Posts in category: nVidia AI

Whether looking for new adventures, epic storylines or games to play with a friend, GeForce NOW members are covered.

Start off with the much-anticipated sequel to the Men of War franchise or cozy up with some adorable pals in Palworld, both part of five games GeForce NOW is bringing to the cloud this week.

No Guts, No Glory

Get transported to the battlefields of World War II with historical accuracy and attention to detail in Men of War II, the newest entry in the real-time strategy series from Fulqrum Publishing.

The game features an extensive roster of units, including tanks, airplanes and infantry. With advanced enemy AI and diverse gameplay modes, Men of War II promises an immersive experience for both history enthusiasts and casual gamers.

Gear up, strategize and prepare to rewrite history. Get an extra fighting chance with a GeForce NOW Ultimate membership, which streams at up to 4K resolution and provides longer gaming sessions and faster access to games over a free membership.

Cloud Pals

Step into a world teeming with enigmatic creatures known as “Pals” in the action-adventure survival game Palworld from Pocketpair. Navigate the wilderness, gather resources and construct a base to capture, tame and train Pals, each with distinct abilities. Explore the world, uncover secrets and forge alliances or rivalries with other survivors in online co-op play mode.

Embark on adventure with these trusty Pals through a GeForce NOW membership. With a Priority membership, enjoy up to six hours of uninterrupted gaming sessions, while Ultimate members can extend their playtime to eight hours.

Master New Games

Vanquish foes with a single strike in 1v1 weapon-based fighter Die by the Blade from Grindstone. Dive into a samurai punk world and wield a range of traditional Japanese weapons. Take up arms and crush friends in local or online multiplayer, or take on unknown warriors in online ranked matches. Outwit opponents in intense, tactical battles and master the art of the one-hit kill.

Check out the list of new games this week:

• Men of War II (New release on Steam, May 15)
• Die by the Blade (New release on Steam, May 16)
• Colony Survival (Steam)
• Palworld (Steam)
• Tomb Raider: Definitive Edition (Xbox, available on PC Game Pass)

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

If you could be any video game character for a day, who would you choose and what would you do?

— NVIDIA GeForce NOW (@NVIDIAGFN) May 15, 2024

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Senior executives from NVIDIA, Siemens and Teradyne Robotics gathered this week in Odense, Denmark, to mark the launch of Teradyne’s new headquarters and discuss the massive advances coming to the robotics industry.

One of Denmark’s oldest cities and known as the city of robotics, Odense is home to over 160 robotics companies with 3,700 employees and contributes profoundly to the industry’s progress.

Teradyne Robotics’ new hub there, which includes cobot company Universal Robots (UR) and autonomous mobile robot (AMR) company MiR, is set to help employees maximize collaborative efforts, foster innovation and provide an environment to revolutionize advanced robotics and autonomous machines.

The grand opening showcased the latest AI robotic applications and featured a panel discussion on the future of advanced robotics. Speakers included Ujjwal Kumar, group president at Teradyne Robotics; Rainer Brehm, CEO of Siemens Factory Automation; and Deepu Talla, vice president of robotics and edge computing at NVIDIA.

“The advent of generative AI coupled with simulation and digital twins technology is at a tipping point right now, and that combination is going to change the trajectory of robotics,” commented Talla.

The Power of Partnerships

The discussion comes as the global robotics market continues to grow rapidly. The cobots market in Europe was valued at $286 million in 2022 and is projected to reach $6.7 billion by 2032, at a yearly growth rate of more than 37%.

Panelists discussed why teaming up is key to innovation for any company — whether a startup or an enterprise — and how physical AI is being used across businesses and workplaces, stressing the game-changing impact of advanced robotics.

The alliance between NVIDIA and Teradyne Robotics, which includes an AI-based intra-logistics solution alongside Siemens, showcases the strength of collaboration across the ecosystem. NVIDIA’s prominent role as a physical AI hardware provider is boosting the cobot and AMR sectors with accelerated computing, while its collaboration with Siemens is transforming industrial automation.

“NVIDIA provides all the core AI capabilities that get integrated into the hundreds and thousands of companies building robotic platforms and robots, so our approach is 100% collaboration,” Talla said.

“What excites me most about AI and robots is that collaboration is at the core of solving our customers’ problems,” Kumar added. “No one company has all the technologies needed to address these problems, so we must work together to understand and solve them at a very fast pace.”

Accelerating Innovation With AI 

AI has already made huge strides across industries and plays an important role in enhancing advanced robotics. Leveraging machine learning, computer vision and natural language processing, AI gives robots the cognitive capability to understand, learn and make decisions.

“For humans, we have our senses, but it’s not that easy for a robot, so you have to build these AI capabilities for autonomous navigation,” Talla said. “NVIDIA’s Isaac platform is enabling increased autonomy in robotics with rapid advancements in simulation, generative AI, foundation models and optimized edge computing.”

NVIDIA is working closely with the UR team to infuse AI into UR’s robotics software technology. In the case of autonomous mobile robots that move things from point A to B to C, it’s all about operating in unstructured environments and navigating autonomously.

Brehm emphasized the need to scale AI by industrializing it, allowing for automated deployment, inference and monitoring of models. He spoke about empowering customers to utilize AI effortlessly, even without AI expertise. “We want to enhance automation for more skill-based automation systems in the future,” he said.

As a leading robotics company with one of the largest installed bases of collaborative and AMRs, Teradyne has identified a long list of industry problems and is working closely with NVIDIA to solve them.

“I use the term ‘physical AI’ as opposed to ‘digital AI’ because we are taking AI to a whole new level by applying it in the physical world,” said Kumar. “We see it helping our customers in three ways: adding new capabilities to our robots, making our robots smarter with advanced path planning and navigation, and further enhancing the safety and reliability of our collaborative robots.”

The Impact of Real-World Robotics

Autonomous machines, or AI robots, are already making a noticeable difference in the real world, from industries to our daily lives. Industries such as manufacturing are using advanced robotics to enhance efficiency, accuracy and productivity.

Companies want to produce goods close to where they are consumed, with sustainability being a key driver. But this often means setting up shop in high-cost countries. The challenge is twofold: producing at competitive prices and dealing with shrinking, aging workforces that are less available for factory jobs.

“The problem for large manufacturers is the same as what small and medium manufacturers have always faced: variability,” Kumar said. “High-volume industrial robots don’t suit applications requiring continuous design tweaks. Collaborative robots combined with AI offer solutions to the pain points that small and medium customers have lived with for years, and to the new challenges now faced by large manufacturers.”

Automation isn’t just about making things faster; it’s also about making the most of the workforce. In manufacturing, automation aids smoother processes, ramps up safety, saves time and relieves pressure on employees.

“Automation is crucial and, to get there, AI is a game-changer for solving problems,” Brehm said.

AI and computing technologies are set to redefine the robotics landscape, transforming robots from mere tools to intelligent partners capable of autonomy and adaptability across industries.

Feature image by Steffen Stamp. Left to right: Fleur Nielsen, head of communications at Universal Robots; Deepu Talla, head of robotics at NVIDIA; Rainer Brehm, CEO of Siemens Factory Automation; and Ujjwal Kumar, president of Teradyne Robotics.

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A collaboration between NVIDIA and academic researchers is prepping robots for surgery.

ORBIT-Surgical — developed by researchers from the University of Toronto, UC Berkeley, ETH Zurich, Georgia Tech and NVIDIA — is a simulation framework to train robots that could augment the skills of surgical teams while reducing surgeons’ cognitive load.

It supports more than a dozen maneuvers inspired by the training curriculum for laparoscopic procedures, aka minimally invasive surgery, such as grasping small objects like needles, passing them from one arm to another and placing them with high precision.

The physics-based framework was built using NVIDIA Isaac Sim, a robotics simulation platform for designing, training and testing AI-based robots. The researchers trained reinforcement learning and imitation learning algorithms on NVIDIA GPUs and used NVIDIA Omniverse, a platform for developing and deploying advanced 3D applications and pipelines based on Universal Scene Description (OpenUSD), to enable photorealistic rendering.

Using the community-supported da Vinci Research Kit, provided by the Intuitive Foundation, a nonprofit supported by robotic surgery leader Intuitive Surgical, the ORBIT-Surgical research team demonstrated how training a digital twin in simulation transfers to a physical robot in a lab environment in the video below.

ORBIT-Surgical will be presented Thursday at ICRA, the IEEE International Conference on Robotics and Automation, taking place this week in Yokohama, Japan. The open-source code package is now available on GitHub.

A Stitch in AI Saves Nine

ORBIT-Surgical is based on Isaac Orbit, a modular framework for robot learning built on Isaac Sim. Orbit includes support for various libraries for reinforcement learning and imitation learning, where AI agents are trained to mimic ground-truth expert examples.

The surgical framework enables developers to train robots like the da Vinci Research Kit robot, or dVRK, to manipulate both rigid and soft objects using reinforcement learning and imitation learning frameworks running on NVIDIA RTX GPUs.

ORBIT-Surgical introduces more than a dozen benchmark tasks for surgical training, including one-handed tasks such as picking up a piece of gauze, inserting a shunt into a blood vessel or lifting a suture needle to a specific position. It also includes two-handed tasks, like handing a needle from one arm to another, passing a threaded needle through a ring pole and reaching two arms to specific positions while avoiding obstacles.

One of ORBIT-Surgical’s benchmark tests is inserting a shunt — shown on left with a real-world robot and on right in simulation.

By developing a surgical simulator that takes advantage of GPU acceleration and parallelization, the team is able to boost robot learning speed by an order of magnitude compared to existing surgical frameworks. They found that the robot digital twin could be trained to complete tasks like inserting a shunt and lifting a suture needle in under two hours on a single NVIDIA RTX GPU.

With the visual realism enabled by rendering in Omniverse, ORBIT-Surgical also allows researchers to generate high-fidelity synthetic data, which could help train AI models for perception tasks such as segmenting surgical tools in real-world videos captured in the operating room.

A proof of concept by the team showed that combining simulation and real data significantly improved the accuracy of an AI model to segment surgical needles from images — helping reduce the need for large, expensive real-world datasets for training such models.

Read the paper behind ORBIT-Surgical, and learn more about NVIDIA-authored papers at ICRA.

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Basecamp Research is on a mission to capture the vastness of life on Earth at an unprecedented scale. Phil Lorenz, CTO at Basecamp Research, discusses using AI and biodiversity data to advance fields like medicine and environmental conservation with host Noah Kravitz in this AI Podcast episode recorded live at the NVIDIA GTC global AI conference. Lorenz explains Basecamp’s systematic collection of biodiversity data in partnership with nature parks worldwide and its use of deep learning to analyze and apply it for use cases such as protein structure prediction and gene editing. He also emphasizes the importance of ethical data governance and touches on technological advancements that will help drive the future of AI in biology. 

Basecamp Research is a member of the NVIDIA Inception program for cutting-edge startups. 

Stay tuned for more episodes recorded live at GTC, and hear more from Lorenz in this GTC session.

Time Stamps

1:31: What is Basecamp Research?
3:08: How does the process of sequencing biodiversity work?
5:15: What is the collected biodiversity data used for?
7:56: Gene editing and how biodiversity data can enhance it
9:00: How the development of AI has affected Basecamp’s work
14:33: Basecamp’s breakthroughs
16:49: AI and machine learning-related challenges Basecamp has encountered
26:02: Ethical considerations in data collecting

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Can machine learning help predict extreme weather events and climate change? Christopher Bretherton, senior director of climate modeling at the Allen Institute for Artificial Intelligence, or AI2, explores the technology’s potential to enhance climate modeling.

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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 which showcases new hardware, software, tools and accelerations for RTX PC users.

Mozilla Firefox, the popular open-source browser, is the latest partner to incorporate NVIDIA RTX Video, a technology that uses AI to improve video quality on Windows PCs and workstations. The browser’s latest release taps local NVIDIA RTX GPUs to make streaming and video better than ever.

Pixel Perfect

First announced at CES in January 2023, RTX Video is a collection of AI video enhancements that improve the quality of videos played on browsers through platforms like YouTube, Prime Video and Disney+. The technology makes videos streamed on NVIDIA GeForce RTX-powered PCs and RTX-powered workstations appear sharper and more detailed without requiring a higher-resolution source.

RTX Video is made up of two parts. RTX Video Super Resolution upscales low-resolution video for cleaner, crisper imagery. It works by analyzing the lower-resolution video and using deep learning to predict what the higher-resolution version should look like. The algorithm then combines this predicted image with a traditionally upscaled version to reduce or eliminate compression artifacts and sharpen the final output.

RTX Video HDR goes one step further: when enabled, it analyzes standard dynamic range (SDR) video content through AI neural networks to add high-dynamic range (HDR) information, improving visibility, details and vibrancy.

Since 90% of video online is 1080p or lower and SDR, enabling RTX Video is like pushing the “remaster” button on most of the content users watch everyday.

Pretty Foxy

Mozilla Firefox now supports RTX Video Super Resolution and HDR in its latest stable version (v126). It’s never been easier for users to access AI-enhanced upscaling, de-artifacting and HDR effects for online videos.

“Video is a core pillar of the modern web, and we are committed to delivering a great experience for our users,” said Bobby Holley, chief technology officer of Firefox at Mozilla. “Mozilla is integrating RTX Video into Firefox to improve video quality for our users with compatible RTX GPUs.”

Firefox joins other Chromium-based browsers, including Google Chrome and Microsoft Edge, in supporting RTX Video. RTX Video Super Resolution is also supported in popular video players like VLC.

Enabling RTX Video is easy:

1. Update to the latest GeForce RTX Game Ready Driver, NVIDIA Studio or NVIDIA RTX Enterprise Driver.
2. Ensure Windows HDR features are enabled by navigating to System > Display > HDR.
3. Open the NVIDIA Control Panel and navigate to Adjust Video Image Settings > RTX Video Enhancement.
4. Turn on “Super Resolution” and “High Dynamic Range.”

Note that RTX Video HDR requires an NVIDIA GeForce RTX or RTX professional GPU connected to an HDR10-compatible monitor or TV.

For more information, check out the RTX Video FAQ.

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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Large language models that power generative AI are seeing intense innovation — models that handle multiple types of data such as text, image and sounds are becoming increasingly common. 

However, building and deploying these models remains challenging. Developers need a way to quickly experience and evaluate models to determine the best fit for their use case, and then optimize the model for performance in a way that not only is cost-effective but offers the best performance.

To make it easier for developers to create AI-powered applications with world-class performance, NVIDIA and Google today announced three new collaborations at Google I/O ‘24. 

Gemma + NIM

Using TensorRT-LLM, NVIDIA is working with Google to optimize two new models it introduced at the event: Gemma 2 and PaliGemma. These models are built from the same research and technology used to create the Gemini models, and each is focused on a specific area: 

• Gemma 2 is the next generation of Gemma models for a broad range of use cases and features a brand new architecture designed for breakthrough performance and efficiency.
• PaliGemma is an open vision language model (VLM) inspired by PaLI-3. Built on open components including the SigLIP vision model and the Gemma language model, PaliGemma is designed for vision-language tasks such as image and short video captioning, visual question answering, understanding text in images, object detection and object segmentation. PaliGemma is designed for class-leading fine-tuning performance on a wide range of vision-language tasks and is also supported by NVIDIA JAX-Toolbox.

Gemma 2 and PaliGemma will be offered with NVIDIA NIM inference microservices, part of the NVIDIA AI Enterprise software platform, which simplifies the deployment of AI models at scale. NIM support for the two new models are available from the API catalog, starting with PaliGemma today; they soon will be released as containers on NVIDIA NGC and GitHub. 

Bringing Accelerated Data Analytics to Colab

Google also announced that RAPIDS cuDF, an open-source GPU dataframe library, is now supported by default on Google Colab, one of the most popular developer platforms for data scientists. It now takes just a few seconds for Google Colab’s 10 million monthly users to accelerate pandas-based Python workflows by up to 50x using NVIDIA L4 Tensor Core GPUs, with zero code changes.

With RAPIDS cuDF, developers using Google Colab can speed up exploratory analysis and production data pipelines. While pandas is one of the world’s most popular data processing tools due to its intuitive API, applications often struggle as their data sizes grow. With even 5-10GB of data, many simple operations can take minutes to finish on a CPU, slowing down exploratory analysis and production data pipelines.

RAPIDS cuDF is designed to solve this problem by seamlessly accelerating pandas code on GPUs where applicable, and falling back to CPU-pandas where not. With RAPIDS cuDF available by default on Colab, all developers everywhere can leverage accelerated data analytics.

Taking AI on the Road 

By employing AI PCs using NVIDIA RTX graphics, Google and NVIDIA also announced a Firebase Genkit collaboration that enables app developers to easily integrate generative AI models, like the new family of Gemma models, into their web and mobile applications to deliver custom content, provide semantic search and answer questions. Developers can start work streams using local RTX GPUs before moving their work seamlessly to Google Cloud infrastructure.

To make this even easier, developers can build apps with Genkit using JavaScript, a programming language mobile developers commonly use to build their apps.

The Innovation Beat Goes On

NVIDIA and Google Cloud are collaborating in multiple domains to propel AI forward. From the upcoming Grace Blackwell-powered DGX Cloud platform and JAX framework support, to bringing the NVIDIA NeMo framework to Google Kubernetes Engine, the companies’ full-stack partnership expands the possibilities of what customers can do with AI using NVIDIA technologies on Google Cloud.

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The integration of AI has become pivotal in shaping the future of driving experiences. As vehicles transition into smart, connected entities, the demand for intuitive human-machine interfaces and advanced driver assistance systems has surged

In this journey toward automotive intelligence, Cerence, a global leader in AI-powered mobility solutions, is tapping NVIDIA’s core expertise in automotive cloud and edge technologies to redefine the in-car user experience.

In a recent video, Iqbal Arshad, chief technology officer of Cerence, emphasized the point, stating: “Generative AI is the single biggest change that’s happening in the tech industry overall.”

The cornerstone of Cerence’s vision lies in the development of its automotive-specific Cerence Automotive Large Language Model, or CaLLM. It serves as the foundation for the company’s next-gen in-car computing platform, running on NVIDIA DRIVE.

The platform, unveiled in December, showcases the future of in-car interaction, with an automotive- and mobility-specific assistant that provides an integrated in-cabin experience.

“We have datasets from the last 20 years of experience working in the automotive space,” Iqbal said. “And we’re able to take that data and make that an automotive-ready LLM.”

Generative AI a Game-Changer for the Automotive Industry

Generative AI enables vehicles to comprehend and respond to human language with remarkable accuracy, revolutionizing the way drivers interact with their cars.

Whether it’s initiating voice commands for navigation, controlling infotainment systems or even engaging in natural language conversations, generative AI opens a realm of possibilities for creating more convenient and enjoyable driving experiences.

Cerence is striving to empower vehicles with the cognitive capabilities necessary to seamlessly assist drivers in navigating their daily routines.

The company leverages NVIDIA DGX Cloud on Microsoft Azure, providing dedicated, scalable access to the latest NVIDIA architecture, co-engineered at every layer with Microsoft Azure, optimized for peak performance in AI workload training. NVIDIA’s inferencing technology helps Cerence deliver real-time performance, facilitating seamless user experiences.

As Cerence sees it, the future is one of intelligent driving, where vehicles aren’t just modes of transportation, but trusted companions on the road ahead.

“Generative computing is going to change your in-car experience,” said Iqbal.

With generative AI at its core, driving will evolve into a personalized, connected and, ultimately, safer experience for all.

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Following an announcement by Japan’s Ministry of Economy, Trade and Industry, NVIDIA will play a central role in developing the nation’s generative AI infrastructure as Japan seeks to capitalize on the technology’s economic potential and further develop its workforce.

NVIDIA is collaborating with key digital infrastructure providers, including GMO Internet Group, Highreso, KDDI Corporation, RUTILEA, SAKURA internet Inc. and SoftBank Corp., which the ministry has certified to spearhead the development of cloud infrastructure crucial for AI applications.

Over the last two months, the ministry announced plans to allocate $740 million, approximately ¥114.6 billion, to assist six local firms in this initiative. Following on from last year, this is a significant effort by the Japanese government to subsidize AI computing resources, by expanding the number of companies involved.

With this move, Japan becomes the latest nation to embrace the concept of sovereign AI, aiming to fortify its local startups, enterprises and research efforts with advanced AI technologies.

Across the globe, nations are building up domestic computing capacity through various models. Some procure and operate sovereign AI clouds with state-owned telecommunications providers or utilities. Others are sponsoring local cloud partners to provide a shared AI computing platform for public and private sector use.

Japan’s initiative follows NVIDIA founder and CEO Jensen Huang’s visit last year, where he met with political and business leaders — including Japanese Prime Minister Fumio Kishida — to discuss the future of AI.

During his trip, Huang emphasized that “AI factories” — next-generation data centers designed to handle the most computationally intensive AI tasks — are crucial for turning vast amounts of data into intelligence. “The AI factory will become the bedrock of modern economies across the world,” Huang said during a meeting with the Japanese press in December.

The Japanese government plans to subsidize a significant portion of the costs for building AI supercomputers, which will facilitate AI adoption, enhance workforce skills, support Japanese language model development and bolster resilience against natural disasters and climate change.

Under the country’s Economic Security Promotion Act, the ministry aims to secure a stable supply of local cloud services, reducing the time and cost of developing next-generation AI technologies.

Japan’s technology powerhouses are already moving fast to embrace AI. Last week, SoftBank Corp. announced that it will invest ¥150 billion, approximately $960 million, for its plan to expand the infrastructure needed to develop Japan’s top-class AI, including purchases of NVIDIA accelerated computing.

The news follows Huang’s meetings with leaders in Canada, France, India, Japan, Malaysia, Singapore and Vietnam over the past year, as countries seek to supercharge their regional economies and embrace challenges such as climate change with AI.

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Described as the largest system in the pharmaceutical industry, BioHive-2 at the Salt Lake City headquarters of Recursion debuts today at No. 35, up more than 100 spots from its predecessor on the latest TOP500 list of the world’s fastest supercomputers.

The advance represents the company’s most recent effort to accelerate drug discovery with NVIDIA technologies.

“Just as with large language models, we see AI models in the biology domain improve performance substantially as we scale our training with more data and compute horsepower, which ultimately leads to greater impacts on patients’ lives,” said Recursion’s CTO, Ben Mabey, who’s been applying machine learning to healthcare for more than a decade.

BioHive-2 packs 504 NVIDIA H100 Tensor Core GPUs linked on an NVIDIA Quantum-2 InfiniBand network to deliver 2 exaflops of AI performance. The resulting NVIDIA DGX SuperPOD is nearly 5x faster than Recursion’s first-generation system, BioHive-1.

Performance Powers Through Complexity

That performance is key to rapid progress because “biology is insanely complex,” Mabey said.

Finding a new drug candidate can take scientists years performing millions of wet-lab experiments.

That work is vital; Recursion’s scientists run more than 2 million such experiments a week. But going forward, they’ll use AI models on BioHive-2 to direct their platform to the most promising biology areas to run their experiments.

“With AI in the loop today, we can get 80% of the value with 40% of the wet lab work, and that ratio will improve going forward,” he said.

Biological Data Propels Healthcare AI

Recursion is collaborating with biopharma companies such as Bayer AG, Roche and Genentech. Over time, it also amassed a more than 50-petabyte database of biological, chemical and patient data, helping it build powerful AI models that are accelerating drug discovery.

“We believe it’s one of the largest biological datasets on Earth — it was built with AI training in mind, intentionally spanning biology and chemistry,” said Mabey, who joined the company more than seven years ago in part due to its commitment to building such a dataset.

Creating an AI Phenomenon

Processing that data on BioHive-1, Recursion developed a family of foundation models called Phenom. They turn a series of microscopic cellular images into meaningful representations for understanding the underlying biology.

A member of that family, Phenom-Beta, is now available as a cloud API and the first third-party model on NVIDIA BioNeMo, a generative AI platform for drug discovery.

Over several months of research and iteration, BioHive-1 trained Phenom-1 using more than 3.5 billion cellular images. Recursion’s expanded system enables training even more powerful models with larger datasets in less time.

The company also used NVIDIA DGX Cloud, hosted by Oracle Cloud Infrastructure, to provide additional supercomputing resources to power their work.

The Phenom-1 model serves Recursion and its partners in several ways, including finding and optimizing molecules to treat a variety of diseases and cancers. Earlier models helped Recursion predict drug candidates for COVID-19 nine out of 10 times.

The company announced its collaboration with NVIDIA in July. Less than 30 days later, the combination of BioHive-1 and DGX Cloud screened and analyzed a massive chemical library to predict protein targets for approximately 36 billion chemical compounds.

In January, the company demonstrated LOWE, an AI workflow engine with a natural-language interface to help make its tools more accessible to scientists. And in April it described a billion-parameter AI model it built to provide a new way to predict the properties of key molecules of interest in healthcare.

Recursion uses NVIDIA software to optimize its systems.

“We love CUDA and NVIDIA AI Enterprise, and we’re looking to see if NVIDIA NIM can help us distribute our models more easily, both internally and to partners,” he said.

A Shared Vision for Healthcare

The efforts are part of a broad vision that Jensen Huang, NVIDIA founder and CEO, described in a fireside chat with Recursion’s chairman as moving toward simulating biology.

“You can now recognize and learn the language of almost anything with structure, and you can translate it to anything with structure … This is the generative AI revolution,” Huang said.

“We share a similar view,” said Mabey.

“We are in the early stages of a very interesting time where just as computers accelerated chip design, AI can speed up drug design. Biology is much more complex, so it will take years to play out, but looking back, people will see this was a real turning point in healthcare,” he added.

Learn about NVIDIA’s AI platform for healthcare and life sciences and subscribe to NVIDIA healthcare news.

Pictured at top: BioHive-2 with a few members of the Recursion team (from left) Paige Despain, John Durkin, Joshua Fryer, Jesse Dean, Ganesh Jagannathan, Chris Gibson, Lindsay Ellinger, Michael Secora, Alex Timofeyev, and Ben Mabey. 

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Quantum computing. Drug discovery. Fusion energy. Scientific computing and physics-based simulations are poised to make giant steps across domains that benefit humanity as advances in accelerated computing and AI drive the world’s next big breakthroughs.

NVIDIA unveiled at GTC in March the NVIDIA Blackwell platform, which promises generative AI on trillion-parameter large language models (LLMs) at up to 25x less cost and energy consumption than the NVIDIA Hopper architecture.

Blackwell has powerful implications for AI workloads, and its technology capabilities can also help to deliver discoveries across all types of scientific computing applications, including traditional numerical simulation.

By reducing energy costs, accelerated computing and AI drive sustainable computing. Many scientific computing applications already benefit. Weather can be simulated at 200x lower cost and with 300x less energy, while digital twin simulations have 65x lower cost and 58x less energy consumption versus traditional CPU-based systems and others.

Multiplying Scientific Computing Simulations With Blackwell

Scientific computing and physics-based simulation often rely on what’s known as double-precision formats, or FP64 (floating point), to solve problems. Blackwell GPUs deliver 30% more FP64 and FP32 FMA (fused multiply-add) performance  than Hopper.

Physics-based simulations are critical to product design and development. From planes and trains to bridges, silicon chips and pharmaceuticals — testing and improving products in simulation saves researchers and developers billions of dollars.

Today application-specific integrated circuits (ASICs) are designed almost exclusively on CPUs in a long and complex workflow, including analog analysis to identify voltages and currents.

But that’s changing. The Cadence SpectreX simulator is one example of an analog circuit design solver. SpectreX circuit simulations are projected to run 13x quicker on a GB200 Grace Blackwell Superchip — which connects Blackwell GPUs and Grace CPUs — than on a traditional CPU.

Also, GPU-accelerated computational fluid dynamics, or CFD, has become a key tool. Engineers and equipment designers use it to predict the behavior of designs. Cadence Fidelity runs CFD simulations that are projected to run as much as 22x faster on GB200 systems than on traditional CPU-powered systems. With parallel scalability and 30TB of memory per GB200 NVL72 rack, it’s possible to capture flow details like never before.

In another application, Cadence Reality’s digital twin software can be used to create a virtual replica of a physical data center, including all its components — servers, cooling systems and power supplies. Such a virtual model allows engineers to test different configurations and scenarios before implementing them in the real world, saving time and costs.

Cadence Reality’s magic happens from physics-based algorithms that can simulate how heat, airflow and power usage affect data centers. This helps engineers and data center operators to more effectively manage capacity, predict potential operational problems and make informed decisions to optimize the layout and operation of the data center for improved efficiency and capacity utilization. With Blackwell GPUs, these simulations are projected to run up to 30x faster than with CPUs, offering accelerated timelines and higher energy efficiency.

AI for Scientific Computing

New Blackwell accelerators and networking will deliver leaps in performance for advanced simulation.

The NVIDIA GB200 kicks off a new era for high-performance computing (HPC). Its architecture sports a second-generation transformer engine optimized to accelerate inference workloads for LLMs.

This delivers a 30x speedup on resource-intensive applications like the 1.8-trillion-parameter GPT-MoE (generative pretrained transformer-mixture of experts) model compared to the H100 generation, unlocking new possibilities for HPC. By enabling LLMs to process and decipher vast amounts of scientific data, HPC applications can sooner reach valuable insights that can accelerate scientific discovery.

Sandia National Laboratories is building an LLM copilot for parallel programming. Traditional AI can generate basic serial computing code efficiently, but when it comes to parallel computing code for HPC applications, LLMs can falter. Sandia researchers are tackling this issue head-on with an ambitious project — automatically generating parallel code in Kokkos, a specialized programming language designed by multiple national labs for running tasks across tens of thousands of processors in the world’s most powerful supercomputers.

Sandia is using an AI technique known as retrieval-augmented generation, or RAG, which combines information-retrieval capabilities with language generation models. The team is creating a Kokkos database and integrating it with AI models using RAG.

Initial results are promising. Different RAG approaches from Sandia have demonstrated autonomously generated Kokkos code for parallel computing applications. By overcoming hurdles in AI-based parallel code generation, Sandia aims to unlock new possibilities in HPC across leading supercomputing facilities worldwide. Other examples include renewables research, climate science and drug discovery.

Driving Quantum Computing Advances

Quantum computing unlocks a time machine trip for fusion energy, climate research, drug discovery and many more areas. So researchers are hard at work simulating future quantum computers on NVIDIA GPU-based systems and software to develop and test quantum algorithms faster than ever.

The NVIDIA CUDA-Q platform enables both simulation of quantum computers and hybrid application development with a unified programming model for CPUs, GPUs and QPUs (quantum processing units) working together.

CUDA-Q is speeding simulations in chemistry workflows for BASF, high-energy and nuclear physics for Stony Brook and quantum chemistry for NERSC.

NVIDIA Blackwell architecture will help drive quantum simulations to new heights. Utilizing the latest NVIDIA NVLink multi-node interconnect technology helps shuttle data faster for speedup benefits to quantum simulations.

Accelerating Data Analytics for Scientific Breakthroughs 

Data processing with RAPIDS is popular for scientific computing. Blackwell introduces a hardware decompression engine to decompress compressed data and speed up analytics in RAPIDS.

The decompression engine provides performance improvements up to 800GB/s and enables Grace Blackwell to perform 18x faster than CPUs — on Sapphire Rapids — and 6x faster than NVIDIA H100 Tensor Core GPUs for query benchmarks.

Rocketing data transfers with 8TB/s of high-memory bandwidth and the Grace CPU high-speed NVLink Chip-to-Chip (C2C) interconnect, the engine speeds up the entire process of database queries. Yielding top-notch performance across data analytics and data science use cases, Blackwell speeds data insights and reduces costs.

Driving Extreme Performance for Scientific Computing with NVIDIA Networking

The NVIDIA Quantum-X800 InfiniBand networking platform offers the highest throughput for scientific computing infrastructure.

It includes NVIDIA Quantum Q3400 and Q3200 switches and the NVIDIA ConnectX-8 SuperNIC, together hitting twice the bandwidth of the prior generation. The Q3400 platform offers 5x higher bandwidth capacity and 14.4Tflops of in-network computing with NVIDIA’s scalable hierarchical aggregation and reduction protocol (SHARPv4), providing a 9x increase compared with the prior generation.

The performance leap and power efficiency translates to significant reductions in workload completion time and energy consumption for scientific computing.

Learn more about NVIDIA Blackwell.

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