Black Ops 6 (New release on Steam and Battle.net, Oct. 25)
• Life Is Strange: Double Exposure (New release on Steam and Xbox, available in the Microsoft store, Oct. 29)
• Artisan TD (Steam) 
• ASKA (Steam)
• DUCKSIDE (Steam)
• Dwarven Realms (Steam)
• Selaco (Steam)
• Spirit City: Lofi Sessions (Steam)
• Starcom: Unknown Space (Steam)
• Star Trek Timelines (Steam)

Surprises in September

In addition to the 18 games announced last month, 12 more joined the GeForce NOW library:

• Warhammer 40,000: Space Marine 2 (New release on Steam, Sept. 9)
• Dead Rising Deluxe Remaster (New release on Steam, Sept. 18)
• Witchfire (New release on Steam, Sept. 23)
• Monopoly (New release on Ubisoft Connect, Sept. 26)
• Dawn of Defiance (Steam)
• Flintlock: The Siege of Dawn (Xbox, available on PC Game Pass)
• Fort Solis (Epic Games Store)
• King Arthur: Legion IX (Steam)
• The Legend of Heroes: Trails Through Daybreak (Steam)
• Squirrel With a Gun (Steam)
• Tyranny – Gold Edition (Xbox, available on Microsoft Store)
• XIII (Xbox, available on Microsoft Store)

Blacksmith Simulator didn’t make it in September as the game’s launch was moved to next year.

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

What’s your favorite horror game?

— NVIDIA GeForce NOW (@NVIDIAGFN) October 2, 2024

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AI isn’t just about building smarter machines. It’s about building a greener world.

From optimizing energy use to reducing emissions, artificial intelligence and accelerated computing are helping industries tackle some of the world’s toughest environmental challenges.

As Joshua Parker, NVIDIA’s Senior Director of Corporate Sustainability, explains on the latest edition of NVIDIA’s AI Podcast, these technologies are powering a new era of energy efficiency.

Can AI Help Reduce Energy Consumption?

Yes. And it’s doing it in ways that might surprise you.

AI systems themselves use energy—sure—but the big story is how AI and accelerated computing are helping other systems save energy.

Take data centers, for instance.

They’re the backbone of AI, housing the powerful systems that crunch the data needed for AI to work.

Globally, data centers account for about 2% of total energy consumption, and AI-specific centers represent only a tiny fraction of that, Parker explains.

Despite this, AI’s real superpower lies in its ability to optimize.

How? By using accelerated computing platforms that combine GPUs and CPUs.

GPUs (Graphics Processing Units) are designed to handle complex computations quickly and efficiently.

In fact, these systems can be up to 20 times more energy-efficient than traditional CPU-only systems, Parker notes.

That’s not just good for tech companies—it’s good for the environment, too.

What is Accelerated Computing?

At its core, accelerated computing is about doing more with less.

It involves using specialized hardware—like GPUs—to perform tasks faster and with less energy.

This isn’t just theoretical. Over the last eight years, AI systems running on accelerated computing platforms have become 45,000 times more energy-efficient, Parker said.

That’s a staggering leap in performance, driven by improvements in both hardware and software.

So why does this matter? It matters because, as AI becomes more widespread, the demand for computing power grows.

Accelerated computing helps companies scale their AI operations without consuming massive amounts of energy. This energy efficiency is key to AI’s ability to tackle some of today’s biggest sustainability challenges.

AI in Action: Tackling Climate Change

AI isn’t just saving energy—it’s helping to fight climate change.

For instance, AI-enhanced weather forecasting is becoming more accurate, allowing industries and governments to prepare for climate-related events like hurricanes or floods, Parker explains.

The better we can predict these events, the better we can prepare for them, which means fewer resources wasted and less damage done.

Another key area is the rise of digital twins—virtual models of physical environments.

These AI-powered simulations allow companies to optimize energy consumption in real-time, without having to make costly changes in the physical world.

In one case, using a digital twin helped a company achieve a 10% reduction in energy use, Parker said. That may sound small, but scale it across industries and the impact is huge.

AI is also playing a crucial role in developing new materials for renewable energy technologies like solar panels and electric vehicles, accelerating the transition to clean energy.

Can AI Make Data Centers More Sustainable?

Here’s the thing: AI needs data centers to operate, and as AI grows, so does the demand for computing power. But data centers don’t have to be energy hogs.

In fact, they can be part of the sustainability solution.

One major innovation is direct-to-chip liquid cooling. This technology allows data centers to cool their systems much more efficiently than traditional air conditioning methods, which are often energy-intensive.

By cooling directly at the chip level, this method saves energy, helping data centers stay cool without guzzling power, Parker explains.

As AI scales up, the future of data centers will depend on designing for energy efficiency from the ground up. That means integrating renewable energy, using energy storage solutions, and continuing to innovate with cooling technologies.

The goal is to create green data centers that can meet the world’s growing demand for compute power without increasing their carbon footprint, Parker says.

The Role of AI in Building a Sustainable Future

AI is not just a tool for optimizing systems—it’s a driver of sustainable innovation. From improving the efficiency of energy grids to enhancing supply chain logistics, AI is leading the charge in reducing waste and emissions.

Let’s look at energy grids. AI can monitor and adjust energy distribution in real-time, ensuring that resources are allocated where they’re needed most, reducing waste.

This is particularly important as the world moves toward renewable energy, which can be less predictable than traditional sources like coal or natural gas, Parker said.

AI is also helping industries reduce their carbon footprints. By optimizing routes and predicting demand more accurately, AI can cut down on fuel use and emissions in logistics and transportation sectors.

Looking to the future, AI’s role in promoting sustainability is only going to grow.

As technologies become more energy-efficient and AI applications expand, we can expect AI to play a crucial role in helping industries meet their sustainability goals, Parker said.

It’s not just about making AI greener—it’s about using AI to make the world greener.

AI and accelerated computing are reshaping how we think about energy and sustainability.

With their ability to optimize processes, reduce energy waste, and drive innovations in clean technology, these technologies are essential tools for creating a sustainable future.

As Parker explains on NVIDIA’s AI Podcast, AI’s potential to save energy and combat climate change is vast—and we’re only just beginning to tap into it.

As AI continues to revolutionize industries and drive sustainability, there’s no better time to dive deeper into its transformative potential. If you’re eager to explore how AI and accelerated computing are shaping the future of energy efficiency and climate solutions, join us at the NVIDIA AI Summit.

Event Date: October 9, 2024
Register here and gain exclusive insights into the innovations that are powering a sustainable world.

Don’t miss your chance to learn from the leading minds in AI and sustainability. Let’s create a greener future together.

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Editor’s note: This post is part of the AI Decoded series, which demystifies AI by making the technology more accessible, and showcases new hardware, software, tools and accelerations for GeForce RTX PC and NVIDIA RTX workstation users.

From games and content creation apps to software development and productivity tools, AI is increasingly being integrated into applications to enhance user experiences and boost efficiency.

Those efficiency boosts extend to everyday tasks, like web browsing. Brave, a privacy-focused web browser, recently launched a smart AI assistant called Leo AI that, in addition to providing search results, helps users summarize articles and videos, surface insights from documents, answer questions and more.

The technology behind Brave and other AI-powered tools is a combination of hardware, libraries and ecosystem software that’s optimized for the unique needs of AI.

Why Software Matters

NVIDIA GPUs power the world’s AI, whether running in the data center or on a local PC. They contain Tensor Cores, which are specifically designed to accelerate AI applications like Leo AI through massively parallel number crunching — rapidly processing the huge number of calculations needed for AI simultaneously, rather than doing them one at a time.

But great hardware only matters if applications can make efficient use of it. The software running on top of GPUs is just as critical for delivering the fastest, most responsive AI experience.

The first layer is the AI inference library, which acts like a translator that takes requests for common AI tasks and converts them to specific instructions for the hardware to run. Popular inference libraries include NVIDIA TensorRT, Microsoft’s DirectML and the one used by Brave and Leo AI via Ollama, called llama.cpp.

Llama.cpp is an open-source library and framework. Through CUDA — the NVIDIA software application programming interface that enables developers to optimize for GeForce RTX and NVIDIA RTX GPUs — provides Tensor Core acceleration for hundreds of models, including popular large language models (LLMs) like Gemma, Llama 3, Mistral and Phi.

On top of the inference library, applications often use a local inference server to simplify integration. The inference server handles tasks like downloading and configuring specific AI models so that the application doesn’t have to.

Ollama is an open-source project that sits on top of llama.cpp and provides access to the library’s features. It supports an ecosystem of applications that deliver local AI capabilities. Across the entire technology stack, NVIDIA works to optimize tools like Ollama for NVIDIA hardware to deliver faster, more responsive AI experiences on RTX.

NVIDIA’s focus on optimization spans the entire technology stack — from hardware to system software to the inference libraries and tools that enable applications to deliver faster, more responsive AI experiences on RTX.

Local vs. Cloud

Brave’s Leo AI can run in the cloud or locally on a PC through Ollama.

There are many benefits to processing inference using a local model. By not sending prompts to an outside server for processing, the experience is private and always available. For instance, Brave users can get help with their finances or medical questions without sending anything to the cloud. Running locally also eliminates the need to pay for unrestricted cloud access. With Ollama, users can take advantage of a wider variety of open-source models than most hosted services, which often support only one or two varieties of the same AI model.

Users can also interact with models that have different specializations, such as bilingual models, compact-sized models, code generation models and more.

RTX enables a fast, responsive experience when running AI locally. Using the Llama 3 8B model with llama.cpp, users can expect responses up to 149 tokens per second — or approximately 110 words per second. When using Brave with Leo AI and Ollama, this means snappier responses to questions, requests for content summaries and more.

Get Started With Brave With Leo AI and Ollama

Installing Ollama is easy — download the installer from the project’s website and let it run in the background. From a command prompt, users can download and install a wide variety of supported models, then interact with the local model from the command line.

For simple instructions on how to add local LLM support via Ollama, read the company’s blog. Once configured to point to Ollama, Leo AI will use the locally hosted LLM for prompts and queries. Users can also switch between cloud and local models at any time.

Developers can learn more about how to use Ollama and llama.cpp in the NVIDIA Technical Blog.

Generative AI is transforming gaming, videoconferencing and interactive experiences of all kinds. Make sense of what’s new and what’s next by subscribing to the AI Decoded newsletter.

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Washington, D.C., is where possibility has always met policy, and AI presents unparalleled opportunities for tackling global challenges.

NVIDIA’s AI Summit in Washington, set for October 7-9, will gather industry leaders to explore how AI addresses some of society’s most significant challenges.

Held at the Ronald Reagan Building and JW Marriott in the heart of the nation’s capital, the event will focus on the potential of AI to drive breakthroughs in healthcare, cybersecurity, manufacturing and more.

Attendees will hear from industry leaders in 50 sessions, live demos and hands-on workshops covering such topics as generative AI, remote sensing, cybersecurity, robotics and industrial digitalization.

Key Speakers and Sessions

Throughout the conference, speakers will touch on sustainability, economic development and AI for good.

A highlight of the event is the special address by Bob Pette, vice president of enterprise platforms at NVIDIA, on October 8.

Pette will explain how NVIDIA’s accelerated computing platform enables advancements in sensor processing, autonomous systems, and digital twins. These AI applications offer wide-reaching benefits across industries.

Following Pette’s keynote, Greg Estes, vice president of corporate marketing and developer programs at NVIDIA, will discuss how the company’s AI platform is empowering millions of developers worldwide.

Estes will provide insights into NVIDIA’s workforce development programs, which are designed to prepare the next generation of AI talent through hands-on training and certifications.

He’ll spotlight NVIDIA’s extensive training initiatives, including those offered at the AI Summit and throughout the year via the NVIDIA Deep Learning Institute, emphasizing how these programs are equipping individuals with the critical skills needed in the AI-driven economy.

Estes will also share examples of successful collaborations with federal and state governments, as well as educational institutions, that are helping to expand AI education and workforce development efforts.

In addition, Estes will highlight opportunities for organizations to partner with NVIDIA in broadening AI training and reskilling initiatives, ensuring that more professionals can contribute to and benefit from the rapid advancements in AI technology.

Other notable speakers include Lisa Einstein, chief AI scientist at the Cybersecurity and Infrastructure Security Agency (CISA), who will offer an executive perspective in her session, “Navigating the Future of Cyber Operations with AI.”

This session will provide critical insights into how AI is transforming the landscape of cyber operations and securing national infrastructure.

Additionally, Sheri Bachstein, CEO of The Weather Company, will focus on how AI-driven tools are addressing environmental challenges like climate monitoring, while Helena Fu, director at the U.S. Department of Energy, will speak to the role of AI in bolstering national security and advancing sustainable technologies.

Breakthroughs and Demos

With more than 60 sessions planned, the summit will explore critical topics such as generative AI, sustainable computing and AI policy.

Key sessions include Kari Briski, vice president of generative AI software product management at NVIDIA, discussing the impact of NVIDIA’s generative AI platform across industries, and Rev Lebaredian, vice president of Omniverse and simulation technology, covering the future of physical AI, robotics and autonomy.

Renee Wegrzyn, director of ARPA-H, and Rory Kelleher, who leads global business development for healthcare and life sciences at NVIDIA, will delve into AI-enabled healthcare, while Tanya Das, from the Bipartisan Policy Center, will examine how AI can drive scientific discovery, economic growth and national security.

Live demos will showcase groundbreaking innovations such as NVIDIA’s Earth-2, a climate forecasting tool, alongside advancements in quantum computing and robotics. A panel of NVIDIA experts, including Nikki Pope and Leon Derczynski, will address the tools ensuring safe and responsible AI deployment.

Hands-on technical workshops will offer attendees opportunities to earn certifications in data science, generative AI and other essential skills for the future workforce.

These sessions will provide participants with the tools needed to help Americans thrive in an AI-driven economy, enhancing productivity and creating new career opportunities.

Networking and Industry Partnerships

The summit will feature over 95 sponsors, including Microsoft, Dell and Lockheed Martin, showcasing how AI is transforming industries.

Attendees will be able to engage with these partners in the expo hall and explore how AI solutions are being implemented to drive positive change in the public and private sectors.

Whether attending in person or virtually, the NVIDIA AI Summit will provide insights into how AI is contributing to solutions for today’s most significant challenges.

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NIO’s smart EV brand, ONVO, has unveiled the L60 flagship mid-size family SUV, built on the NVIDIA DRIVE Orin system-on-a-chip.

Earlier this year, the automaker introduced the ONVO brand — which stands for On Voyage — to reinforce its commitment to bringing safer, smarter, and more enjoyable and affordable mobility solutions for the mainstream family market.

NVIDIA DRIVE Orin serves as the AI brain of ONVO’s smart-driving system — known as OSD — and delivers up to 254 trillion operations per second of high-performance compute. This allows for diverse and redundant processing of sensor data from the L60’s vision-based sensor suite of high-definition cameras (with maximum forward detection of 687 meters) and 4D radar (with maximum detection range of 370 meters).

The automotive-grade NVIDIA DRIVE Orin runs NVIDIA DriveOS, an operating system for safe, AI-defined vehicles, and is widely used by leading global automakers, including in NIO’s ET7 sedan and its ET5 and ES7 models.

NVIDIA DRIVE Orin enables highly automated driver assistance and autonomous driving systems, along with other features that can be updated via over-the-air software updates.

Bold Design Built to Go the Distance

The ONVO L60 embodies NIO’s commitment to innovative design and user-centric features, blending sleek style with cutting-edge technology.

The L60 was developed to elevate driving experiences by incorporating six critical features: comprehensive safety, spacious comfort, smart cabins with immersive digital experiences, impressive mileage range with convenient recharging, superior ride and handling, and advanced assisted driving capabilities.

The L60 base model (which costs approximately ¥149, 900 yuan, equivalent to $21,000) comes without a battery, allowing users to opt for NIO’s battery-as-a-service option, which is used by 70% of NIO customers. ONVO vehicles are compatible with NIO’s battery swap network, which includes more than 2,500 Power Swap Stations and is expected to continue expanding throughout China.

The launch of the ONVO L60 marks the latest in NIO and NVIDIA’s decade of collaboration. With the integration of NVIDIA DRIVE, the ONVO L60 is poised to deliver an advanced driving experience at an affordable price.

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Whether looking for a time-traveling adventure, strategic roleplay or epic action, anyone can find something to play on GeForce NOW, with over 2,000 games in the cloud.

The GeForce NOW library continues to grow with seven titles arriving this week, including the role-playing game GreedFall II: The Dying World from developer Spiders and publisher Nacon.

Plus, be sure to claim the new in-game reward for Guild Wars 2 for extra style points.

GeForce NOW is improving experiences for members using Windows on Arm laptops. Support for these products is currently in beta, and improvements will be included in the GeForce NOW 2.0.67 app update, rolling out this week to bring GeForce NOW streaming at up to 4K resolution, 120 frames per second and high dynamic range to Arm-based laptops.

Greed Is Good

GreedFall II: The Dying World, the sequel to the acclaimed GreedFall, transports players to a captivating world set three years before the events of the original game. It features a revamped combat system, offering players enhanced control over Companions, and introduces a tactic pause feature during live battles for strategic planning. In this immersive adventure, step into the shoes of a person native to the magical archipelago uprooted from their homeland and thrust into the complex political landscape of the Old Continent. GreedFall II delivers an immersive experience filled with alliances, schemes and intense battles as players navigate the treacherous waters of colonial conflict and supernatural forces.

Members can shape the destiny of the Old Continent all from the cloud. Ultimate and Priority members can elevate their gaming experiences with longer gaming sessions and higher-resolution gameplay over free members. Upgrade today to get immersed in the fight for freedom.

Adventure in Style

The Guild Wars 2: Janthir Wilds expansion is here, bringing new adventures and challenges to explore in the world of Tyria. To celebrate this release, GeForce NOW is offering a special member reward: a unique style bundle to enliven members’ in-game experiences.

Transform characters’ hairstyle, horns and facial hair, customize armor and tailor a wardrobe for epic quests. The reward allows players to stand out as a true champion of Tyria while exploring the new lands of Janthir.

Members enrolled in the GeForce NOW rewards program can check their email for instructions on how to claim the reward. Ultimate and Priority members can redeem their style packages today, and free members can access the reward beginning on Friday, Sept. 27. Don’t miss out — the offer is available through Saturday, Oct. 26, on a first-come, first-served basis.

Something for Everyone

The hit survival action shooter Remnant II from Arc Games this week released its newest and final downloadable content (DLC), The Dark Horizon, along with a free update that brings a brand-new game mode called Boss Rush. In the DLC, players return to N’Erud and uncover a mysterious place preserved in time, where alien farmlands are tended by robots for inhabitants who have long since perished. But time corrupts all, and robotic creations threaten at every turn. Stream the game instantly on GeForce NOW without waiting for downloads or updates.

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

• Witchfire (New release on Steam, Sept. 23)
• Tiny Glade (New release on Steam, Sept. 23)
• Disney Epic Mickey: Rebrushed (New release on Steam, Sept. 24)
• GreedFall II: The Dying World (New release on Steam, Sept. 24)
• Breachway (New release on Steam, Sept. 26)
• Mechabellum (New release on Steam, Sept. 26)
• Monopoly (New release on Ubisoft Connect, Sept. 26)

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

Combat or diplomacy—what’s your go-to approach in RPGs?

— NVIDIA GeForce NOW (@NVIDIAGFN) September 25, 2024

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Editor’s note: This post is part of the AI Decoded series, which demystifies AI by making the technology more accessible, and showcases new hardware, software, tools and accelerations for RTX workstation and PC users.

Across industries, AI is driving innovation and enabling efficiencies — but to unlock its full potential, the technology must be trained on vast amounts of high-quality data.

Data scientists play a key role in preparing this data, especially in domain-specific fields where specialized, often proprietary data is essential to enhancing AI capabilities.

To help data scientists with increasing workload demands, NVIDIA announced that RAPIDS cuDF, a library that allows users to more easily work with data, accelerates the pandas software library with zero code changes. Pandas is a flexible, powerful and popular data analysis and manipulation library for the Python programming language. With cuDF, data scientists can now use their preferred code base without compromising on data processing speed.

NVIDIA RTX AI hardware and technologies can also deliver data processing speedups. They include powerful GPUs that deliver the computational performance necessary to quickly and efficiently accelerate AI at every level — from data science workflows to model training and customization on PCs and workstations.

The Data Science Bottleneck

The most common data format is tabular data, which is organized in rows and columns. Smaller datasets can be managed with spreadsheet tools like Excel, however, datasets and modeling pipelines with tens of millions of rows typically rely on dataframe libraries in programming languages like Python.

Python is a popular choice for data analysis, primarily because of the pandas library, which features an easy-to-use application programming interface (API). However, as dataset sizes grow, pandas struggles with processing speed and efficiency in CPU-only systems. The library also notoriously struggles with text-heavy datasets, which is an important data type for large language models.

When data requirements outgrow pandas’ capabilities, data scientists are faced with a dilemma: endure slow processing timelines or take the complex and costly step of switching to more efficient but less user-friendly tools.

Accelerating Preprocessing Pipelines With RAPIDS cuDF 

With RAPIDS cuDF, data scientists can use their preferred code base without sacrificing processing speed.

RAPIDS is an open-source suite of GPU-accelerated Python libraries designed to improve data science and analytics pipelines. cuDF is a GPU DataFrame library that provides a pandas-like API for loading, filtering and manipulating data.

Using cuDF’s “pandas accelerator mode,” data scientists can run their existing pandas code on GPUs to take advantage of powerful parallel processing, with the assurance that the code will switch to CPUs when necessary. This interoperability delivers advanced, reliable performance.

The latest release of cuDF supports larger datasets and billions of rows of tabular text data. This allows data scientists to use pandas code to preprocess data for generative AI use cases.

Accelerating Data Science on NVIDIA RTX-Powered AI Workstations and PCs

According to a recent study, 57% of data scientists use local resources such as PCs, desktops or workstations for data science.

Data scientists can achieve significant speedups starting with the NVIDIA GeForce RTX 4090 GPU. As datasets grow and processing becomes more memory-intensive, they can use cuDF to deliver up to 100x better performance with NVIDIA RTX 6000 Ada Generation GPUs in workstations, compared with traditional CPU-based solutions.

Data scientists can easily get started with RAPIDS cuDF on NVIDIA AI Workbench. This free developer environment manager powered by containers enables data scientists and developers to create, collaborate and migrate AI and data science workloads across GPU systems. Users can get started with several example projects available on the NVIDIA GitHub repository, such as the cuDF AI Workbench project.

cuDF is also available by default on HP AI Studio, a centralized data science platform designed to help AI developers seamlessly replicate their development environment from workstations to the cloud. This allows them to set up, develop and collaborate on projects without managing multiple environments.

The benefits of cuDF on RTX-powered AI PCs and workstations extend beyond raw performance speedups. It also:

• Saves time and money with fixed-cost local development on powerful GPUs that replicates seamlessly to on-premises servers or cloud instances.
• Enables faster data processing for quicker iterations, allowing data scientists to experiment, refine and derive insights from datasets at interactive speeds.
• Delivers more impactful data processing for better model outcomes further down the pipeline.

Learn more about RAPIDS cuDF.

A New Era of Data Science

As AI and data science continue to evolve, the ability to rapidly process and analyze massive datasets will become a key differentiator to enable breakthroughs across industries. Whether for developing sophisticated machine learning models, conducting complex statistical analyses or exploring generative AI, RAPIDS cuDF provides the foundation for next-generation data processing.

NVIDIA is expanding that foundation by adding support for the most popular dataframe tools, including Polars, one of the fastest-growing Python libraries, which significantly accelerates data processing compared with other CPU-only tools out of the box.

Polars announced this month the open beta of the Polars GPU Engine, powered by RAPIDS cuDF. Polars users can now boost the performance of the already lightning-fast dataframe library by up to 13x.

Endless Possibilities for Tomorrow’s Engineers With RTX AI

NVIDIA GPUs — whether running in university data centers, GeForce RTX laptops or NVIDIA RTX workstations — are accelerating studies. Students in data science fields and beyond are enhancing their learning experience and gaining hands-on experience with hardware used widely in real-world applications.

Learn more about how NVIDIA RTX PCs and workstations help students level up their studies with AI-powered tools.

Generative AI is transforming gaming, videoconferencing and interactive experiences of all kinds. Make sense of what’s new and what’s next by subscribing to the AI Decoded newsletter.

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Hitachi Rail, a global transportation company powering railway systems in over 50 countries, is integrating NVIDIA AI technology to lower maintenance costs for rail operators, reduce train idling time and improve transit reliability for passengers.

The company is adopting NVIDIA IGX — an industrial-grade, enterprise-level platform that delivers high-bandwidth sensor processing, powerful AI compute, functional safety capabilities and enterprise security — into its new HMAX platform to process sensor and camera data in real time.

By removing the lag time between data collection and analysis, the HMAX platform will enable Hitachi Rail clients to more quickly detect train tracks that need repair, monitor the degradation of overhead power lines and assess the health of trains and signaling equipment.

Hitachi Rail estimates that proactive maintenance costs around 7x less than emergency repairs done after infrastructure fails unexpectedly. Its existing AI monitoring systems are already reducing service delays by up to 20% and train maintenance costs by up to 15% — and are cutting down energy consumption by decreasing fuel costs at train depots by up to 40%.

With real-time analysis using NVIDIA IGX and NVIDIA Holoscan platform for sensor processing, the company aims to further increase these savings.

“Using previous digital monitoring systems, it would take a few days to process the data and discover issues that need attention,” said Koji Agatsuma, executive director and chief technology officer of rail vehicles at Hitachi Rail. “If we can instead conduct real-time prediction using NVIDIA technology, that enables us to avoid service disruptions and significantly improve safety, reliability and operating costs.”

NVIDIA IGX Powers Real-Time AI Engine

Building on its existing collection of HMAX applications — which are currently running on data from 8,000 train cars on 2,000 trains — Hitachi Rail has used NVIDIA IGX and the NVIDIA AI Enterprise software platform to create new accelerated AI applications to help operators monitor train fleets and infrastructure. NVIDIA AI Enterprise offers tools, pretrained models and application frameworks to streamline the development and deployment of production-grade AI applications.

These applications, available soon through the HMAX platform, can be used by the company’s international customer base to process huge quantities of data streaming from sensors onboard trains, taken from existing systems or imported from third-party software already in use by the customer.

In the U.K., for example, each Hitachi Rail train has sensors that report nearly 50,000 data points as frequently as every fifth of a second. AI infrastructure that keeps pace with this data flow can send train operators timely alerts when a component of a train or rail line needs maintenance. The AI insights can also be accessed through a chatbot interface, helping operators easily identify trends and opportunities to optimize maintenance schedules and more.

“If a potential issue isn’t identified and fixed promptly, it can result in a service disruption that causes significant economic loss for our customers and impacts the passengers who rely on these transit lines,” Agatsuma said. “NVIDIA AI infrastructure has enabled us to get immediate alerts on thousands of miles of railway for the first time, which we anticipate will reduce delays and disruptions to passenger travel.”

Driving Benefits Down the Track

The opportunities go beyond monitoring trains and tracks.

By mounting cameras atop trains, Hitachi Rail can monitor power lines overhead to identify degrading electric cables and help prevent disruptive failures. Traditionally, it takes up to 10 days to process one day’s worth of video data collected by the train. With NVIDIA-accelerated sensor processing, data can be processed in real time at the edge, sending only relevant information back to operational control centers for analysis and action.

Learn more about the NVIDIA IGX platform. 

Main image courtesy of Hitachi Rail.

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NVIDIA is joining the U.S. government’s launch of the Partnership for Global Inclusivity on AI (PGIAI), providing Deep Learning Institute training, GPU credits and hardware and software grants in developing countries.

The partnership was announced today in New York at the U.N. General Assembly by U.S. Secretary of State Antony Blinken. The effort aims to harness the potential of artificial intelligence to advance sustainable development around the world.

“Artificial Intelligence is driving the next industrial revolution, offering incredible potential to contribute meaningful progress on sustainable development goals,” said Ned Finkle, VP of NVIDIA government affairs. “NVIDIA is committed to empowering communities to use AI to innovate through support for research, education and small and medium size enterprises.”

NVIDIA is joined by Amazon, Anthropic, Apple,  Google, IBM, Meta, Microsoft and OpenAI in the initiative.

Members of the partnership have pledged to provide access to training, compute and other AI tools to drive sustainable development and improved quality of life in developing countries.

The PGIAI initiative recognizes that equitable AI requires understanding and respect for the diverse cultures, languages and traditions of the communities where services are provided. With that criteria, PGIAI members will focus on increasing access to AI models, APIs, compute credit and other AI tools, as well as technical training and access to local datasets.

Under this partnership, NVIDIA will provide approximately $10 million in free training to universities and developers to help support AI for local solutions and development goals.

NVIDIA’s global Inception program supports nearly 5,000 start-ups in emerging economies with technical expertise, go-to-market support, hardware and software discounts, and access to free cloud computing credits provided by NVIDIA partners.

In 2024, Inception provided access to more than $60 million worth of free cloud compute credits through partners to start-ups in emerging economies.

CTA: Learn more about the NVIDIA Inception program for startups. Learn more about the NVIDIA Deep Learning Institute.

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The Wellcome Sanger Institute, a key contributor to the international Human Genome Project, is turning to NVIDIA accelerated computing to save energy while saving lives.

With one of the world’s largest sequencing facilities, the U.K.-based institute has read more than 48 petabases — or 48 quadrillion base pairs — of DNA and RNA sequences to uncover crucial insights into health and disease.

Its Cancer, Ageing and Somatic Mutation (CASM) program sequences and analyzes tens of thousands of cancer genomes a year to study the mutational processes driving cancer formation, as well as genetic variations that determine treatment effectiveness.

To tackle such large-scale initiatives, the Sanger Institute is exploring the use of an NVIDIA DGX system with NVIDIA Parabricks, a scalable genomics analysis software suite that taps into accelerated computing to process data in just minutes.

“The Sanger Institute handles hundreds of thousands of somatic samples annually,” said Jingwei Wang, principal software developer for CASM at the Wellcome Sanger Institute. “NVIDIA accelerated computing and Parabricks will save us considerable time, cost and energy when analyzing samples, and we’re excited to explore NVIDIA’s advanced architectures, such as NVIDIA Grace and Grace Hopper, for even higher performance and efficiency.”

Reducing Runtime and Energy Consumption

The Sanger Institute develops high-throughput models of cancer samples for genome-wide functional screens and drug testing.

NVIDIA accelerated computing and software drastically reduce the institute’s analysis runtime and energy consumption per genome.

To accelerate genomic analysis with Burrows-Wheeler Aligner (BWA), a software package for mapping DNA sequences against a large reference genome, Sanger uses its proprietary CaVEMan workflow running on CPUs and is tapping into Parabricks on NVIDIA GPUs.

The institute reduced runtime 1.6x, costs 24x and energy consumption up to 42x — using one NVIDIA DGX system compared with 128 dual-socket CPU servers.

About 125 million CPU hours are consumed per 10,000 genomes sequenced by the institute annually.

This means that the Sanger Institute could, each year, save $1 million and 1,000 megawatt-hours by switching to using BWA with Parabricks on GPUs. That’s about the amount of energy needed to power an average American home for a century.

Collaborating With Industry Leaders

The Sanger Institute’s NVIDIA-accelerated sequencing lab can be considered an AI factory, where data comes in and intelligence comes out.

AI factories are next-generation data centers that host advanced, full-stack accelerated computing platforms for the most computationally intensive tasks.

As it explores crucial scientific questions to discover new cancer genes and mutational processes, the Sanger Institute is boosting operational and energy efficiency by using NVIDIA infrastructure for its AI factory.

In addition, companies and organizations building AI factories are participating in cross-industry collaborations with leaders like Schneider Electric, an energy management and automation company, to optimize data center designs for running demanding workloads in the most energy-efficient way.

The Sanger Institute is collaborating with Schneider Electric to minimize data center downtime and equip the DNA sequencing lab’s data center with uninterruptible power supplies and cooling equipment, among other technologies pivotal to reducing energy consumption.

At the NVIDIA GTC conference in March, Schneider Electric announced it’s helping organizations across industries optimize infrastructure by releasing AI data center reference designs tailored for NVIDIA accelerated computing clusters.

The reference designs — built for data processing, engineering simulation, electronic design automation, computer-aided drug design and generative AI — will focus on high-power distribution, liquid-cooling systems and other aspects of scalable, high-performance, sustainable data centers.

In an NYC Climate Week panel this week hosted by The Economist, representatives from Sanger, Schneider Electric and NVIDIA will talk about their work.

Learn more about sustainable computing and the Sanger Institute’s potentially life-saving work.

Featured image courtesy of the Wellcome Sanger Institute.

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