We're at a once-in-a-generation inflection point in computing. The traditional ways of designing and building computing infrastructure are no longer adequate for the exponentially growing demands of workloads like generative AI and LLMs. In fact, the number of parameters in LLMs has increased by 10x per year over the past five years. As a result, customers need AI-optimized infrastructure that is both cost effective and scalable.

For two decades, Google has built some of the industry's leading AI capabilities: from the creation of Google's Transformer architecture that makes gen AI possible, to our AI-optimized infrastructure, which is built to deliver the global scale and performance required by Google products that serve billions of users like YouTube, Gmail, Google Maps, Google Play, and Android. We are excited to bring decades of innovation and research to Google Cloud customers as they pursue transformative opportunities in AI. We offer a complete solution for AI, from computing infrastructure optimized for AI to the end-to-end software and services that support the full lifecycle of model training, tuning, and serving at global scale.

Google Cloud Next—Google Cloud and NVIDIA today announced new AI infrastructure and software for customers to build and deploy massive models for generative AI and speed data science workloads.

In a fireside chat at Google Cloud Next, Google Cloud CEO Thomas Kurian and NVIDIA founder and CEO Jensen Huang discussed how the partnership is bringing end-to-end machine learning services to some of the largest AI customers in the world—including by making it easy to run AI supercomputers with Google Cloud offerings built on NVIDIA technologies. The new hardware and software integrations utilize the same NVIDIA technologies employed over the past two years by Google DeepMind and Google research teams.

NVIDIA's latest financial report for FY2Q24 reveals that its data center business reached US$10.32 billion—a QoQ growth of 141% and YoY increase of 171%. The company remains optimistic about its future growth. TrendForce believes that the primary driver behind NVIDIA's robust revenue growth stems from its data center's AI server-related solutions. Key products include AI-accelerated GPUs and AI server HGX reference architecture, which serve as the foundational AI infrastructure for large data centers.

TrendForce further anticipates that NVIDIA will integrate its software and hardware resources. Utilizing a refined approach, NVIDIA will align its high-end, mid-tier, and entry-level GPU AI accelerator chips with various ODMs and OEMs, establishing a collaborative system certification model. Beyond accelerating the deployment of CSP cloud AI server infrastructures, NVIDIA is also partnering with entities like VMware on solutions including the Private AI Foundation. This strategy extends NVIDIA's reach into the edge enterprise AI server market, underpinning steady growth in its data center business for the next two years.

Microsoft Azure users can now turn to the latest NVIDIA accelerated computing technology to train and deploy their generative AI applications. Available today, the Microsoft Azure ND H100 v5 VMs using NVIDIA H100 Tensor Core GPUs and NVIDIA Quantum-2 InfiniBand networking—enables scaling generative AI, high performance computing (HPC) and other applications with a click from a browser. Available to customers across the U.S., the new instance arrives as developers and researchers are using large language models (LLMs) and accelerated computing to uncover new consumer and business use cases.

The NVIDIA H100 GPU delivers supercomputing-class performance through architectural innovations, including fourth-generation Tensor Cores, a new Transformer Engine for accelerating LLMs and the latest NVLink technology that lets GPUs talk to each other at 900 GB/s. The inclusion of NVIDIA Quantum-2 CX7 InfiniBand with 3,200 Gbps cross-node bandwidth ensures seamless performance across the GPUs at massive scale, matching the capabilities of top-performing supercomputers globally.

Inventec, a global leader in high-powered servers headquartered in Taiwan, is launching its cutting-edge C805G6 server for data centers based on AMD's newest 4th Gen EPYC platform—a major innovation in computing power that provides double the operating efficiency of previous platforms. These innovations are timely, as the industry worldwide faces converse challenges—on one hand, a growing need to reduce carbon footprints and power consumption, while, on the other hand, the push for ever higher computing power and performance for AI. In fact, in 2022 MIT found that improving a machine learning model tenfold will require a 10,000-fold increase in computational requirements.

Addressing both pain points, George Lin, VP of Business Unit VI, Inventec Enterprise Business Group (Inventec EBG) notes that, "Our latest C805G6 data center solution represents an innovation both for the present and the future, setting the standard for performance, energy efficiency, and security while delivering top-notch hardware for powering AI workloads."

TrendForce reports that the HBM (High Bandwidth Memory) market's dominant product for 2023 is HBM2e, employed by the NVIDIA A100/A800, AMD MI200, and most CSPs' (Cloud Service Providers) self-developed accelerator chips. As the demand for AI accelerator chips evolves, manufacturers plan to introduce new HBM3e products in 2024, with HBM3 and HBM3e expected to become mainstream in the market next year.

The distinctions between HBM generations primarily lie in their speed. The industry experienced a proliferation of confusing names when transitioning to the HBM3 generation. TrendForce clarifies that the so-called HBM3 in the current market should be subdivided into two categories based on speed. One category includes HBM3 running at speeds between 5.6 to 6.4 Gbps, while the other features the 8 Gbps HBM3e, which also goes by several names including HBM3P, HBM3A, HBM3+, and HBM3 Gen2.

AWS users can now access the leading performance demonstrated in industry benchmarks of AI training and inference. The cloud giant officially switched on a new Amazon EC2 P5 instance powered by NVIDIA H100 Tensor Core GPUs. The service lets users scale generative AI, high performance computing (HPC) and other applications with a click from a browser.

The news comes in the wake of AI's iPhone moment. Developers and researchers are using large language models (LLMs) to uncover new applications for AI almost daily. Bringing these new use cases to market requires the efficiency of accelerated computing. The NVIDIA H100 GPU delivers supercomputing-class performance through architectural innovations including fourth-generation Tensor Cores, a new Transformer Engine for accelerating LLMs and the latest NVLink technology that lets GPUs talk to each other at 900 GB/sec.

NVIDIA could be adjusting its enterprise-grade GPU production strategies for the Chinese market, according to an article published by MyDriver—despite major sanctions placed on semiconductor imports, Team Green is doing plenty of business with tech firms operating in the region thanks to an uptick in AI-related activities. NVIDIA offers two market specific accelerator models that have been cut down to conform to rules and regulations—the more powerful and expensive (250K RMB/~$35K) H800 is an adaptation of the western H100 GPU, while the A800 is a legal market alternative to the older A100.

The report proposes that NVIDIA is considering plans to reduce factory output of the A800 (sold for 100K RMB/~$14K per unit), so clients will be semi-forced into purchasing the higher-end H800 model instead (if they require a significant number of GPUs). The A800 seems to be the more popular choice for the majority of companies at the moment, with the heavy hitters—Alibaba, Baidu, Tencent, Jitwei and ByteDance—flexing their spending muscles and splurging on mixed shipments of the two accelerators. By limiting supplies of the lesser A800, Team Green could be generating more profit by prioritizing the more expensive (and readily available) model.

A relatively new player in the water cooling industry, Comino, has recently introduced its latest product: a water block for the NVIDIA H100 PCIe accelerator card. The new block provides full coverage with cooling to the GPU, GDDR, and VRM. In the design, Comino only used non-corrosive materials such as copper, stainless steel, aluminium, and Plastic. The core of the block uses copper, while the frame and backplate use aluminium. The company claims that at a coolant temperature of 20°C, the temperature of the GH100 chip with Comino water blocks will be 30º-40°C.

Comino uses "deformational cutting" technology to create a copper fin as thin as 0.1 mm with a 0.1 mm channel and 3 mm height. In Comino water blocks, micro fins are optimized for a low-pressure drop with a thickness of 0.25 mm, channel - 0.25 mm, and 2.7 mm height. The block itself is a single-slot solution with fitting adapters on the back and a 90º adapter option for workstation implementation. More information is available on the Comino website. You can see the images below.

The AI hype continues to push hardware shipments, especially for servers with GPUs that are in very high demand. Another example is the latest feat of AI startup, Inflection AI. Building foundational AI models, the Inflection AI crew has secured an order of 22,000 NVIDIA H100 GPUs and built a supercomputer. Assuming a configuration of a single Intel Xeon CPU with eight GPUs, almost 700 four-node racks should go into the supercomputer. Scaling and connecting 22,000 GPUs is easier than it is to acquire them, as NVIDIA's H100 GPUs are selling out everywhere due to the enormous demand for AI applications both on and off premises.

Getting 22,000 H100 GPUs is the biggest challenge here, and Inflection AI managed to get them by having NVIDIA as an investor in the startup. The supercomputer is estimated to cost around one billion USD and consume 31 Mega-Watts of power. The Inflection AI startup is now valued at 1.5 billion USD at the time of writing.

In a new industry-standard benchmark, a cluster of 3,584 H100 GPUs at cloud service provider CoreWeave trained a massive GPT-3-based model in just 11 minutes. Leading users and industry-standard benchmarks agree: NVIDIA H100 Tensor Core GPUs deliver the best AI performance, especially on the large language models (LLMs) powering generative AI.

H100 GPUs set new records on all eight tests in the latest MLPerf training benchmarks released today, excelling on a new MLPerf test for generative AI. That excellence is delivered both per-accelerator and at-scale in massive servers. For example, on a commercially available cluster of 3,584 H100 GPUs co-developed by startup Inflection AI and operated by CoreWeave, a cloud service provider specializing in GPU-accelerated workloads, the system completed the massive GPT-3-based training benchmark in less than eleven minutes.

Today, MLCommons published results of its industry AI performance benchmark, MLPerf Training 3.0, in which both the Habana Gaudi2 deep learning accelerator and the 4th Gen Intel Xeon Scalable processor delivered impressive training results.

"The latest MLPerf results published by MLCommons validates the TCO value Intel Xeon processors and Intel Gaudi deep learning accelerators provide to customers in the area of AI. Xeon's built-in accelerators make it an ideal solution to run volume AI workloads on general-purpose processors, while Gaudi delivers competitive performance for large language models and generative AI. Intel's scalable systems with optimized, easy-to-program open software lowers the barrier for customers and partners to deploy a broad array of AI-based solutions in the data center from the cloud to the intelligent edge." - Sandra Rivera, Intel executive vice president and general manager of the Data Center and AI Group

NVIDIA's compute and AI-oriented H100 GPU is supposedly getting an upgrade. The H100 GPU is NVIDIA's most powerful offering and comes in a few different flavors: H100 PCIe, H100 SXM, and H100 NVL (a duo of two GPUs). Currently, the H100 GPU comes with 80 GB of HBM2E, both in the PCIe and SXM5 version of the card. A notable exception if the H100 NVL, which comes with 188 GB of HBM3, but that is for two cards, making it 94 GB per each. However, we could see NVIDIA enable 94 and 64 GB options for the H100 accelerator soon, as the latest PCI ID Repository shows.

According to the PCI ID Repository listing, two messages are posted: "Kindly help to add H100 SXM5 64 GB into 2337." and "Kindly help to add H100 SXM5 94 GB into 2339." These two messages indicate that NVIDIA could prepare its H100 in more variations. In September 2022, we saw NVIDIA prepare an H100 variation with 120 GB of memory, but that still isn't official. These PCIe IDs could just come from engineering samples that NVIDIA is testing in the labs, and these cards could never appear on any market. So, we have to wait and see how it plays out.

TrendForce reports that explosive growth in generative AI applications like chatbots has spurred significant expansion in AI server development in 2023. Major CSPs including Microsoft, Google, AWS, as well as Chinese enterprises like Baidu and ByteDance, have invested heavily in high-end AI servers to continuously train and optimize their AI models. This reliance on high-end AI servers necessitates the use of high-end AI chips, which in turn will not only drive up demand for HBM during 2023~2024, but is also expected to boost growth in advanced packaging capacity by 30~40% in 2024.

TrendForce highlights that to augment the computational efficiency of AI servers and enhance memory transmission bandwidth, leading AI chip makers such as Nvidia, AMD, and Intel have opted to incorporate HBM. Presently, Nvidia's A100 and H100 chips each boast up to 80 GB of HBM2e and HBM3. In its latest integrated CPU and GPU, the Grace Hopper Superchip, Nvidia expanded a single chip's HBM capacity by 20%, hitting a mark of 96 GB. AMD's MI300 also uses HBM3, with the MI300A capacity remaining at 128 GB like its predecessor, while the more advanced MI300X has ramped up to 192 GB, marking a 50% increase. Google is expected to broaden its partnership with Broadcom in late 2023 to produce the AISC AI accelerator chip TPU, which will also incorporate HBM memory, in order to extend AI infrastructure.

The top technology companies in China have been ordering a lot of NVIDIA enterprise-grade GPUs, even though U.S. sanctions have prevented the shipment of A100 and H100 models (plus AMD's MI250 Instinct accelerator) to the nation in recent times. ByteDance - best known for developing TikTok - managed to grab plenty of Ampere enterprise units prior to last Autumn's cutoff period, and has continued to purchase Team Green's H800 GPU, which is a cut-down version of the H100 flagship. Smaller outfits are relying on less direct sources to acquire HBC GPUs—according to a Reuters investigative article, international trade restrictions have created a thriving black market for "top-end NVIDIA AI chips."

Their reporters carried out some on-site sleuthing: "Visiting the famed Huaqiangbei electronics area in the southern Chinese city of Shenzhen is a good bet - in particular, the SEG Plaza skyscraper whose first 10 floors are crammed with shops selling everything from camera parts to drones. The chips are not advertised but asking discreetly works...They don't come cheap. Two vendors there, who spoke with Reuters in person on condition of anonymity, said they could provide small numbers of A100 artificial intelligence chips made by the U.S. chip designer, pricing them at $20,000 a piece - double the usual price."

NVIDIA's H100 Hopper GPU is a device designed for pure AI and other compute workloads, with the least amount of consideration for gaming workloads that involve graphics processing. However, it is still interesting to see how this 30,000 USD GPU fairs in comparison to other gaming GPUs and whether it is even possible to run games on it. It turns out that it is technically feasible but not making much sense, as the Chinese YouTube channel Geekerwan notes. Based on the GH100 GPU SKU with 14,592 CUDA, the H100 PCIe version tested here can achieve 204.9 TeraFLOPS at FP16, 51.22 TeraFLOPS at FP32, and 25.61 TeraFLOPS at FP64, with its natural power laying in accelerating AI workloads.

However, how does it fare in gaming benchmarks? Not very well, as the testing shows. It scored 2681 points in 3DMark Time Spy, which is lower than AMD's integrated Radeon 680M, which managed to score 2710 points. Interestingly, the GH100 has only 24 ROPs (render output units), while the gaming-oriented GA102 (highest-end gaming GPU SKU) has 112 ROPs. This is self-explanatory and provides a clear picture as to why the H100 GPU is used for computing only. Since it doesn't have any display outputs, the system needed another regular GPU to provide the picture, while the computation happened on the H100 GPU.

ASUS today announced ESC N8-E11, its most advanced HGX H100 eight-GPU AI server, along with a comprehensive PCI Express (PCIe) GPU server portfolio—the ESC8000 and ESC4000 series empowered by Intel and AMD platforms to support higher CPU and GPU TDPs to accelerate the development of AI and data science.

ASUS is one of the few HPC solution providers with its own all-dimensional resources that consist of the ASUS server business unit, Taiwan Web Service (TWS) and ASUS Cloud—all part of the ASUS group. This uniquely positions ASUS to deliver in-house AI server design, data-center infrastructure, and AI software-development capabilities, plus a diverse ecosystem of industrial hardware and software partners.

GIGABYTE is exhibiting cutting-edge technologies and solutions at COMPUTEX 2023, presenting the theme "Future of COMPUTING". From May 30th to June 2nd, GIGABYTE is showcasing over 110 products that are driving future industry transformation, demonstrating the emerging trends of AI technology and sustainability, on the 1st floor, Taipei Nangang Exhibition Center, Hall 1.

GIGABYTE and its subsidiary, Giga Computing, are introducing unparalleled AI/HPC server lineups, leading the era of exascale supercomputing. One of the stars is the industry's first NVIDIA-certified HGX H100 8-GPU SXM5 server, G593-SD0. Equipped with the 4th Gen Intel Xeon Scalable Processors and GIGABYTE's industry-leading thermal design, G593-SD0 can perform extremely intensive workloads from generative AI and deep learning model training within a density-optimized 5U server chassis, making it a top choice for data centers aimed for AI breakthroughs. In addition, GIGABYTE is debuting AI computing servers supporting NVIDIA Grace CPU and Grace Hopper Superchips. The high-density servers are accelerated with NVLink-C2C technology under the ARM Neoverse V2 platform, setting a new standard for AI/HPC computing efficiency and bandwidth.

NVIDIA today announced NVIDIA Spectrum-X, an accelerated networking platform designed to improve the performance and efficiency of Ethernet-based AI clouds. NVIDIA Spectrum-X is built on networking innovations powered by the tight coupling of the NVIDIA Spectrum-4 Ethernet switch with the NVIDIA BlueField -3 DPU, achieving 1.7x better overall AI performance and power efficiency, along with consistent, predictable performance in multi-tenant environments. Spectrum-X is supercharged by NVIDIA acceleration software and software development kits (SDKs), allowing developers to build software-defined, cloud-native AI applications.

The delivery of end-to-end capabilities reduces run-times of massive transformer-based generative AI models. This allows network engineers, AI data scientists and cloud service providers to improve results and make informed decisions faster. The world's top hyperscalers are adopting NVIDIA Spectrum-X, including industry-leading cloud innovators. As a blueprint and testbed for NVIDIA Spectrum-X reference designs, NVIDIA is building Israel-1, a hyperscale generative AI supercomputer to be deployed in its Israeli data center on Dell PowerEdge XE9680 servers based on the NVIDIA HGX H100 eight-GPU platform, BlueField-3 DPUs and Spectrum-4 switches.

Dell Technologies and NVIDIA announce a joint initiative to make it easier for businesses to build and use generative AI models on-premises to quickly and securely deliver better customer service, market intelligence, enterprise search and a range of other capabilities. Project Helix will deliver a series of full-stack solutions with technical expertise and pre-built tools based on Dell and NVIDIA infrastructure and software. It includes a complete blueprint to help enterprises use their proprietary data and more easily deploy generative AI responsibly and accurately.

"Project Helix gives enterprises purpose-built AI models to more quickly and securely gain value from the immense amounts of data underused today," said Jeff Clarke, vice chairman and co-chief operating officer, Dell Technologies. "With highly scalable and efficient infrastructure, enterprises can create a new wave of generative AI solutions that can reinvent their industries."

"We are at a historic moment, when incredible advances in generative AI are intersecting with enterprise demand to do more with less," said Jensen Huang, founder and CEO, NVIDIA. "With Dell Technologies, we've designed extremely scalable, highly efficient infrastructure that enables enterprises to transform their business by securely using their own data to build and operate generative AI applications."

ASUS today announced a showcase of the latest HPC solutions to empower innovation and push the boundaries of supercomputing, at ISC High Performance 2023 in Hamburg, Germany on May 21-25, 2023. The ASUS exhibition, at booth H813, will reveal the latest supercomputing advances, including liquid-cooling and AI solutions, as well as outlining a slew of sustainability breakthroughs - plus a whole lot more besides.

Comprehensive Liquid-Cooling Solutions
ASUS is working with Submer, the industry-leading liquid-cooling provider to demonstrate immersion-cooling solutions at ISC High Performance 2023, focused on ASUS RS720-E11-IM - the Intel -based 2U4N server that leverages our trusted legacy server architecture and popular features to create a compact new design. This fresh outlook improves the accessibility on I/O ports, storage and cable routing, and strengthens the structure to allow the server to be placed vertically in the tank, with durability assured.

Increasing its HPL score from 1.02 Eflop/s in November 2022 to an impressive 1.194 Eflop/s on this list, Frontier was able to improve upon its score after a stagnation between June 2022 and November 2022. Considering exascale was only a goal to aspire to just a few years ago, a roughly 17% increase here is an enormous success. Additionally, Frontier earned a score of 9.95 Eflop/s on the HLP-MxP benchmark, which measures performance for mixed-precision calculation. This is also an increase over the 7.94 EFlop/s that the system achieved on the previous list and nearly 10 times more powerful than the machine's HPL score. Frontier is based on the HPE Cray EX235a architecture and utilizes AMD EPYC 64C 2 GHz processors. It also has 8,699,904 cores and an incredible energy efficiency rating of 52.59 Gflops/watt. It also relies on gigabit ethernet for data transfer.

NVIDIA today announced a supercomputer built on the NVIDIA Grace CPU Superchip, adding to a wave of new energy-efficient supercomputers based on the Arm Neoverse platform. The Isambard 3 supercomputer to be based at the Bristol & Bath Science Park, in the U.K., will feature 384 Arm-based NVIDIA Grace CPU Superchips to power medical and scientific research, and is expected to deliver 6x the performance and energy efficiency of Isambard 2, placing it among Europe's most energy-efficient systems.

It will achieve about 2.7 petaflops of FP64 peak performance and consume less than 270 kilowatts of power, ranking it among the world's three greenest non-accelerated supercomputers. The project is being led by the University of Bristol, as part of the research consortium the GW4 Alliance, together with the universities of Bath, Cardiff and Exeter.

Supermicro, Inc., a Total IT Solution Provider for Cloud, AI/ML, Storage, and 5G/Edge, continues to expand its data center offerings with liquid cooled NVIDIA HGX H100 rack scale solutions. Advanced liquid cooling technologies entirely from Supermicro reduce the lead time for a complete installation, increase performance, and result in lower operating expenses while significantly reducing the PUE of data centers. Savings for a data center are estimated to be 40% for power when using Supermicro liquid cooling solutions compared to an air-cooled data center. In addition, up to 86% reduction in direct cooling costs compared to existing data centers may be realized.

"Supermicro continues to lead the industry supporting the demanding needs of AI workloads and modern data centers worldwide," said Charles Liang, president, and CEO of Supermicro. "Our innovative GPU servers that use our liquid cooling technology significantly lower the power requirements of data centers. With the amount of power required to enable today's rapidly evolving large scale AI models, optimizing TCO and the Total Cost to Environment (TCE) is crucial to data center operators. We have proven expertise in designing and building entire racks of high-performance servers. These GPU systems are designed from the ground up for rack scale integration with liquid cooling to provide superior performance, efficiency, and ease of deployments, allowing us to meet our customers' requirements with a short lead time."

Implementing state-of-the-art artificial intelligence (AI) and machine learning (ML) models requires large amounts of computation, both to train the underlying models, and to serve those models once they're trained. Given the demands of these workloads, a one-size-fits-all approach is not enough - you need infrastructure that's purpose-built for AI.

Together with our partners, we offer a wide range of compute options for ML use cases such as large language models (LLMs), generative AI, and diffusion models. Recently, we announced G2 VMs, becoming the first cloud to offer the new NVIDIA L4 Tensor Core GPUs for serving generative AI workloads. Today, we're expanding that portfolio with the private preview launch of the next-generation A3 GPU supercomputer. Google Cloud now offers a complete range of GPU options for training and inference of ML models.