Putting More Tech to the Test, NVIDIA Certifies New Categories of Gen AI-Ready Systems

Putting More Tech to the Test, NVIDIA Certifies New Categories of Gen AI-Ready Systems

Fueled by generative AI, enterprises globally are creating “AI factories,” where data comes in and intelligence comes out.

Critical to this movement are validated systems and reference architectures that reduce the risk and time involved in deploying specialized infrastructure that can support complex, computationally intensive generative AI workloads.

At the COMPUTEX trade show, NVIDIA today announced the expansion of its NVIDIA-Certified Systems program, which designates leading partner systems as suited for AI and accelerated computing, so customers can confidently deploy these platforms from the data center to the edge.

Two new certification types are now included: NVIDIA-Certified Spectrum-X Ready systems for AI in the data center and NVIDIA-Certified IGX systems for AI at the edge.

Each NVIDIA-Certified System undergoes rigorous testing and is validated to provide enterprise-grade performance, manageability, security and scalability for NVIDIA AI Enterprise software workloads, including generative AI applications built with NVIDIA NIM inference microservices. The systems provide a trusted pathway to design and implement efficient, reliable infrastructure.

NVIDIA-Certified Spectrum-X Ready Systems

The world’s first Ethernet fabric built for AI, the NVIDIA Spectrum-X AI Ethernet platform combines the NVIDIA Spectrum-4 SN5000 Ethernet switch series, NVIDIA BlueField-3 SuperNICs and networking acceleration software to deliver 1.6x AI networking performance over traditional Ethernet fabrics.

NVIDIA-Certified Spectrum-X Ready servers will act as building blocks for high-performance AI computing clusters and support powerful NVIDIA Hopper architecture and NVIDIA L40S GPUs.

NVIDIA-Certified IGX Systems

NVIDIA IGX Orin is an enterprise-ready AI platform for the industrial edge and medical applications that features industrial-grade hardware, a production-grade software stack and long-term enterprise support. It includes the latest technologies in device security, remote provisioning and management, along with built-in extensions, to deliver high-performance AI and proactive safety for low-latency, real-time applications in such areas as medical diagnostics, manufacturing, industrial robotics, agriculture and more.

Expanding Partner Portfolio

Top NVIDIA ecosystem partners are set to achieve the new certifications.

ASUS, Dell Technologies, GIGABYTE, Hewlett Packard Enterprise, Ingrasys, Lenovo, QCT and Supermicro will soon offer NVIDIA-Certified Spectrum-X Ready systems.

And NVIDIA-Certified IGX systems will soon be available from ADLINK, Advantech, Aetina, Ahead, Cosmo Intelligent Medical Devices (a division of Cosmo Pharmaceuticals), Dedicated Computing, Leadtek, Onyx and YUAN.

Learn more about NVIDIA-Certified Systems and the latest generative AI technologies by joining NVIDIA at COMPUTEX.

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Leading Medical Centers in Taiwan Adopt NVIDIA Accelerated Computing to Advance Biomedical Research

Leading Medical Centers in Taiwan Adopt NVIDIA Accelerated Computing to Advance Biomedical Research

Taiwan’s leading medical centers — the National Health Research Institute (NHRI) and Chang Gung Memorial Hospital (CGMH) — are set to advance biomedical research and healthcare for patients.

The centers are embracing accelerated computing and generative AI for everything from imaging to enhancing patient care, from streamlining clinical workflows to drug discovery research.

“The use of AI in healthcare will fundamentally change the way we approach disease prevention and treatment,” said Dr. Hung-Yi Chiou, director of the Institute of Population Health Sciences (IPHS) at NHRI. “With AI’s ability to analyze vast amounts of data quickly and accurately, we can develop personalized medicine strategies and early intervention methods that were previously unattainable.”

“The future of AI in healthcare is incredibly promising,” said Dr. Wen-Jin Cherng at CGMH.

With the assistance of AI in smart healthcare, future diagnoses will become more accurate, treatment plans will have better predictions and patients will experience faster recovery, Dr. Cherng explained. And in complex analytical processes, AI can enable more efficient and cost-effective decision-making in healthcare, he added.

“The transformative potential of the NVIDIA Blackwell platform allows us to integrate advanced AI capabilities into our medical practices, enhancing patient care and streamlining clinical workflows like never before,” he said.

NHRI, the leading medical research institution in Taiwan, plays a crucial role in advancing public health through biomedical research and innovation. The integration of NVIDIA accelerated computing into its IT infrastructure marks a significant leap forward in the realm of AI-driven healthcare.

NHRI’s collaboration with NVIDIA also extends to the development of large language models tailored specifically for Taiwan’s healthcare needs.

“Traditional Chinese medical records and genomic data present unique challenges that require localized solutions,” said Dr. Feng-Chi Chen, deputy director of IPHS at the NHRI.

These challenges include the complexity of language variations and the need for precise genomic interpretations specific to Taiwan’s population, Dr. Chen explained.

“NVIDIA accelerated computing enables us to create these solutions, ensuring that our healthcare system remains at the cutting edge of medical research,” he said.

CGMH, one of the largest healthcare systems in Taiwan, operates a network of 10 hospitals with a combined inpatient capacity of over 11,000 beds. It also serves millions of people in outpatient services. It’s a cornerstone of Taiwan’s healthcare system, which is one of the most advanced in the world.

“With the computational power of Blackwell, we can expand our language model services to all hospitals under our umbrella, enhancing professional support, patient care and streamlining clinical workflows,” said Dr. Chang-Fu Kuo, director of the AI center at CGMH. “It addresses the needs of various medical disciplines and diverse patient populations, enabling healthcare professionals to focus on critical clinical tasks and ultimately improve patient outcomes.”

NHRI, CGMH Pioneering Medical AI

NHRI currently uses six NVIDIA DGX A100 systems for cloud and data center services, focusing on biomedical model training and genomic analysis.

By harnessing the power of NVIDIA accelerated computing, NHRI is also tackling pressing public health issues. One of its key projects involves using AI to predict the risk of chronic diseases such as diabetes and cardiovascular conditions by analyzing a multitude of genetic and environmental parameters.

“This level of analysis was previously unattainable due to computational constraints,” said Dr. Chen. “Now, with the power of NVIDIA accelerated computing, we will be able to offer more accurate risk assessments and preventative strategies.”

CGMH also has a diverse array of NVIDIA hardware, including NVIDIA H100, A100, and other Tensor Core GPUs, which it uses for medical imaging development and deployment. The foundation serves 46 models daily and intends to use Blackwell for LLM training and the deployment of service robots in hospitals.

Running these systems on premises and keeping the data within the hospital’s infrastructure are key to ensuring patient data privacy as well as faster data processing and reduced latency, said Dr. Chihung Lin, deputy director of the CGMH AI center.

These technologies may be used in various medical applications, including:

  • Clinical Decision Support System: Developed on premises to ensure patient data confidentiality and privacy, this system assists clinicians by providing access to up-to-date data and guidelines and using models to answer questions and prepare medical decisions.
  • Patient Interaction System: Allows patients to interact with a robot to get answers about their medication and medical conditions, reducing the burden on medical staff. Medical staff review the robot’s responses to ensure accuracy.
  • Medical Imaging: Enhances radiology and other imaging tasks using AI. This project is one of the most mature AI technologies in CGMH’s healthcare system.
  • Precision Medicine: Handles large-scale genomic data and transforms sequences into readable medical reports for doctors. Focused on building computational facilities to support whole genome and exome sequencing.
  • Expansion of AI Services: Aims to extend the language model services to all hospitals under CGMH’s umbrella, leveraging the computational capacity from the Blackwell platform to support this expansion.

Other applications include early detection of colorectal cancer via endoscopy, autoimmune disease screening through microscope images and kidney disease prediction using general imaging techniques.

NHRI and CGMH’s adoption of accelerated computing underscores the growing importance of AI and advanced computing in medical research and healthcare delivery.

With these tools, Taiwan is poised to make strides in improving patient outcomes and advancing biomedical science.

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Power Tool: Generative AI Tracks Typhoons, Tames Energy Use

Power Tool: Generative AI Tracks Typhoons, Tames Energy Use

Weather forecasters in Taiwan had their hair blown back when they saw a typhoon up close, created on a computer that slashed the time and energy needed for the job.

It’s a reaction that users in many fields are feeling as generative AI shows them how new levels of performance contribute to reductions in total cost of ownership.

Inside the AI of the Storm

Tracking a typhoon provided a great test case of generative AI’s prowess. The work traditionally begins with clusters of CPUs cranking on complex algorithms to create atmospheric models with a 25-kilometer resolution.

Enter CorrDiff, a generative AI model that’s part of NVIDIA Earth-2, a set of services and software for weather and climate research.

Using a class of diffusion models that power today’s text-to-image services, CorrDiff resolved the 25-km models to two kilometers 1,000x faster, using 3,000x less energy for a single inference than traditional methods.

CorrDiff Cuts Costs 50x, Energy Use 25x

CorrDiff shines on the NVIDIA AI platform, even when retraining the model once a year and using statistical groups of a thousand forecasts to boost the accuracy of predictions. Compared to traditional methods under these conditions, it slashes cost by 50x cost and energy use by 25x a year.

That means work that used to require nearly $3 million for a cluster of CPUs and the energy to run them can be done for about $60,000 on a single system with an NVIDIA H100 Tensor Core GPU. It’s a massive reduction that shows how generative AI and accelerated computing increases energy efficiency and lowers total cost of ownership.

The technology also helps forecasters see more precisely where a typhoon will land, potentially saving lives.

“NVIDIA’s CorrDiff generative AI model opens the door to the use of AI-generated kilometer-scale weather forecasts, enabling Taiwan to prepare better for typhoons,” said Hongey Chen, a director of Taiwan’s National Science and Technology Center for Disaster Reduction.

The Taiwan forecasters could save nearly a gigawatt-hour a year, using CorrDiff. Energy savings could balloon if the nearly 200 regional weather data centers around the world adopt the technology for more sustainable computing.

Companies that sell commercial forecasts are also adopting CorrDiff, attracted by its speed and savings.

Broad Horizons for Energy Efficiency

NVIDIA Earth-2 takes these capabilities to a planetary scale. It fuses AI, physics simulations and observed data to help countries and companies respond to global issues like climate change. That will help address the impacts of climate change, which is expected to cost a million lives and $1.7 trillion per year by 2050.

Accelerated computing and generative AI are bringing new levels of performance and energy efficiency to many applications. Explainers on green computing and why GPUs are great for AI provide more context and some examples.

Compare the costs and energy consumption of popular workloads running on an x86 CPU-based server versus an NVIDIA GPU server with this simple calculator. And watch Huang’s keynote address at COMPUTEX to get the big picture.

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‘Create a Data Flywheel With AI,’ NVIDIA CEO Jensen Huang Tells Attendees at Snowflake Summit

‘Create a Data Flywheel With AI,’ NVIDIA CEO Jensen Huang Tells Attendees at Snowflake Summit

AI gives every company an opportunity to turn its processes into a data flywheel, NVIDIA founder and CEO Jensen Huang told thousands of attendees Monday at the Snowflake Data Cloud Summit.

Companies need to “take all the most important processes they do, capture them in a data flywheel and turn that into the company’s AI to drive that flywheel even further,” said Huang, joining from Taipei a virtual fireside chat with Snowflake’s CEO Sridhar Ramaswamy in San Francisco.

The two executives described how the combination of the Snowflake AI Data Cloud and NVIDIA AI will simplify and accelerate enterprise AI.

“You want to jump on this train as fast as you can, don’t let it fly by because you can use it to transform your business or go into new businesses,” said Huang, the day after he gave a keynote kicking off COMPUTEX in Taiwan.

Snowflake Users Can Tap Into NVIDIA AI Enterprise

For example, businesses will be able to deploy Snowflake Arctic, an enterprise-focused large language model (LLM), in seconds using NVIDIA NIM inference microservices, part of the NVIDIA AI Enterprise software platform. 

Arctic was trained on NVIDIA H100 Tensor Core GPUs and is available on the NVIDIA API catalog, fully supported by NVIDIA TensorRT-LLM, software that accelerates generative AI inference.

The two companies also will integrate Snowflake Cortex AI and NVIDIA NeMo Retriever, so businesses can link their AI-powered applications to information sources, ensuring highly accurate results with retrieval-augmented generation (RAG).

Ramaswamy gave examples of generative AI applications developed with the NVIDIA NeMo framework and Snowpark Container Services that will be available on Snowflake Marketplace for use by thousands of Snowflake’s customers.

“NVIDIA’s industry-leading accelerated computing is game changing for our customers and our own research team that used it to create the state-of-the-art Artic model for our customers,” said Ramaswamy.

To learn more, watch NVIDIA GTC on-demand sessions presented by Snowflake on how to build chatbots with a RAG architecture and how to leverage LLMs for life sciences.

 

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