“Generative AI is reshaping industries and opening new opportunities for innovation and growth,” NVIDIA founder and CEO Jensen Huang said in an address ahead of this week’s COMPUTEX technology conference in Taipei.

“Today, we’re at the cusp of a major shift in computing,” Huang told the audience, clad in his trademark black leather jacket. “The intersection of AI and accelerated computing is set to redefine the future.”

Huang spoke ahead of one of the world’s premier technology conferences to an audience of more than 6,500 industry leaders, press, entrepreneurs, gamers, creators and AI enthusiasts gathered at the glass-domed National Taiwan University Sports Center set in the verdant heart of Taipei.

The theme: NVIDIA accelerated platforms are in full production, whether through AI PCs and consumer devices featuring a host of NVIDIA RTX-powered capabilities or enterprises building and deploying AI factories with NVIDIA’s full-stack computing platform.

“The future of computing is accelerated,” Huang said. “With our innovations in AI and accelerated computing, we’re pushing the boundaries of what’s possible and driving the next wave of technological advancement.”
 

‘One-Year Rhythm’

More’s coming, with Huang revealing a roadmap for new semiconductors that will arrive on a one-year rhythm. Revealed for the first time, the Rubin platform will succeed the upcoming Blackwell platform, featuring new GPUs, a new Arm-based CPU — Vera — and advanced networking with NVLink 6, CX9 SuperNIC and the X1600 converged InfiniBand/Ethernet switch.

“Our company has a one-year rhythm. Our basic philosophy is very simple: build the entire data center scale, disaggregate and sell to you parts on a one-year rhythm, and push everything to technology limits,” Huang explained.

NVIDIA’s creative team used AI tools from members of the NVIDIA Inception startup program, built on NVIDIA NIM and NVIDIA’s accelerated computing, to create the COMPUTEX keynote. Packed with demos, this showcase highlighted these innovative tools and the transformative impact of NVIDIA’s technology.

‘Accelerated Computing Is Sustainable Computing’

NVIDIA is driving down the cost of turning data into intelligence, Huang explained as he began his talk.

“Accelerated computing is sustainable computing,” he emphasized, outlining how the combination of GPUs and CPUs can deliver up to a 100x speedup while only increasing power consumption by a factor of three, achieving 25x more performance per Watt over CPUs alone.

“The more you buy, the more you save,” Huang noted, highlighting this approach’s significant cost and energy savings.

Industry Joins NVIDIA to Build AI Factories to Power New Industrial Revolution

Leading computer manufacturers, particularly from Taiwan, the global IT hub, have embraced NVIDIA GPUs and networking solutions. Top companies include ASRock Rack, ASUS, GIGABYTE, Ingrasys, Inventec, Pegatron, QCT, Supermicro, Wistron and Wiwynn, which are creating cloud, on-premises and edge AI systems.

The NVIDIA MGX modular reference design platform now supports Blackwell, including the GB200 NVL2 platform, designed for optimal performance in large language model inference, retrieval-augmented generation and data processing.

AMD and Intel are supporting the MGX architecture with plans to deliver, for the first time, their own CPU host processor module designs. Any server system builder can use these reference designs to save development time while ensuring consistency in design and performance.

Next-Generation Networking with Spectrum-X

In networking, Huang unveiled plans for the annual release of Spectrum-X products to cater to the growing demand for high-performance Ethernet networking for AI.

NVIDIA Spectrum-X, the first Ethernet fabric built for AI, enhances network performance by 1.6x more than traditional Ethernet fabrics. It accelerates the processing, analysis and execution of AI workloads and, in turn, the development and deployment of AI solutions.

CoreWeave, GMO Internet Group, Lambda, Scaleway, STPX Global and Yotta are among the first AI cloud service providers embracing Spectrum-X to bring extreme networking performance to their AI infrastructures.

NVIDIA NIM to Transform Millions Into Gen AI Developers

With NVIDIA NIM, the world’s 28 million developers can now easily create generative AI applications. NIM — inference microservices that provide models as optimized containers — can be deployed on clouds, data centers or workstations.

NIM also enables enterprises to maximize their infrastructure investments. For example, running Meta Llama 3-8B in a NIM produces up to 3x more generative AI tokens on accelerated infrastructure than without NIM.

Nearly 200 technology partners — including Cadence, Cloudera, Cohesity, DataStax, NetApp, Scale AI, and Synopsys — are integrating NIM into their platforms to speed generative AI deployments for domain-specific applications, such as copilots, code assistants, digital human avatars and more. Hugging Face is now offering NIM — starting with Meta Llama 3.

“Today we just posted up in Hugging Face the Llama 3 fully optimized, it’s available there for you to try. You can even take it with you,” Huang said. “So you could run it in the cloud, run it in any cloud, download this container, put it into your own data center, and you can host it to make it available for your customers.”

NVIDIA Brings AI Assistants to Life With GeForce RTX AI PCs

NVIDIA’s RTX AI PCs, powered by RTX technologies, are set to revolutionize consumer experiences with over 200 RTX AI laptops and more than 500 AI-powered apps and games.

The RTX AI Toolkit and newly available PC-based NIM inference microservices for the NVIDIA ACE digital human platform underscore NVIDIA’s commitment to AI accessibility.

Project G-Assist, an RTX-powered AI assistant technology demo, was also announced, showcasing context-aware assistance for PC games and apps.

And Microsoft and NVIDIA are collaborating to help developers bring new generative AI capabilities to their Windows native and web apps with easy API access to RTX-accelerated SLMs that enable RAG capabilities that run on-device as part of Windows Copilot Runtime.

NVIDIA Robotics Adopted by Industry Leaders

NVIDIA is spearheading the $50 trillion industrial digitization shift, with sectors embracing autonomous operations and digital twins — virtual models that enhance efficiency and cut costs. Through its Developer Program, NVIDIA offers access to NIM, fostering AI innovation.

Taiwanese manufacturers are transforming their factories using NVIDIA’s technology, with Huang showcasing Foxconn’s use of NVIDIA Omniverse, Isaac and Metropolis to create digital twins, combining vision AI and robot development tools for enhanced robotic facilities.

“The next wave of AI is physical AI. AI that understands the laws of physics, AI that can work among us,” Huang said, emphasizing the importance of robotics and AI in future developments.

The NVIDIA Isaac platform provides a robust toolkit for developers to build AI robots, including AMRs, industrial arms and humanoids, powered by AI models and supercomputers like Jetson Orin and Thor.

“Robotics is here. Physical AI is here. This is not science fiction, and it’s being used all over Taiwan. It’s just really, really exciting,” Huang added.

Global electronics giants are integrating NVIDIA’s autonomous robotics into their factories, leveraging simulation in Omniverse to test and validate this new wave of AI for the physical world. This includes over 5 million preprogrammed robots worldwide.

“All the factories will be robotic. The factories will orchestrate robots, and those robots will be building products that are robotic,” Huang explained.

Huang emphasized NVIDIA Isaac’s role in boosting factory and warehouse efficiency, with global leaders like BYD Electronics, Siemens, Teradyne Robotics and Intrinsic adopting its advanced libraries and AI models.

NVIDIA AI Enterprise on the IGX platform, with partners like ADLINK, Advantech and ONYX, delivers edge AI solutions meeting strict regulatory standards, essential for medical technology and other industries.

Huang ended his keynote on the same note he began it on, paying tribute to Taiwan and NVIDIA’s many partners there. “Thank you,” Huang said. “I love you guys.”

Building on more than a dozen years of stacking wins at the COMPUTEX trade show’s annual Best Choice Awards, NVIDIA was today honored with BCAs for its latest technologies.

The NVIDIA GH200 Grace Hopper Superchip won the Computer and System Category Award; the NVIDIA Spectrum-X AI Ethernet networking platform won the Networking and Communication Category Award; and the NVIDIA AI Enterprise software platform won a Golden Award.

The awards — judged on the functionality, innovation and market potential of products exhibited at the leading computer and technology expo — were announced ahead of the show, which runs from June 4-7, in Taipei.

NVIDIA founder and CEO Jensen Huang will deliver a COMPUTEX keynote address on Sunday, June 2, at 7 p.m. Taiwan time, at the NTU Sports Center and online.

NVIDIA AI Enterprise Takes Gold

NVIDIA AI Enterprise — a cloud-native software platform that streamlines the development and deployment of copilots and other generative AI applications — won a Golden Award.

The platform lifts the burden of maintaining and securing complex AI software, so businesses can focus on building and harnessing the technology’s game-changing insights.

Microservices that come with NVIDIA AI Enterprise — including NVIDIA NIM and NVIDIA CUDA-X — optimize model performance and run anywhere with enterprise-grade security, support and stability, offering users a smooth transition from prototype to production.

Plus, the platform’s ability to improve AI performance results in better overall utilization of computing resources. This means companies using NVIDIA AI Enterprise need fewer servers to support the same workloads, greatly reducing their energy costs and data center footprint.

More BCA Wins for NVIDIA Technologies

NVIDIA GH200 and Spectrum-X were named best in their respective categories.

The NVIDIA GH200 Grace Hopper Superchip is the world’s first truly heterogeneous accelerated platform for AI and high-performance computing workloads. It combines the power-efficient NVIDIA Grace CPU with an NVIDIA Hopper architecture-based GPU over a high-bandwidth 900GB/s coherent NVIDIA NVLink chip-to-chip interconnect.

The superchip — shipping worldwide and powering more than 40 AI supercomputers across global research centers, system makers and cloud providers — supercharges scientific innovation with accelerated computing and scale-out solutions for AI inference, large language models, recommenders, vector databases, HPC applications and more.

The Spectrum-X platform, featuring NVIDIA Spectrum SN5600 switches and NVIDIA BlueField-3 SuperNICs, is the world’s first Ethernet fabric built for AI, accelerating generative AI network performance 1.6x over traditional Ethernet fabrics.

It can serve as the backend AI fabric for any AI cloud or large enterprise deployment, and is available from major server manufacturers as part of the full NVIDIA AI stack.

NVIDIA Partners Recognized

Other BCA winners include NVIDIA partners Acer, ASUS, MSI and YUAN, which were given Golden Awards for their respective laptops, gaming motherboards and smart-city applications — all powered by NVIDIA technologies, such as NVIDIA GeForce RTX 4090 GPUs, the NVIDIA Studio platform for creative workflows and the NVIDIA Jetson platform for edge AI and robotics.

ASUS also won a Computer and System Category Award, while MSI won a Gaming and Entertainment Category Award.

Learn more about the latest generative AI, HPC and networking technologies by joining NVIDIA at COMPUTEX.

Making breakthroughs in artificial intelligence these days requires huge amounts of computing power. In January, Meta CEO Mark Zuckerberg announced that by the end of this year, the company will have installed 350,000 Nvidia GPUs—the specialized computer chips used to train AI models—to power its AI research.

As a data-center network engineer with Meta’s network infrastructure team, Susana Contrera is playing a leading role in this unprecedented technology rollout. Her job is about “bringing designs to life,” she says. Contrera and her colleagues take high-level plans for the company’s AI infrastructure and turn those blueprints into reality by working out how to wire, power, cool, and house the GPUs in the company’s data centers.

Susana Contrera

Employer:

Meta

Occupation:

Data-center network engineer

Education:

Bachelor’s degree in telecommunications engineering, Andrés Bello Catholic University in Caracas, Venezuela

Contrera, who now works remotely from Florida, has been at Meta since 2013, spending most of that time helping to build the computer systems that support its social media networks, including Facebook and Instagram. But she says that AI infrastructure has become a growing priority, particularly in the past two years, and represents an entirely new challenge. Not only is Meta building some of the world’s first AI supercomputers, it is racing against other companies like Google and OpenAI to be the first to make breakthroughs.

“We are sitting right at the forefront of the technology,” Contrera says. “It’s super challenging, but it’s also super interesting, because you see all these people pushing the boundaries of what we thought we could do.”

Cisco Certification Opened Doors

Growing up in Caracas, Venezuela, Contrera says her first introduction to technology came from playing video games with her older brother. But she decided to pursue a career in engineering because of her parents, who were small-business owners.

“They were always telling me how technology was going to be a game changer in the future, and how a career in engineering could open many doors,” she says.

She enrolled at Andrés Bello Catholic University in Caracas in 2001 to study telecommunications engineering. In her final year, she signed up for the training and certification program to become a Cisco Certified Network Associate. The program covered topics such as the fundamentals of networking and security, IP services, and automation and programmability.

The certificate opened the door to her first job in 2006—managing the computer network of a business-process outsourcing company, Atento, in Caracas.

“Getting your hands dirty can give you a lot of perspective.”

“It was a very large enterprise network that had just the right amount of complexity for a very small team,” she says. “That gave me a lot of freedom to put my knowledge into practice.”

At the time, Venezuela was going through a period of political unrest. Contrera says she didn’t see a future for herself in the country, so she decided to leave for Europe.

She enrolled in a master’s degree program in project management in 2009 at Spain’s Pontifical University of Salamanca, continuing to collect additional certifications through Cisco in her free time. In 2010, partway through the program, she left for a job as a support engineer at the Madrid-based law firm Ecija, which provides legal advice to technology, media, and telecommunications companies. Following that with a stint as a network engineer at Amazon’s facility in Dublin from 2011 to 2013, she then joined Meta and “the rest is history,” she says.

Starting From the Edge Network

Contrera first joined Meta as a network deployment engineer, helping build the company’s “edge” network. In this type of network design, user requests go out to small edge servers dotted around the world instead of to Meta’s main data centers. Edge systems can deal with requests faster and reduce the load on the company’s main computers.

After several years traveling around Europe setting up this infrastructure, she took a managerial position in 2016. But after a couple of years she decided to return to a hands-on role at the company.

“I missed the satisfaction that you get when you’re part of a project, and you can clearly see the impact of solving a complex technical problem,” she says.

Because of the rapid growth of Meta’s services, her work primarily involved scaling up the capacity of its data centers as quickly as possible and boosting the efficiency with which data flowed through the network. But the work she is doing today to build out Meta’s AI infrastructure presents very different challenges, she says.

Designing Data Centers for AI

Training Meta’s largest AI models involves coordinating computation over large numbers of GPUs split into clusters. These clusters are often housed in different facilities, often in distant cities. It’s crucial that messages passing back and forth have very low latency and are lossless—in other words, they move fast and don’t drop any information.

Building data centers that can meet these requirements first involves Meta’s network engineering team deciding what kind of hardware should be used and how it needs to be connected.

“They have to think about how those clusters look from a logical perspective,” Contrera says.

Then Contrera and other members of the network infrastructure team take this plan and figure out how to fit it into Meta’s existing data centers. They consider how much space the hardware needs, how much power and cooling it will require, and how to adapt the communications systems to support the additional data traffic it will generate. Crucially, this AI hardware sits in the same facilities as the rest of Meta’s computing hardware, so the engineers have to make sure it doesn’t take resources away from other important services.

“We help translate these ideas into the real world,” Contrera says. “And we have to make sure they fit not only today, but they also make sense for the long-term plans of how we are scaling our infrastructure.”

Working on a Transformative Technology

Planning for the future is particularly challenging when it comes to AI, Contrera says, because the field is moving so quickly.

“It’s not like there is a road map of how AI is going to look in the next five years,” she says. “So we sometimes have to adapt quickly to changes.”

With today’s heated competition among companies to be the first to make AI advances, there is a lot of pressure to get the AI computing infrastructure up and running. This makes the work much more demanding, she says, but it’s also energizing to see the entire company rallying around this goal.

While she sometimes gets lost in the day-to-day of the job, she loves working on a potentially transformative technology. “It’s pretty exciting to see the possibilities and to know that we are a tiny piece of that big puzzle,” she says.

Hands-on Data Center Experience

For those interested in becoming a network engineer, Contrera says the certification programs run by companies like Cisco are useful. But she says it’s also important not to focus just on simply ticking boxes or rushing through courses just to earn credentials. “Take your time to understand the topics because that’s where the value is,” she says.

It’s good to get some experience working in data centers on infrastructure deployment, she says, because “getting your hands dirty can give you a lot of perspective.” And increasingly, coding can be another useful skill to develop to complement more traditional network engineering capabilities.

Mainly, she says, just “enjoy the ride” because networking can be a truly fascinating topic once you delve in. “There’s this orchestra of protocols and different technologies playing together and interacting,” she says. “I think that’s beautiful.”