Posts in category: nVidia AI

Artificial intelligence, once the realm of science fiction, claimed its place at the pinnacle of scientific achievement Monday in Sweden.

In a historic ceremony at Stockholm’s iconic Konserthuset, John Hopfield and Geoffrey Hinton received the Nobel Prize in Physics for their pioneering work on neural networks — systems that mimic the brain’s architecture and form the bedrock of modern AI.

Meanwhile, Demis Hassabis and John Jumper accepted the Nobel Prize in Chemistry for Google DeepMind’s AlphaFold, a system that solved biology’s “impossible” problem: predicting the structure of proteins, a feat with profound implications for medicine and biotechnology.

These achievements go beyond academic prestige. They mark the start of an era where GPU-powered AI systems tackle problems once deemed unsolvable, revolutionizing multitrillion-dollar industries from healthcare to finance.

Hopfield’s Legacy and the Foundations of Neural Networks

In the 1980s, Hopfield, a physicist with a knack for asking big questions, brought a new perspective to neural networks.

He introduced energy landscapes — borrowed from physics — to explain how neural networks solve problems by finding stable, low-energy states. His ideas, abstract yet elegant, laid the foundation for AI by showing how complex systems optimize themselves.

Fast forward to the early 2000s, when Geoffrey Hinton — a British cognitive psychologist with a penchant for radical ideas — picked up the baton. Hinton believed neural networks could revolutionize AI, but training these systems required enormous computational power.

In 1983, Hinton and Sejnowski built on Hopfield’s work and invented the Boltzmann Machine which used stochastic binary neurons to jump out of local minima. They discovered an elegant and very simple learning procedure based on statistical mechanics which was an alternative to backpropagation.

In 2006 a simplified version of this learning procedure proved to be very effective at initializing deep neural networks before training them with backpropagation. However, training these systems still required enormous computational power.

AlphaFold: Biology’s AI Revolution

A decade after AlexNet, AI moved to biology. Hassabis and Jumper led the development of AlphaFold to solve a problem that had stumped scientists for years: predicting the shape of proteins.

Proteins are life’s building blocks. Their shapes determine what they can do. Understanding these shapes is the key to fighting diseases and developing new medicines. But finding them was slow, costly and unreliable.

AlphaFold changed that. It used Hopfield’s ideas and Hinton’s networks to predict protein shapes with stunning accuracy. Powered by GPUs, it mapped almost every known protein. Now, scientists use AlphaFold to fight drug resistance, make better antibiotics and treat diseases once thought to be incurable.

What was once biology’s Gordian knot has been untangled — by AI.

The GPU Factor: Enabling AI’s Potential

GPUs, the indispensable engines of modern AI, are at the heart of these achievements. Originally designed to make video games look good, GPUs were perfect for the massive parallel processing demands of neural networks.

NVIDIA GPUs, in particular, became the engine driving breakthroughs like AlexNet and AlphaFold. Their ability to process vast datasets with extraordinary speed allowed AI to tackle problems on a scale and complexity never before possible.

Redefining Science and Industry

The Nobel-winning breakthroughs of 2024 aren’t just rewriting textbooks — they’re optimizing global supply chains, accelerating drug development and helping farmers adapt to changing climates.

Hopfield’s energy-based optimization principles now inform AI-powered logistics systems. Hinton’s architectures underpin self-driving cars and language models like ChatGPT. AlphaFold’s success is inspiring AI-driven approaches to climate modeling, sustainable agriculture and even materials science.

The recognition of AI in physics and chemistry signals a shift in how we think about science. These tools are no longer confined to the digital realm. They’re reshaping the physical and biological worlds.

Read More

Quantum computing has the potential to transform industries ranging from drug discovery to logistics, but a huge barrier standing between today’s quantum devices and useful applications is noise. These disturbances, introduced by environmental interactions and imperfect hardware, mean that today’s qubits can only perform hundreds of operations before quantum computations irretrievably deteriorate. 

Though seemingly inevitable, noise in quantum hardware can be tackled by so-called logical qubits – collections of tens, hundreds or even thousands of actual physical qubits that allow the correction of noise-induced errors. Logical qubits are the holy grail of quantum computing, and quantum hardware builder Infleqtion today published groundbreaking work that used the NVIDIA CUDA-Q platform to both design and demonstrate an experiment with two of them.  

These logical qubits were used to perform a small-scale demonstration of the so-called single-impurity Anderson model, a high-accuracy approach necessary for many important materials science applications. 

This constitutes the first time that a demonstration of a materials science quantum algorithm has been performed on logical qubits. The creation of just a single logical qubit is extremely challenging. Infleqtion was able to achieve such a feat thanks to accurate modeling of its quantum computer using CUDA-Q’s unique GPU-accelerated simulation capabilities.  

Having developed and tested its entire experiment within CUDA-Q’s simulators, with only trivial changes, Infleqtion could then use CUDA-Q to orchestrate the experiment using the actual physical qubits within its Sqale neutral atom quantum processor. 

This work sets the stage for quantum computing’s move toward large-scale, error-corrected systems.  

Many scaling challenges still stand between today’s quantum devices and large systems of logical qubits, which will only be solved by integrating quantum hardware with AI supercomputers to form accelerated quantum supercomputers.  

NVIDIA continues to work with partners like Infleqtion to enable this breakthrough research needed to make accelerated quantum supercomputing a reality. 

Learn more about NVIDIA’s quantum computing platforms. 

Read More

Content streaming and engagement are entering a new dimension with QUEEN, an AI model by NVIDIA Research and the University of Maryland that makes it possible to stream free-viewpoint video, which lets viewers experience a 3D scene from any angle.

QUEEN could be used to build immersive streaming applications that teach skills like cooking, put sports fans on the field to watch their favorite teams play from any angle, or bring an extra level of depth to video conferencing in the workplace. It could also be used in industrial environments to help teleoperate robots in a warehouse or a manufacturing plant.

The model will be presented at NeurIPS, the annual conference for AI research that begins Tuesday, Dec. 10, in Vancouver.

“To stream free-viewpoint videos in near real time, we must simultaneously reconstruct and compress the 3D scene,” said Shalini De Mello, director of research and a distinguished research scientist at NVIDIA. “QUEEN balances factors including compression rate, visual quality, encoding time and rendering time to create an optimized pipeline that sets a new standard for visual quality and streamability.”

Reduce, Reuse and Recycle for Efficient Streaming

Free-viewpoint videos are typically created using video footage captured from different camera angles, like a multicamera film studio setup, a set of security cameras in a warehouse or a system of videoconferencing cameras in an office.

Prior AI methods for generating free-viewpoint videos either took too much memory for livestreaming or sacrificed visual quality for smaller file sizes. QUEEN balances both to deliver high-quality visuals — even in dynamic scenes featuring sparks, flames or furry animals — that can be easily transmitted from a host server to a client’s device. It also renders visuals faster than previous methods, supporting streaming use cases.

In most real-world environments, many elements of a scene stay static. In a video, that means a large share of pixels don’t change from one frame to another. To save computation time, QUEEN tracks and reuses renders of these static regions — focusing instead on reconstructing the content that changes over time.

Using an NVIDIA Tensor Core GPU, the researchers evaluated QUEEN’s performance on several benchmarks and found the model outperformed state-of-the-art methods for online free-viewpoint video on a range of metrics. Given 2D videos of the same scene captured from different angles, it typically takes under five seconds of training time to render free-viewpoint videos at around 350 frames per second.

This combination of speed and visual quality can support media broadcasts of concerts and sports games by offering immersive virtual reality experiences or instant replays of key moments in a competition.

In warehouse settings, robot operators could use QUEEN to better gauge depth when maneuvering physical objects. And in a videoconferencing application — such as the 3D videoconferencing demo shown at SIGGRAPH and NVIDIA GTC — it could help presenters demonstrate tasks like cooking or origami while letting viewers pick the visual angle that best supports their learning.

The code for QUEEN will soon be released as open source and shared on the project page.

QUEEN is one of over 50 NVIDIA-authored NeurIPS posters and papers that feature groundbreaking AI research with potential applications in fields including simulation, robotics and healthcare.

Generative Adversarial Nets, the paper that first introduced GAN models, won the NeurIPS 2024 Test of Time Award. Cited more than 85,000 times, the paper was coauthored by Bing Xu, distinguished engineer at NVIDIA. Hear more from its lead author, Ian Goodfellow, research scientist at DeepMind, on the AI Podcast:

Learn more about NVIDIA Research at NeurIPS.

See the latest work from NVIDIA Research, which has hundreds of scientists and engineers worldwide, with teams focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics.

Academic researchers working on large language models, simulation and modeling, edge AI and more can apply to the NVIDIA Academic Grant Program.

See notice regarding software product information.

Read More

Southeast Asia is embracing sovereign AI.

The prime ministers of Thailand and Vietnam this week met with NVIDIA founder and CEO Jensen Huang to discuss initiatives that will accelerate AI innovation in their countries.

During his visit to the region, Huang also joined Bangkok-based cloud infrastructure company SIAM.AI Cloud onstage for a fireside chat on sovereign AI. In Vietnam, he announced NVIDIA’s collaboration with the country’s government on an AI research and development center — and NVIDIA’s acquisition of VinBrain, a health technology startup funded by Vingroup, one of Vietnam’s largest public companies.

These events capped a year of global investments in sovereign AI, the ability for countries to develop and harness AI using domestic computing infrastructure, data and workforces. AI will contribute nearly $20 trillion to the global economy through the end of the decade, according to IDC.

Canada, Denmark and Indonesia are among the countries that have announced initiatives to develop sovereign AI infrastructure powered by NVIDIA technology. And at the recent NVIDIA AI Summits in India and Japan, leading enterprises, infrastructure providers and startups in both countries announced sovereign AI projects in sectors including finance, healthcare and manufacturing.

Supporting Sovereign Cloud Infrastructure in Thailand

Huang’s Southeast Asia visit kicked off with a meeting with Thailand Prime Minister Paetongtarn Shinawatra, where he discussed the opportunities for sovereign AI development in Thailand and shared memories of his childhood years spent in Bangkok.

The pair discussed how further investing in AI education and training can help Thailand drive AI innovations in fields such as weather prediction, climate simulation and healthcare. NVIDIA is working with dozens of local universities and startups to support AI advancement in the country.

Huang later took the stage at an “AI Vision for Thailand” event hosted by SIAM.AI Cloud, a cloud platform company that offers customers access to virtual servers featuring NVIDIA Tensor Core GPUs.

“The most important part of artificial intelligence is the data. And the data of Thailand belongs to the Thai people,” Huang said in a fireside chat with Ratanaphon Wongnapachant, CEO of SIAM.AI Cloud. Highlighting the importance of sovereign AI development, Huang said, “The digital data of Thailand encodes the knowledge, the history, the culture, the common sense of your people. It should be harvested by your people.”

Following the conversation, Wongnapachant gifted Huang a custom leather jacket lined with Thai silk. The pair also signed an NVIDIA DGX H200 system in recognition of SIAM.AI Cloud’s plans to expand its offerings to NVIDIA H200 Tensor Core GPUs and NVIDIA GB200 Grace Blackwell Superchips.

Advancing AI From Research to Industry in Vietnam

In Hanoi the next day, Huang met with Vietnam’s Prime Minister Pham Minh Chinh, and NVIDIA signed an agreement to build the company’s first research and development center in the country. The center will focus on software development and collaborate with Vietnam’s enterprises, startups, government agencies and universities to accelerate AI adoption in the country.

The announcement builds on NVIDIA’s existing work with 65 universities in Vietnam and more than 100 of the country’s AI startups through NVIDIA Inception, a global program designed to help startups evolve faster. NVIDIA has acquired Inception member VinBrain, a Hanoi-based company that applies AI diagnostics to multimodal health data.

While in Vietnam, Huang also received the 2024 VinFuture Prize alongside AI pioneers Yoshua Bengio, Geoffrey Hinton, Yann Le Cun and Fei-Fei Li for their “transformational contributions to the advancement of deep learning.”

Broadcast live nationally in the country, the awards ceremony was hosted by the VinFuture Foundation, a nonprofit that recognizes innovations in science and technology with significant societal impact.

“Our award today is recognition by the VinFuture committee of the transformative power of AI to revolutionize every field of science and every industry,” Huang said in his acceptance speech.

Learn more about sovereign AI.

Editor’s note: The data on the economic impact of AI is from IDC’s press release titled “IDC: Artificial Intelligence Will Contribute $19.9 Trillion to the Global Economy through 2030 and Drive 3.5% of Global GDP in 2030,” published in September 2024.

Read More

From boardroom to break room, generative AI took this year by storm, stirring discussion across industries about how to best harness the technology to enhance innovation and creativity, improve customer service, transform product development and even boost communication.

Yet, some companies and startups are still slow to adopt AI, sticking to experimentation and siloed projects even as the technology advances at a dizzying pace. That’s partly because AI benefits vary by company, use case and level of investment.

Cautious approaches are giving way to optimism. Two-thirds of the respondents to Forrester Research’s 2024 State of AI Survey believe their organizations would require less than 50% return on investments to consider their AI initiatives successful.

The next big thing on the horizon is agentic AI, a form of autonomous or “reasoning” AI that requires using diverse language models, sophisticated retrieval-augmented generation stacks and advanced data architectures.

NVIDIA experts in industry verticals already shared their expectations for the year ahead. Now, hear from company experts driving innovation in AI across enterprises, research and the startup ecosystem:

IAN BUCK
Vice President of Hyperscale and HPC

Inference drives the AI charge: As AI models grow in size and complexity, the demand for efficient inference solutions will increase.

The rise of generative AI has transformed inference from simple recognition of the query and response to complex information generation — including summarizing from multiple sources and large language models such as OpenAI o1 and Llama 450B — which dramatically increases computational demands. Through new hardware innovations, coupled with continuous software improvements, performance will increase and total cost of ownership is expected to shrink by 5x or more.

Accelerate everything: With GPUs becoming more widely adopted, industries will look to accelerate everything, from planning to production. New architectures will add to that virtuous cycle, delivering cost efficiencies and an order of magnitude higher compute performance with each generation.

As nations and businesses race to build AI factories to accelerate even more workloads, expect many to look for platform solutions and reference data center architectures or blueprints that can get a data center up and running in weeks versus months. This will help them solve some of the world’s toughest challenges, including quantum computing and drug discovery.

Quantum computing — all trials, no errors: Quantum computing will make significant strides as researchers focus on supercomputing and simulation to solve the greatest challenges to the nascent field: errors.

Qubits, the basic unit of information in quantum computing, are susceptible to noise, becoming unstable after performing only thousands of operations. This prevents today’s quantum hardware from solving useful problems. In 2025, expect to see the quantum computing community move toward challenging, but crucial, quantum error correction techniques. Error correction requires quick, low-latency calculations. Also expect to see quantum hardware that’s physically colocated within supercomputers, supported by specialized infrastructure.

AI will also play a crucial role in managing these complex quantum systems, optimizing error correction and enhancing overall quantum hardware performance. This convergence of quantum computing, supercomputing and AI into accelerated quantum supercomputers will drive progress in realizing quantum applications for solving complex problems across various fields, including drug discovery, materials development and logistics.

BRYAN CATANZARO
Vice President of Applied Deep Learning Research

Putting a face to AI: AI will become more familiar to use, emotionally responsive and marked by greater creativity and diversity. The first generative AI models that drew pictures struggled with simple tasks like drawing teeth. Rapid advances in AI are making image and video outputs much more photorealistic, while AI-generated voices are losing that robotic feel.

These advancements will be driven by the refinement of algorithms and datasets and enterprises’ acknowledgment that AI needs a face and a voice to matter to 8 billion people. This will also cause a shift from turn-based AI interactions to more fluid and natural conversations. Interactions with AI will no longer feel like a series of exchanges but instead offer a more engaging and humanlike conversational experience.

Rethinking industry infrastructure and urban planning: Nations and industries will begin examining how AI automates various aspects of the economy to maintain the current standard of living, even as the global population shrinks.

These efforts could help with sustainability and climate change. For instance, the agriculture industry will begin investing in autonomous robots that can clean fields and remove pests and weeds mechanically. This will reduce the need for pesticides and herbicides, keeping the planet healthier and freeing up human capital for other meaningful contributions. Expect to see new thinking in urban planning offices to account for autonomous vehicles and improve traffic management.

Longer term, AI can help find solutions for reducing carbon emissions and storing carbon, an urgent global challenge.

KARI BRISKI
Vice President of Generative AI Software

A symphony of agents — AI orchestrators: Enterprises are set to have a slew of AI agents, which are semiautonomous, trained models that work across internal networks to help with customer service, human resources, data security and more. To maximize these efficiencies, expect to see a rise in AI orchestrators that work across numerous agents to seamlessly route human inquiries and interpret collective results to recommend and take actions for users.

These orchestrators will have access to deeper content understanding, multilingual capabilities and fluency with multiple data types, ranging from PDFs to video streams. Powered by self-learning data flywheels, AI orchestrators will continuously refine business-specific insights. For instance, in manufacturing, an AI orchestrator could optimize supply chains by analyzing real-time data and making recommendations on production schedules and supplier negotiations.

This evolution in enterprise AI will significantly boost productivity and innovation across industries while becoming more accessible. Knowledge workers will be more productive because they can tap into a personalized team of AI-powered experts. Developers will be able to build these advanced agents using customizable AI blueprints.

Multistep reasoning amplifies AI insights: AI for years has been good at giving answers to specific questions without having to delve into the context of a given query. With advances in accelerated computing and new model architectures, AI models will tackle increasingly complex problems and respond with greater accuracy and deeper analysis.

Using a capability called multistep reasoning, AI systems increase the amount of “thinking time” by breaking down large, complex questions into smaller tasks — sometimes even running multiple simulations — to problem-solve from various angles. These models dynamically evaluate each step, ensuring contextually relevant and transparent responses. Multistep reasoning also involves integrating knowledge from various sources to enable AI to make logical connections and synthesize information across different domains.

This will likely impact fields ranging from finance and healthcare to scientific research and entertainment. For example, a healthcare model with multistep reasoning could make a number of recommendations for a doctor to consider, depending on the patient’s diagnosis, medications and response to other treatments.

Start your AI query engine: With enterprises and research organizations sitting on petabytes of data, the challenge is gaining quick access to the data to deliver actionable insights.

AI query engines will change how businesses mine that data, and company-specific search engines will be able to sift through structured and unstructured data, including text, images and videos, using natural language processing and machine learning to interpret a user’s intent and provide more relevant and comprehensive results.

This will lead to more intelligent decision-making processes, improved customer experiences and enhanced productivity across industries. The continuous learning capabilities of AI query engines will create self-improving data flywheels that help  applications become increasingly effective.

CHARLIE BOYLE
Vice President of DGX Platforms

Agentic AI makes high-performance inference essential for enterprises: The dawn of agentic AI will drive demand for near-instant responses from complex systems of multiple models. This will make high-performance inference just as important as high-performance training infrastructure. IT leaders will need scalable, purpose-built and optimized accelerated computing infrastructure that can keep pace with the demands of agentic AI to deliver the performance required for real-time decision-making.

Enterprises expand AI factories to process data into intelligence: Enterprise AI factories transform raw data into business intelligence. Next year, enterprises will expand these factories to leverage massive amounts of historical and synthetic data, then generate forecasts and simulations for everything from consumer behavior and supply chain optimization to financial market movements and digital twins of factories and warehouses. AI factories will become a key competitive advantage that helps early adopters anticipate and shape future scenarios, rather than just react to them.

Chill factor — liquid-cooled AI data centers: As AI workloads continue to drive growth, pioneering organizations will transition to liquid cooling to maximize performance and energy efficiency. Hyperscale cloud providers and large enterprises will lead the way, using liquid cooling in new AI data centers that house hundreds of thousands of AI accelerators, networking and software.

Enterprises will increasingly choose to deploy AI infrastructure in colocation facilities rather than build their own — in part to ease the financial burden of designing, deploying and operating intelligence manufacturing at scale. Or, they will rent capacity as needed. These deployments will help enterprises harness the latest infrastructure without needing to install and operate it themselves. This shift will accelerate broader industry adoption of liquid cooling as a mainstream solution for AI data centers.

GILAD SHAINER
Senior Vice President of Networking 

Goodbye network, hello computing fabric:  The term “networking” in the data center will seem dated as data center architecture transforms into an integrated compute fabric that enables thousands of accelerators to efficiently communicate with one another via scale-up and scale-out communications, spanning miles of cabling and multiple data center facilities.

This integrated compute fabric will include NVIDIA NVLink, which enables scale-up communications, as well as scale-out capabilities enabled by intelligent switches, SuperNICs and DPUs. This will help securely move data to and from accelerators and perform calculations on the fly that drastically minimize data movement. Scale-out communication across networks will be crucial to large-scale AI data center deployments — and key to getting them up and running in weeks versus months or years.

As agentic AI workloads grow — requiring communication across multiple interconnected AI models working together rather than monolithic and localized AI models — compute fabrics will be essential to delivering real-time generative AI.

Distributed AI: All data centers will become accelerated as new approaches to Ethernet design emerge that enable hundreds of thousands of GPUs to support a single workload. This will help democratize AI factory rollouts for multi-tenant generative AI clouds and enterprise AI data centers.

This breakthrough technology will also enable AI to expand quickly into enterprise platforms and simplify the buildup and management of AI clouds.

Companies will build data center resources that are more geographically dispersed — located hundreds or even thousands of miles apart — because of power limitations and the need to build closer to renewable energy sources. Scale-out communications will ensure reliable data movement over these long distances.

LINXI (JIM) FAN
Senior Research Scientist, AI Agents

Robotics will evolve more into humanoids: Robots will begin to understand arbitrary language commands. Right now, industry robots must be programmed by hand, and they don’t respond intelligently to unpredictable inputs or languages other than those programmed. Multimodal robot foundation models that incorporate vision, language and arbitrary actions will evolve this “AI brain,” as will agentic AI that allows for greater AI reasoning.

To be sure, don’t expect to immediately see intelligent robots in homes, restaurants, service areas and factories. But these use cases may be closer than you think, as governments look for solutions to aging societies and shrinking labor pools. Physical automation is going to happen gradually, in 10 years being as ubiquitous as the iPhone.

AI agents are all about inferencing: In September, OpenAI announced a new large language model trained with reinforcement learning to perform complex reasoning. OpenAI o1, dubbed Strawberry, thinks before it answers: It can produce a long internal chain of thought, correcting mistakes and breaking down tricky steps into simple ones, before responding to the user.

2025 will be the year a lot of computation begins to shift to inference at the edge. Applications will need hundreds of thousands of tokens for a single query, as small language models make one query after another in microseconds before churning out an answer.

Small models will be more energy efficient and will become increasingly important for robotics, creating humanoids and robots that can assist humans in everyday jobs and promoting mobile intelligence applications..

BOB PETTE
Vice President of Enterprise Platforms

Seeking sustainable scalability: As enterprises prepare to embrace a new generation of semiautonomous AI agents to enhance various business processes, they’ll focus on creating robust infrastructure, governance and human-like capabilities for effective large-scale deployment. At the same time, AI applications will increasingly use local processing power to enable more sophisticated AI features to run directly on workstations, including thin, lightweight laptops and compact form factors, and improve performance while reducing latency for AI-driven tasks.

Validated reference architectures, which provide guidance on appropriate hardware and software platforms, will become crucial to optimize performance and accelerate AI deployments. These architectures will serve as essential tools for organizations navigating the complex terrain of AI implementation by helping ensure that their investments align with current needs and future technological advancements.

Revolutionizing construction, engineering and design with AI: Expect to see a rise in generative AI models tailored to the construction, engineering and design industries that will boost efficiency and accelerate innovation.

In construction, agentic AI will extract meaning from massive volumes of construction data collected from onsite sensors and cameras, offering insights that lead to more efficient project timelines and budget management.

AI will evaluate reality capture data (lidar, photogrammetry and radiance fields) 24/7 and derive mission-critical insights on quality, safety and compliance — resulting in reduced errors and worksite injuries.

For engineers, predictive physics based on physics-informed neural networks will accelerate flood prediction, structural engineering and computational fluid dynamics for airflow solutions tailored to individual rooms or floors of a building — allowing for faster design iteration.

In design, retrieval-augmented generation will enable compliance early in the design phase by ensuring that information modeling for designing and constructing buildings complies with local building codes. Diffusion AI models will accelerate conceptual design and site planning by enabling architects and designers to combine keyword prompts and rough sketches to generate richly detailed conceptual images for client presentations. That will free up time to focus on research and design.

SANJA FIDLER
Vice President of AI Research

Predicting unpredictability: Expect to see more models that can learn in the everyday world, helping digital humans, robots and even autonomous cars understand chaotic and sometimes unpredictable situations, using very complex skills with little human intervention.

From the research lab to Wall Street, we’re entering a hype cycle similar to the optimism about autonomous driving 5-7 years ago. It took many years for companies like Waymo and Cruise to deliver a system that works — and it’s still not scalable because the troves of data these companies and others, including Tesla, have collected may be applicable in one region but not another.

With models introduced this year, we can now move more quickly — and with much less capital expense — to use internet-scale data to understand natural language and emulate movements by observing human and other actions. Edge applications like robots, cars and warehouse machinery will quickly learn coordination, dexterity and other skills in order to navigate, adapt and interact with the real world.

Will a robot be able to make coffee and eggs in your kitchen, and then clean up after? Not yet. But it may come sooner than you think.

Getting real: Fidelity and realism is coming to generative AI across the graphics and simulation pipeline, leading to hyperrealistic games, AI-generated movies and digital humans.

Unlike with traditional graphics, the vast majority of images will come from generated pixels instead of renderings, resulting in more natural motions and appearances. Tools that develop and iterate on contextual behaviors will result in more sophisticated games for a fraction of the cost of today’s AAA titles.

Industries adopt generative AI: Nearly every industry is poised to use AI to enhance and improve the way people live and play.

Agriculture will use AI to optimize the food chain, improving the delivery of food. For example, AI can be used to predict the greenhouse gas emissions from different crops on individual farms. These analyses can help inform design strategies that help reduce greenhouse gas in supply chains. Meanwhile, AI agents in education will personalize learning experiences, speaking in a person’s native language and asking or answering questions based on level of education in a particular subject.

As next-generation accelerators enter the marketplace, you’ll also see a lot more efficiency in delivering these generative AI applications. By improving the training and efficiency of the models in testing, businesses and startups will see better and faster returns on investment across those applications.

ANDREW FENG
Vice President of GPU Software 

Accelerated data analytics offers insights with no code change: In 2025, accelerated data analytics will become mainstream for organizations grappling with ever-increasing volumes of data.

Businesses generate hundreds of petabytes of data annually, and every company is seeking ways to put it to work. To do so, many will adopt accelerated computing for data analytics.

The future lies in accelerated data analytics solutions that support “no code change” and “no configuration change,” enabling organizations to combine their existing data analytics applications with accelerated computing with minimum effort. Generative AI-empowered analytics technology will further widen the adoption of accelerated data analytics by empowering users — even those who don’t have traditional programming knowledge — to create new data analytics applications.

The seamless integration of accelerated computing, facilitated by a simplified developer experience, will help eliminate adoption barriers and allow organizations to harness their unique data for new AI applications and richer business intelligence.

NADER KHALIL
Director of Developer Technology

The startup workforce: If you haven’t heard much about prompt engineers or AI personality designers, you will in 2025. As businesses embrace AI to increase productivity, expect to see new categories of essential workers for both startups and enterprises that blend new and existing skills.

A prompt engineer designs and refines precise text strings that optimize AI training and produce desired outcomes based on the creation, testing and iteration of prompt designs for chatbots and agentic AI. The demand for prompt engineers will extend beyond tech companies to sectors like legal, customer support and publishing. As AI agents proliferate, businesses and startups will increasingly lean in to AI personality designers to enhance agents with unique personalities.

Just as the rise of computers spawned job titles like computer scientists, data scientists and machine learning engineers, AI will create different types of work, expanding opportunities for people with strong analytical skills and natural language processing abilities.

Understanding employee efficiency: Startups incorporating AI into their practices increasingly will add revenue per employee (RPE) to their lexicon when talking to investors and business partners.

Instead of a “growth at all costs” mentality, AI supplementation of the workforce will allow startup owners to home in on how hiring each new employee helps everyone else in the business generate more revenue. In the world of startups, RPE fits into discussions about the return on investment in AI and the challenges of filling roles in competition against big enterprises and tech companies.

Read More

GeForce NOW is wrapping a sleigh-full of gaming gifts this month, stuffing members’ cloud gaming stockings with new titles and fresh offers to keep holiday gaming spirits merry and bright.

Adventure calls and whip-cracking action awaits in the highly anticipated Indiana Jones and the Great Circle, streaming in the cloud today during the Advanced Access period for those who have preordered the Premium Edition from Steam or the Microsoft Store.

The title can only be played with RTX ON — GeForce NOW is offering gamers without high-performance hardware the ability to play it with 25% off Ultimate and Performance Day Passes. It’s like finding that extra-special gift hidden behind the tree.

This GFN Thursday also brings a new limited-time offer: 50% off the first month of new Ultimate or Performance memberships — a gift that can keep on giving.

Whether looking to try out GeForce NOW or buckle in for long-term cloud gaming, new members can choose between the Day Pass sale or the new membership offer. There’s a perfect gaming gift for everyone this holiday season.

GFN Thursday also brings 13 new titles in December, with four available this week to get the festivities going.

Plus, the latest update to GeForce NOW — version 2.0.69 — includes expanded support for 10-bit color precision. This feature enhances image quality when streaming on Windows, macOS and NVIDIA SHIELD TVs — and now to Edge and Chrome browsers on Windows devices, as well as to the Chrome browser on Chromebooks, Samsung TVs and LG TVs.

An Epic, Globetrotting Adventure

Uncover one of history’s greatest mysteries, streaming Indiana Jones and the Great Circle from the cloud. Members can access it today through Steam’s Advance Access period and will also be able to enjoy it via PC Game Pass on GeForce NOW next week.

The year is 1937, sinister forces are scouring the globe for the secret to an ancient power connected to the Great Circle, and only Indiana Jones can stop them. Experience a thrilling story full of exploration, immersive action and intriguing puzzles. Travel the world, from the pyramids of Egypt to the sunken temples of Sukhothai and beyond. Combine stealth infiltration, melee combat and gunplay to overcome enemies. Use guile and wits to unearth fascinating secrets, solve ancient riddles and survive deadly traps.

Members can indulge their inner explorer by streaming Indiana Jones and the Great Circle on GeForce NOW at release. Enhanced with NVIDIA’s ray-tracing technology, every game scene is bathed in rich, natural light that bounces realistically off surfaces for enhanced immersion.

Ultimate and Performance members can max out their settings for a globe-trotting journey at the highest resolution and lowest latency, even on low-powered devices, thanks to enhancements like NVIDIA DLSS 3 Frame Generation and NVIDIA Reflex. Ultimate members can experience additional perks, like 4K resolution and longer gaming sessions.

This game requires RTX ON, so free members can upgrade today to join in on the action. Take advantage of a limited-time Day Pass sale, with 25% off through Thursday, Dec. 12. Experience all the premium features of GeForce NOW’s Ultimate and Performance tiers with a 24-hour trial before upgrading to a one- or six-month membership.

Making the Cloud Merry and Bright

For gamers looking to take their cloud gaming journey even further, unlock the power of GeForce RTX-powered cloud gaming with a monthly GeForce NOW membership. It’s the perfect time to do so, with new members gettings 50% off their first month, now through Monday, Dec. 30.

Experience gaming at its finest with an Ultimate membership by streaming at up to 4K resolution and 120 frames per second, or 1080p at 240 fps. The Performance membership offers an enhanced streaming experience at up to 1440p resolution with ultrawide resolutions for even more immersive gameplay. Both premium tiers provide extended session lengths, priority access to servers and the ability to play the latest and greatest titles with RTX ON.

Whether looking to conquer new worlds, compete at the highest level or unwind with a long-time favorite game, now is an ideal time to join the cloud gaming community. Sign up to transform any device into a powerful gaming rig — just in time for the holiday gaming marathons.

Dashing December

Path of Exile 2 is the highly anticipated sequel to the popular free-to-play action role-playing game from Grinding Gear Games. The game will be available for members to stream Friday, Dec. 6, in early access with a wealth of content to experience.

Explore the three acts of the campaign, six playable character classes and a robust endgame system in the dark world of Wraeclast, a continent populated by unique cultures, ancient secrets and monstrous dangers. A sinister threat, long thought destroyed, has begun creeping back on the edge of civilisation, driving people mad and sickening the land with Corruption. Play Path of Exile 2 solo or grab the squad for online co-op with up to six players.

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

• Indiana Jones and the Great Circle (Advanced Access on Steam and Xbox, available on the Microsoft Store)
• Path of Exile 2 (New release on Steam and Grinding Gears, Dec. 6)
• JR EAST Train Simulator (Steam)
• JR EAST Train Simulator Demo (Steam)

Here’s what members can expect in December:

• Fast Food Simulator (New release on Steam, Dec. 10)
• Legacy of Kain Soul Reaver 1&2 Remastered (New release on Steam, Dec. 10)
• The Spirit of the Samurai (New release on Steam, Dec. 12)
• The Lord of the Rings: Return to Moria (Steam)
• NieR:Automata (Steam)
• NieR Replicant ver.1.22474487139… (Steam)
• Replikant Chat (Steam)
• Supermarket Together (Steam)
• Ys X: Nordics (Steam)

New to November

In addition to the 17 games announced last month, 13 more joined the GeForce NOW library:

• Ara: History Untold (Steam and Xbox, available on PC Game Pass)
• Call of Duty: Black Ops Cold War (Steam and Battle.net)
• Call of Duty: Vanguard (Steam and Battle.net)
• Crash Bandicoot N. Sane Trilogy (Steam and Xbox, available on PC Game Pass)
• The Elder Scrolls IV: Oblivion Game of the Year Edition (Epic Games Store, Steam and Xbox, available on PC Game Pass)
• Fallout 3: Game of the Year Edition (Epic Games Store, Steam and Xbox, available on PC Game Pass)
• Magicraft (Steam)
• MEGA MAN X DiVE Offline Demo (Steam)
• New Arc Line (New release on Steam, Nov. 26)
• Resident Evil 7 Teaser: Beginning Hour Demo (Steam)
• Spyro Reignited Trilogy (Steam and Xbox, available on PC Game Pass)
• StarCraft II (Xbox, available on PC Game Pass, Nov. 5. Members need to enable access.)
• StarCraft Remastered (Xbox, available on PC Game Pass, Nov. 5. Members need to enable access.)

Metal Slug Tactics, Dungeons & Degenerate Gamblers and Headquarters: World War II didn’t make it last month. Stay tuned to future GFN Thursday for updates.

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

There’s no place like the cloud for the holidays.

Unlock the power of GeForce RTX-powered gaming with a GeForce NOW monthly membership — 𝟱𝟬% 𝗼𝗳𝗳 the first month for new members!

RUN https://t.co/aCfuQ7KtkB pic.twitter.com/8cjY5ZTBPW

— NVIDIA GeForce NOW (@NVIDIAGFN) December 4, 2024

Read More

Generative AI is rapidly transforming industries, driving demand for secure, high-performance inference solutions to scale increasingly complex models efficiently and cost-effectively.

Expanding its collaboration with NVIDIA, Amazon Web Services (AWS) revealed today at its annual AWS re:Invent conference that it has extended NVIDIA NIM microservices across key AWS AI services to support faster AI inference and lower latency for generative AI applications.

NVIDIA NIM microservices are now available directly from the AWS Marketplace, as well as Amazon Bedrock Marketplace and Amazon SageMaker JumpStart, making it even easier for developers to deploy NVIDIA-optimized inference for commonly used models at scale.

NVIDIA NIM, part of the NVIDIA AI Enterprise software platform available in the AWS Marketplace, provides developers with a set of easy-to-use microservices designed for secure, reliable deployment of high-performance, enterprise-grade AI model inference across clouds, data centers and workstations.

These prebuilt containers are built on robust inference engines, such as NVIDIA Triton Inference Server, NVIDIA TensorRT, NVIDIA TensorRT-LLM and PyTorch, and support a broad spectrum of AI models — from open-source community ones to NVIDIA AI Foundation models and custom ones.

NIM microservices can be deployed across various AWS services, including Amazon Elastic Compute Cloud (EC2), Amazon Elastic Kubernetes Service (EKS) and Amazon SageMaker.

Developers can preview over 100 NIM microservices built from commonly used models and model families, including Meta’s Llama 3, Mistral AI’s Mistral and Mixtral, NVIDIA’s Nemotron, Stability AI’s SDXL and many more on the NVIDIA API catalog. The most commonly used ones are available for self-hosting to deploy on AWS services and are optimized to run on NVIDIA accelerated computing instances on AWS.

NIM microservices now available directly from AWS include:

• NVIDIA Nemotron-4, available in Amazon Bedrock Marketplace, Amazon SageMaker Jumpstart and AWS Marketplace. This is a cutting-edge LLM designed to generate diverse synthetic data that closely mimics real-world data, enhancing the performance and robustness of custom LLMs across various domains.
• Llama 3.1 8B-Instruct, available on AWS Marketplace. This 8-billion-parameter multilingual large language model is pretrained and instruction-tuned for language understanding, reasoning and text-generation use cases.
• Llama 3.1 70B-Instruct, available on AWS Marketplace. This 70-billion-parameter pretrained, instruction-tuned model is optimized for multilingual dialogue.
• Mixtral 8x7B Instruct v0.1, available on AWS Marketplace. This high-quality sparse mixture of experts model with open weights can follow instructions, complete requests and generate creative text formats.

NIM on AWS for Everyone

Customers and partners across industries are tapping NIM on AWS to get to market faster, maintain security and control of their generative AI applications and data, and lower costs.

SoftServe, an IT consulting and digital services provider, has developed six generative AI solutions fully deployed on AWS and accelerated by NVIDIA NIM and AWS services. The solutions, available on AWS Marketplace, include SoftServe Gen AI Drug Discovery, SoftServe Gen AI Industrial Assistant, Digital Concierge, Multimodal RAG System, Content Creator and Speech Recognition Platform.

They’re all based on NVIDIA AI Blueprints, comprehensive reference workflows that accelerate AI application development and deployment and feature NVIDIA acceleration libraries, software development kits and NIM microservices for AI agents, digital twins and more.

Start Now With NIM on AWS

Developers can deploy NVIDIA NIM microservices on AWS according to their unique needs and requirements. By doing so, developers and enterprises can achieve high-performance AI with NVIDIA-optimized inference containers across various AWS services.

Visit the NVIDIA API catalog to try out over 100 different NIM-optimized models, and request either a developer license or 90-day NVIDIA AI Enterprise trial license to get started deploying the microservices on AWS services. Developers can also explore NIM microservices in the AWS Marketplace, Amazon Bedrock Marketplace or Amazon SageMaker JumpStart.

See notice regarding software product information.

Read More

Expanding what’s possible for developers and enterprises in the cloud, NVIDIA and Amazon Web Services are converging at AWS re:Invent in Las Vegas this week to showcase new solutions designed to accelerate AI and robotics breakthroughs and simplify research in quantum computing development.

AWS re:Invent is a conference for the global cloud-computing community packed with keynotes and more than 2,000 technical sessions.

Announcement highlights include the availability of NVIDIA DGX Cloud on AWS and enhanced AI, quantum computing and robotics tools.

NVIDIA DGX Cloud on AWS for AI at Scale

The NVIDIA DGX Cloud AI computing platform is now available through AWS Marketplace Private Offers, offering a high-performance, fully managed solution for enterprises to train and customize AI models.

DGX Cloud offers flexible terms, a fully managed and optimized platform, and direct access to NVIDIA experts to help businesses scale their AI capabilities quickly.

Early adopter Leonardo.ai, part of the Canva family, is already using DGX Cloud on AWS to develop advanced design tools.

AWS Liquid-Cooled Data Centers With NVIDIA Blackwell

Newer AI servers benefit from liquid cooling to cool high-density compute chips more efficiently for better performance and energy efficiency. AWS has developed solutions that provide configurable liquid-to-chip cooling across its data centers.

The cooling solution announced today will seamlessly integrate air- and liquid-cooling capabilities for the most powerful rack-scale AI supercomputing systems like NVIDIA GB200 NVL72, as well as AWS’ network switches and storage servers.

This flexible, multimodal cooling design provides maximum performance and efficiency for running AI models and will be used for the next-generation NVIDIA Blackwell platform.

Blackwell will be the foundation of Amazon EC2 P6 instances, DGX Cloud on AWS and Project Ceiba.

NVIDIA Advances Physical AI With Accelerated Robotics Simulation on AWS

NVIDIA is also expanding the reach of NVIDIA Omniverse on AWS with NVIDIA Isaac Sim, now running on high-performance Amazon EC2 G6e instances accelerated by NVIDIA L40S GPUs.

Available now, this reference application built on NVIDIA Omniverse enables developers to simulate and test AI-driven robots in physically based virtual environments.

One of the many workflows enabled by Isaac Sim is synthetic data generation. This pipeline is now further accelerated with the infusion of OpenUSD NIM microservices, from scene creation to data augmentation.

Robotics companies such as Aescape, Cohesive Robotics, Cobot, Field AI, Standard Bots, Swiss Mile and Vention are using Isaac Sim to simulate and validate the performance of their robots prior to deployment.

In addition, Rendered.ai, SoftServe and Tata Consultancy Services are using the synthetic data generation capabilities of Omniverse Replicator and Isaac Sim to bootstrap perception AI models that power various robotics applications.

NVIDIA BioNeMo on AWS for Advanced AI-Based Drug Discovery

NVIDIA BioNeMo NIM microservices and AI Blueprints, developed to advance drug discovery, are now integrated into AWS HealthOmics, a fully managed biological data compute and storage service designed to accelerate scientific breakthroughs in clinical diagnostics and drug discovery.

This collaboration gives researchers access to AI models and scalable cloud infrastructure tailored to drug discovery workflows. Several biotech companies already use NVIDIA BioNeMo on AWS to drive their research and development pipelines.

For example, A-Alpha Bio, a biotechnology company based in Seattle, recently published a study in biorxiv describing a collaborative effort with NVIDIA and AWS to develop and deploy an antibody AI model called AlphaBind.

Using AlphaBind via the BioNeMo framework on Amazon EC2 P5 instances equipped with NVIDIA H100 Tensor Core GPUs, A-Alpha Bio achieved a 12x increase in inference speed and processed over 108 million inference calls in two months.

Additionally, SoftServe today launched Drug Discovery, its generative AI solution built with NVIDIA Blueprints, to enable computer-aided drug discovery and efficient drug development. This solution is set to deliver faster workflows and will soon be available in AWS Marketplace.

Real-Time AI Blueprints: Ready-to-Deploy Options for Video, Cybersecurity and More

NVIDIA’s latest AI Blueprints are available for instant deployment on AWS, making real-time applications like vulnerability analysis for container security, and video search and summarization agents readily accessible.

Developers can easily integrate these blueprints into existing workflows to speed deployments.

Developers and enterprises can use the NVIDIA AI Blueprint for video search and summarization to build visual AI agents that can analyze real-time or archived videos to answer user questions, generate summaries and enable alerts for specific scenarios.

AWS collaborated with NVIDIA to provide a reference architecture applying the NVIDIA AI Blueprint for vulnerability analysis to augment early security patching in continuous integration pipelines on AWS cloud-native services.

NVIDIA CUDA-Q on Amazon Braket: Quantum Computing Made Practical

NVIDIA CUDA-Q is now integrated with Amazon Braket to streamline quantum computing development. CUDA-Q users can  use Amazon Braket’s quantum processors, while Braket users can tap CUDA-Q’s GPU-accelerated workflows for development and simulation.

The CUDA-Q platform allows developers to build hybrid quantum-classical applications and run them on many different types of quantum processors, simulated and physical.

Now preinstalled on Amazon Braket, CUDA-Q provides a seamless development platform for hybrid quantum-classical applications, unlocking new potential in quantum research.

Enterprise Platform Providers and Consulting Leaders Advance AI With NVIDIA on AWS

Leading software platforms and global system integrators are helping enterprises rapidly scale generative AI applications built with NVIDIA AI on AWS to drive innovation across industries.

Cloudera is using NVIDIA AI on AWS to enhance its new AI inference solution, helping Mercy Corps improve the precision and effectiveness of its aid distribution technology.

Cohesity has integrated NVIDIA NeMo Retriever microservices in its generative AI-powered conversational search assistant, Cohesity Gaia, to improve the recall performance of retrieval-augmented generation. Cohesity customers running on AWS can take advantage of the NeMo Retriever integration within Gaia.

DataStax announced that Wikimedia Deutschland is applying the DataStax AI Platform to make Wikidata available to developers as an embedded vectorized database. The Datastax AI Platform is built with NVIDIA NeMo Retriever and NIM microservices, and available on AWS.

Deloitte’s C-Suite AI now supports NVIDIA AI Enterprise software, including NVIDIA NIM microservices and NVIDIA NeMo for CFO-specific use cases, including financial statement analysis, scenario modeling and market analysis.

RAPIDS Quick Start Notebooks Now Available on Amazon EMR

NVIDIA and AWS are also speeding data science and data analytics workloads with the RAPIDS Accelerator for Apache Spark, which accelerates analytics and machine learning workloads with no code change and reduces data processing costs by up to 80%.

Quick Start notebooks for RAPIDS Accelerator for Apache Spark are now available on Amazon EMR, Amazon EC2 and Amazon EMR on EKS. These offer a simple way to qualify Spark jobs tuned to maximize the performance of RAPIDS on GPUs, all within AWS EMR.

NVIDIA and AWS Power the Next Generation of Industrial Edge Systems

The NVIDIA IGX Orin and Jetson Orin platforms now integrate seamlessly with AWS IoT Greengrass to streamline  the deployment and running of AI models at the edge and to efficiently manage fleets of connected devices at scale. This combination enhances scalability and simplifies the deployment process for industrial and robotics applications.

Developers can now tap into NVIDIA’s advanced edge computing power with AWS’ purpose-built IoT services, creating a secure, scalable environment for autonomous machines and smart sensors. A guide for getting started, authored by AWS, is now available to support developers putting these capabilities to work.

The integration underscores NVIDIA’s work in advancing enterprise-ready industrial edge systems to enable rapid, intelligent operations in real-world applications.

Catch more of NVIDIA’s work at AWS: re:Invent 2024 through live demos, technical sessions and hands-on labs. 

See notice regarding software product information.

Read More

Field AI is building robot brains that enable robots to autonomously manage a wide range of industrial processes. Vention creates pretrained skills to ease development of robotic tasks. And Cobot offers Proxie, an AI-powered cobot designed to handle material movement and adapt to dynamic environments, working seamlessly alongside humans.

These leading robotics startups are all making advances using NVIDIA Isaac Sim on Amazon Web Services. Isaac Sim is a reference application built on NVIDIA Omniverse for developers to simulate and test AI-driven robots in physically based virtual environments.

NVIDIA announced at AWS re:Invent today that Isaac Sim now runs on Amazon Elastic Cloud Computing (EC2) G6e instances accelerated by NVIDIA L40S GPUs. And with NVIDIA OSMO, a cloud-native orchestration platform, developers can easily manage their complex robotics workflows across their AWS computing infrastructure.

This combination of NVIDIA-accelerated hardware and software — available on the cloud — allows teams of any size to scale their physical AI workflows.

Physical AI describes AI models that can understand and interact with the physical world. It embodies the next wave of autonomous machines and robots, such as self-driving cars, industrial manipulators, mobile robots, humanoids and even robot-run infrastructure like factories and warehouses.

With physical AI, developers are embracing a three computer solution for training, simulation and inference to make breakthroughs.

Yet physical AI for robotics systems requires robust training datasets to achieve precision inference in deployment. Developing such datasets, however, and testing them in real situations can be impractical and costly.

Simulation offers an answer, as it can significantly accelerate the training, testing and deployment of AI-driven robots.

Harnessing L40S GPUs in the Cloud to Scale Robotics Simulation and Training

Simulation is used to verify, validate and optimize robot designs as well as the systems and their algorithms before deployment. Simulation can also optimize facility and system designs before construction or remodeling starts for maximum efficiencies, reducing costly manufacturing change orders.

Amazon EC2 G6e instances accelerated by NVIDIA L40S GPUs provide a 2x performance gain over the prior architecture, while allowing the flexibility to scale as scene and simulation complexity grows. The instances are used to train many computer vision models that power AI-driven robots. This means the same instances can be extended for various tasks, from data generation to simulation to model training.

Using NVIDIA OSMO in the cloud allows teams to orchestrate and scale complex ‌robotics development workflows across distributed computing resources, whether on premises or in the AWS cloud.

Isaac Sim provides access to the latest robotics simulation capabilities and the cloud, fostering collaboration. One of the critical workflows is generating synthetic data for perception model training.

Using a reference workflow that combines NVIDIA Omniverse Replicator, a framework for building custom synthetic data generation (SDG) pipelines and a core extension of Isaac Sim, with NVIDIA NIM microservices, developers can build generative AI-enabled SDG pipelines.

These include the USD Code NIM microservice for generating Python USD code and answering OpenUSD queries, and the USD Search NIM microservice for exploring OpenUSD assets using natural language or image inputs. The Edify 360 HDRi NIM microservice generates 360-degree environment maps, while the Edify 3D NIM microservice creates ready-to-edit 3D assets from text or image prompts. This eases the synthetic data generation process by reducing many tedious and manual steps, from asset creation to image augmentation, using the power of generative AI.

Rendered.ai’s synthetic data engineering platform integrated with Omniverse Replicator enables companies to generate synthetic data for computer vision models used in industries from security and intelligence to manufacturing and agriculture.

SoftServe, an IT consulting and digital services provider, uses Isaac Sim to generate synthetic data and validate robots used in vertical farming with Pfeifer & Langen, a leading European food producer.

Tata Consultancy Services is building custom synthetic data generation pipelines to power its Mobility AI suite to address automotive and autonomous use cases by simulating real-world scenarios. Its applications include defect detection, end-of-line quality inspection and hazard avoidance.

Learning to Be Robots in Simulation

While Isaac Sim enables developers to test and validate robots in physically accurate simulation, Isaac Lab, an open-source robot learning framework built on Isaac Sim, provides a virtual playground for building robot policies that can run on AWS Batch.

Because these simulations are repeatable, developers can easily troubleshoot and reduce the number of cycles required for validation and testing.

Several robotics developers are embracing NVIDIA Isaac on AWS to develop physical AI, such as:

• Aescape’s robots are able to provide precision-tailored massages by accurately modeling and tuning onboard sensors in Isaac Sim.
• Cobot has used Isaac Sim with its AI-powered cobot, Proxie, to optimize logistics in warehouses, hospitals, manufacturing sites, and more.
• Cohesive Robotics has integrated Isaac Sim into its software framework called Argus OS for developing and deploying robotic workcells used in high-mix manufacturing environments.
• Field AI, a builder of robot foundation models, uses Isaac Sim and Isaac Lab to evaluate the performance of its models in complex, unstructured environments across industries such as construction, manufacturing, oil and gas, mining and more.
• Standard Bots is simulating and validating the performance of its R01 robot used in manufacturing and machining setup.
• Swiss Mile is using Isaac Sim and Isaac Lab for robot learning so that wheeled quadruped robots can perform tasks autonomously with new levels of efficiency in factories and warehouses.
• Vention, which offers a full-stack cloud-based automation platform, is harnessing Isaac Sim for developing and testing new capabilities for robot cells used by small to medium-size manufacturers.

Learn more about Isaac Sim 4.2, now available on Amazon EC2 G6e instances powered by NVIDIA L40S GPUs on AWS Marketplace.

Read More

As generative AI continues to grow, implementing and managing the right infrastructure becomes even more critical to ensure the secure and efficient development and deployment of AI-based solutions.

To meet these needs, NVIDIA has introduced two professional-level certifications that offer structured paths for infrastructure and operations practitioners to enhance and validate the skills needed to work effectively with advanced AI technologies.

The certification exams and recommended training to prepare for them are designed for network and system administrators, DevOps and MLOps engineers, and others who need to understand AI infrastructure and operations.

NVIDIA’s certification program equips professionals with skills in areas such as AI infrastructure, deep learning and accelerated computing to enhance their career prospects and give them a competitive edge in these high-demand fields.

Developed in collaboration with industry experts, the program features relevant content that emphasizes practical application alongside theoretical knowledge.

The NVIDIA-Certified Professional: AI Infrastructure certification is designed for practitioners seeking to showcase advanced skills in deploying AI infrastructure. Candidates must demonstrate expertise in GPU and DPU installation, hardware validation and system optimization for both AI and HPC workloads. The exam also tests proficiency in managing physical layers, configuring MIG, deploying the NVIDIA BlueField operating system, and integrating NVIDIA’s cloud-native stack with Docker and NVIDIA NGC.

To prepare for this professional-level certification, candidates are encouraged to attend the AI Infrastructure Professional Workshop. This hands-on training covers critical AI data center technologies, including compute platforms, GPU operations, networking, storage solutions and BlueField DPUs. The workshop is recommended for professionals aiming to elevate their AI infrastructure expertise.

The NVIDIA-Certified Professional: AI Operations certification is tailored for individuals seeking proficiency in managing and optimizing AI operations. The exam tests expertise in managing AI data centers, including the use of Kubernetes, Slurm, MIG, BCM, NGC containers, storage configuration and DPU services.

To prepare for this professional-level certification, candidates are encouraged to attend the AI Operations Professional Workshop, where they will gain hands-on experience in managing AI data centers, including compute platforms, networking, storage and GPU virtualization. The workshop also provides practical experience with NVIDIA AI software and solutions, including NGC containers and the NVIDIA AI Enterprise software suite, making it ideal for professionals looking to deepen their AI operations expertise.

Both of these professional-level certifications build upon the foundational knowledge covered in the NVIDIA-Certified Associate: AI Infrastructure and Operations certification.

Additional NVIDIA certifications include:

• NVIDIA-Certified Associate: Generative AI LLMs — validates skills in using generative AI and large language models
• NVIDIA-Certified Associate: Generative AI Multimodal — focuses on multimodal AI content creation

Saleh Hassan, an embedded software engineer at Two Six Technologies, successfully completed three NVIDIA certification exams at the NVIDIA AI Summit in Washington, D.C., earlier this year.

“The knowledge I gained has definitely made me a better developer when it comes to integrating AI,” said Hassan, who encourages others to pursue certifications as a key milestone for advancing their AI careers.

All NVIDIA certifications are part of a comprehensive learning path that offers foundational courses, advanced training and hands-on labs to thoroughly prepare candidates for real-world applications.

The certifications support individual career growth and organizations can use them to enhance workforce capabilities.

Explore the options on the NVIDIA Certification portal and sign up for NVIDIA’s monthly newsletter to stay updated on the latest offerings.

Read More