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New Amazon EC2 P5 Instances Deployed in EC2 UltraClusters Are Fully Optimized to Harness NVIDIA Hopper GPUs for Accelerating Generative AI Training and Inference at Massive Scale

GTCAmazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), and NVIDIA (NASDAQ: NVDA) today announced a multi-part collaboration focused on building out the world's most scalable, on-demand artificial intelligence (AI) infrastructure optimized for training increasingly complex large language models (LLMs) and developing generative AI applications.

The joint work features next-generation Amazon Elastic Compute Cloud (Amazon EC2) P5 instances powered by NVIDIA H100 Tensor Core GPUs and AWSs state-of-the-art networking and scalability that will deliver up to 20 exaFLOPS of compute performance for building and training the largest deep learning models. P5 instances will be the first GPU-based instance to take advantage of AWSs second-generation Elastic Fabric Adapter (EFA) networking, which provides 3,200 Gbps of low-latency, high bandwidth networking throughput, enabling customers to scale up to 20,000 H100 GPUs in EC2 UltraClusters for on-demand access to supercomputer-class performance for AI.

AWS and NVIDIA have collaborated for more than 12 years to deliver large-scale, cost-effective GPU-based solutions on demand for various applications such as AI/ML, graphics, gaming, and HPC, said Adam Selipsky, CEO at AWS. AWS has unmatched experience delivering GPU-based instances that have pushed the scalability envelope with each successive generation, with many customers scaling machine learning training workloads to more than 10,000 GPUs today. With second-generation EFA, customers will be able to scale their P5 instances to over 20,000 NVIDIA H100 GPUs, bringing supercomputer capabilities on demand to customers ranging from startups to large enterprises.

Accelerated computing and AI have arrived, and just in time. Accelerated computing provides step-function speed-ups while driving down cost and power as enterprises strive to do more with less. Generative AI has awakened companies to reimagine their products and business models and to be the disruptor and not the disrupted, said Jensen Huang, founder and CEO of NVIDIA. AWS is a long-time partner and was the first cloud service provider to offer NVIDIA GPUs. We are thrilled to combine our expertise, scale, and reach to help customers harness accelerated computing and generative AI to engage the enormous opportunities ahead.

New Supercomputing ClustersNew P5 instances are built on more than a decade of collaboration between AWS and NVIDIA delivering the AI and HPC infrastructure and build on four previous collaborations across P2, P3, P3dn, and P4d(e) instances. P5 instances are the fifth generation of AWS offerings powered by NVIDIA GPUs and come almost 13 years after its initial deployment of NVIDIA GPUs, beginning with CG1 instances.

P5 instances are ideal for training and running inference for increasingly complex LLMs and computer vision models behind the most-demanding and compute-intensive generative AI applications, including question answering, code generation, video and image generation, speech recognition, and more.

Specifically built for both enterprises and startups racing to bring AI-fueled innovation to market in a scalable and secure way, P5 instances feature eight NVIDIA H100 GPUs capable of 16 petaFLOPs of mixed-precision performance, 640 GB of high-bandwidth memory, and 3,200 Gbps networking connectivity (8x more than the previous generation) in a single EC2 instance. The increased performance of P5 instances accelerates the time-to-train machine learning (ML) models by up to 6x (reducing training time from days to hours), and the additional GPU memory helps customers train larger, more complex models. P5 instances are expected to lower the cost to train ML models by up to 40% over the previous generation, providing customers greater efficiency over less flexible cloud offerings or expensive on-premises systems.

Amazon EC2 P5 instances are deployed in hyperscale clusters called EC2 UltraClusters that are comprised of the highest performance compute, networking, and storage in the cloud. Each EC2 UltraCluster is one of the most powerful supercomputers in the world, enabling customers to run their most complex multi-node ML training and distributed HPC workloads. They feature petabit-scale non-blocking networking, powered by AWS EFA, a network interface for Amazon EC2 instances that enables customers to run applications requiring high levels of inter-node communications at scale on AWS. EFAs custom-built operating system (OS) bypass hardware interface and integration with NVIDIA GPUDirect RDMA enhances the performance of inter-instance communications by lowering latency and increasing bandwidth utilization, which is critical to scaling training of deep learning models across hundreds of P5 nodes. With P5 instances and EFA, ML applications can use NVIDIA Collective Communications Library (NCCL) to scale up to 20,000 H100 GPUs. As a result, customers get the application performance of on-premises HPC clusters with the on-demand elasticity and flexibility of AWS. On top of these cutting-edge computing capabilities, customers can use the industrys broadest and deepest portfolio of services such as Amazon S3 for object storage, Amazon FSx for high-performance file systems, and Amazon SageMaker for building, training, and deploying deep learning applications. P5 instances will be available in the coming weeks in limited preview. To request access, visit https://pages.awscloud.com/EC2-P5-Interest.html.

With the new EC2 P5 instances, customers like Anthropic, Cohere, Hugging Face, Pinterest, and Stability AI will be able to build and train the largest ML models at scale. The collaboration through additional generations of EC2 instances will help startups, enterprises, and researchers seamlessly scale to meet their ML needs.

Anthropic builds reliable, interpretable, and steerable AI systems that will have many opportunities to create value commercially and for public benefit. At Anthropic, we are working to build reliable, interpretable, and steerable AI systems. While the large, general AI systems of today can have significant benefits, they can also be unpredictable, unreliable, and opaque. Our goal is to make progress on these issues and deploy systems that people find useful, said Tom Brown, co-founder of Anthropic. Our organization is one of the few in the world that is building foundational models in deep learning research. These models are highly complex, and to develop and train these cutting-edge models, we need to distribute them efficiently across large clusters of GPUs. We are using Amazon EC2 P4 instances extensively today, and we are excited about the upcoming launch of P5 instances. We expect them to deliver substantial price-performance benefits over P4d instances, and theyll be available at the massive scale required for building next-generation large language models and related products.

Cohere, a leading pioneer in language AI, empowers every developer and enterprise to build incredible products with world-leading natural language processing (NLP) technology while keeping their data private and secure. Cohere leads the charge in helping every enterprise harness the power of language AI to explore, generate, search for, and act upon information in a natural and intuitive manner, deploying across multiple cloud platforms in the data environment that works best for each customer, said Aidan Gomez, CEO at Cohere. NVIDIA H100-powered Amazon EC2 P5 instances will unleash the ability of businesses to create, grow, and scale faster with its computing power combined with Coheres state-of-the-art LLM and generative AI capabilities.

Hugging Face is on a mission to democratize good machine learning. As the fastest growing open source community for machine learning, we now provide over 150,000 pre-trained models and 25,000 datasets on our platform for NLP, computer vision, biology, reinforcement learning, and more, said Julien Chaumond, CTO and co-founder at Hugging Face. With significant advances in large language models and generative AI, were working with AWS to build and contribute the open source models of tomorrow. Were looking forward to using Amazon EC2 P5 instances via Amazon SageMaker at scale in UltraClusters with EFA to accelerate the delivery of new foundation AI models for everyone.

Today, more than 450 million people around the world use Pinterest as a visual inspiration platform to shop for products personalized to their taste, find ideas to do offline, and discover the most inspiring creators. We use deep learning extensively across our platform for use-cases such as labeling and categorizing billions of photos that are uploaded to our platform, and visual search that provides our users the ability to go from inspiration to action," said David Chaiken, Chief Architect at Pinterest. "We have built and deployed these use-cases by leveraging AWS GPU instances such as P3 and the latest P4d instances. We are looking forward to using Amazon EC2 P5 instances featuring H100 GPUs, EFA and Ultraclusters to accelerate our product development and bring new Empathetic AI-based experiences to our customers.

As the leader in multimodal, open-source AI model development and deployment, Stability AI collaborates with public- and private-sector partners to bring this next-generation infrastructure to a global audience. At Stability AI, our goal is to maximize the accessibility of modern AI to inspire global creativity and innovation, said Emad Mostaque, CEO of Stability AI. We initially partnered with AWS in 2021 to build Stable Diffusion, a latent text-to-image diffusion model, using Amazon EC2 P4d instances that we employed at scale to accelerate model training time from months to weeks. As we work on our next generation of open-source generative AI models and expand into new modalities, we are excited to use Amazon EC2 P5 instances in second-generation EC2 UltraClusters. We expect P5 instances will further improve our model training time by up to 4x, enabling us to deliver breakthrough AI more quickly and at a lower cost.

New Server Designs for Scalable, Efficient AILeading up to the release of H100, NVIDIA and AWS engineering teams with expertise in thermal, electrical, and mechanical fields have collaborated to design servers to harness GPUs to deliver AI at scale, with a focus on energy efficiency in AWS infrastructure. GPUs are typically 20x more energy efficient than CPUs for certain AI workloads, with the H100 up to 300x more efficient for LLMs than CPUs.

The joint work has included developing a system thermal design, integrated security and system management, security with the AWS Nitro hardware accelerated hypervisor, and NVIDIA GPUDirect optimizations for AWS custom-EFA network fabric.

Building on AWS and NVIDIAs work focused on server optimization, the companies have begun collaborating on future server designs to increase the scaling efficiency with subsequent-generation system designs, cooling technologies, and network scalability.

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By using machine learning and state-of-the-art supernova nucleosynthesis, a team of researchers have found the majority of observed second-generation stars in the universe were enriched by multiple supernovae, reports a new study in The Astrophysical Journal.

Nuclear astrophysics research has shown elements including and heavier than carbon in the universe are produced in stars. But the first stars, stars born soon after the Big Bang, did not contain such heavy elements, which astronomers call 'metals'. The next generation of stars contained only a small amount of heavy elements produced by the first stars. To understand the universe in its infancy, it requires researchers to study these metal-poor stars.

Luckily, these second-generation metal-poor stars are observed in our Milky Way Galaxy, and have been studied by a team of Affiliate Members of the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU) to close in on the physical properties of the first stars in the universe.

The team, led by Kavli IPMU Visiting Associate Scientist and The University of Tokyo Institute for Physics of Intelligence Assistant Professor Tilman Hartwig, including Visiting Associate Scientist and National Astronomical Observatory of Japan Assistant Professor Miho Ishigaki, Visiting Senior Scientist and University of Hertfordshire Professor Chiaki Kobayashi, Visiting Senior Scientist and National Astronomical Observatory of Japan Professor Nozomu Tominaga, and Visiting Senior Scientist and The University of Tokyo Professor Emeritus Ken'ichi Nomoto, used artificial intelligence to analyze elemental abundances in more than 450 extremely metal-poor stars observed to date. Based on the newly developed supervised machine learning algorithm trained on theoretical supernova nucleosynthesis models, they found that 68 per cent of the observed extremely metal-poor stars have a chemical fingerprint consistent with enrichment by multiple previous supernovae.

The team's results give the first quantitative constraint based on observations on the multiplicity of the first stars.

"Multiplicity of the first stars were only predicted from numerical simulations so far, and there was no way to observationally examine the theoretical prediction until now," said lead author Hartwig. "Our result suggests that most first stars formed in small clusters so that multiple of their supernovae can contribute to the metal enrichment of the early interstellar medium," he said.

"Our new algorithm provides an excellent tool to interpret the big data we will have in the next decade from on-going and future astronomical surveys across the world" said Kobayashi, also a Leverhulme Research Fellow.

"At the moment, the available data of old stars are the tip of the iceberg within the solar neighborhood. The Prime Focus Spectrograph, a cutting-edge multi-object spectrograph on the Subaru Telescope developed by the international collaboration led by Kavli IPMU, is the best instrument to discover ancient stars in the outer regions of the Milky Way far beyond the solar neighborhood.," said Ishigaki.

The new algorithm invented in this study opens the door to make the most of diverse chemical fingerprints in metal-poor stars discovered by the Prime Focus Spectrograph.

"The theory of the first stars tells us that the first stars should be more massive than the Sun. The natural expectation was that the first star was born in a gas cloud containing the mass million times more than the Sun. However, our new finding strongly suggests that the first stars were not born alone, but instead formed as a part of a star cluster or a binary or multiple star system. This also means that we can expect gravitational waves from the first binary stars soon after the Big Bang, which could be detected future missions in space or on the Moon," said Kobayashi.

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Autonomous transport company Aurrigo has improved its driverless vehicles capabilities in a project with Aston University.

Aurrigos airport Auto-Dolly is now able to differentiate between many different objects

The two-year Knowledge Transfer Partnership (KTP) with the university developed a new machine vision solution, using machine learning and artificial intelligence that means the Coventry-based companys driverless vehicles are now able to see and recognise objects in greater detail. This results in improved performance across a wider spectrum of test situations.

Previously the companys driverless vehicles were only capable of detecting that there was an object in their path, not the type of object, so would just stop when they encountered something in their way.

The new computer vision systems, coupled with machine learning and artificial intelligence, are now able to differentiate between different objects, enabling Aurrigos airport Auto-Dolly to differentiate between many different objects airside.

Professor David Keene, CEO of Aurrigo, said: This partnership has allowed us to produce a system which has resulted in our vehicles becoming smarter and more capable and enabled us to expand our operations, particularly with baggage handling in airports worldwide.

Dr George Vogiatzis, senior lecturer in computer science at Aston University, added: This KTP has been a great way for us to work with a new industrial partner whilst applying our expertise in deep learning and robotics to the exciting field of autonomous vehicles.

It is very rewarding to see the success of this collaboration.

The project findings will also be applied to other vehicles in the Aurrigo product range.

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