According to a well-known industry analyst, Ming-Chi Kuo, NVIDIA has restructured its "Blackwell" architecture roadmap, emphasizing dual-die designs using CoWoS-L packaging technology. The new roadmap eliminates several single-die products that would have used CoWoS-S packaging, changing NVIDIA's manufacturing strategy. The 200 Series will exclusively use dual-die designs with CoWoS-L packaging, featuring the GB200 NVL72 and HGX B200 systems. Notably absent is the previously expected B200A single-die variant. The 300 Series will include both dual-die and single-die options, though NVIDIA and cloud providers are prioritizing the GB200 NVL72 dual-die system. Starting Q1 2025, NVIDIA will reduce H series production, which uses CoWoS-S packaging, while ramping up 200 Series production. This transition indicates significantly decreased demand for CoWoS-S capacity through 2025.

While B300 systems using single-die CoWoS-S are planned for 2026 mass production, the current focus remains on dual-die CoWoS-L products. From TSMC's perspective, the transition between Blackwell generations requires minimal process adjustments, as both use similar front-end-of-line processes with only back-end-of-line modifications needed. Supply chain partners heavily dependent on CoWoS-S production face significant impact, reflected in recent stock price corrections. However, NVIDIA maintains this change reflects product strategy evolution rather than market demand weakness. TSMC continues expanding CoWoS-R capacity while slowing CoWoS-S expansion, viewing AI and high-performance computing as sustained growth drivers despite these packaging technology transitions.

NVIDIA's new GB200 "Blackwell" racks are running into trouble (again). Big cloud companies like Microsoft, Amazon, Google, and Meta Platforms are cutting back their orders because of heat problems, Reuters reports, quoting The Information. The first shipments of racks with Blackwell chips are getting too hot and have connection issues between chips, the report says. These tech hiccups have made some customers who ordered $10 billion or more worth of racks think twice about buying.

Some are putting off their orders until NVIDIA has better versions of the racks. Others are looking at buying older NVIDIA AI chips instead. For example, Microsoft planned to set up GB200 racks with no less than 50,000 Blackwell chips at one of its Phoenix sites. However, The Information reports that OpenAI has asked Microsoft to provide NVIDIA's older "Hopper" chips instead pointing to delays linked to the Blackwell racks. NVIDIA's problems with its Blackwell GPUs housed in high-density racks are not something new; in November 2024, Reuters, also referencing The Information, uncovered overheating issues in servers that housed 72 processors. NVIDIA has made several changes to its server rack designs to tackle these problems, however, it seems that the problem was not entirely solved.

GIGABYTE Technology, internationally renowned for its R&D capabilities and a leading innovator in server and data center solutions, continues to lead technological innovation during this critical period of AI and computing advancement. With its comprehensive AI product portfolio, GIGABYTE will showcase its complete range of AI computing solutions at CES 2025, from data center infrastructure to IoT applications and personal computing, demonstrating how its extensive product line enables digital transformation across all sectors in this AI-driven era.

Powering AI from the Cloud
With AI Large Language Models (LLMs) now routinely featuring parameters in the hundreds of billions to trillions, robust training environments (data centers) have become a critical requirement in the AI race. GIGABYTE offers three distinctive solutions for AI infrastructure.
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Giga Computing, a subsidiary of GIGABYTE and an industry leader in generative AI servers and advanced cooling technologies, announced new GIGABYTE G893 series servers using the NVIDIA HGX B200 platform. The launch of these flagship 8U air-cooled servers, the G893-SD1-AAX5 and G893-ZD1-AAX5, signifies a new architecture and platform change for GIGABYTE in the demanding world of high-performance computing and AI, setting new standards for speed, scalability, and versatility.

These servers join GIGABYTE's accelerated computing portfolio alongside the NVIDIA GB200 NVL72 platform, which is a rack-scale design that connects 36 NVIDIA Grace CPUs and 72 NVIDIA Blackwell GPUs. At CES 2025 (January 7-10), the GIGABYTE booth will display the NVIDIA GB200 NVL72, and attendees can engage in discussions about the benefits of GIGABYTE platforms with the NVIDIA Blackwell architecture.

NVIDIA "Blackwell" series is barely out with B100, B200, and GB200 chips shipping to OEMs and hyperscalers, but the company is already setting in its upgraded "Blackwell Ultra" plans with its upcoming GB300 AI server. According to UDN, the next generation NVIDIA system will be powered by the B300 GPU chip, operating at 1400 W and delivering a remarkable 1.5x improvement in FP4 performance per card compared to its B200 predecessor. One of the most notable upgrades is the memory configuration, with each GPU now sporting 288 GB of HBM3e memory, a substantial increase from the previous 192 GB of GB200. The new design implements a 12-layer stack architecture, advancing from the GB200's 8-layer configuration. The system's cooling infrastructure has been completely reimagined, incorporating advanced water cooling plates and enhanced quick disconnects in the liquid cooling system.

Networking capabilities have also seen a substantial upgrade, with the implementation of ConnectX 8 network cards replacing the previous ConnectX 7 generation, while optical modules have been upgraded from 800G to 1.6T, ensuring faster data transmission. Regarding power management and reliability, the GB300 NVL72 cabinet will standardize capacitor tray implementation, with an optional Battery Backup Unit (BBU) system. Each BBU module costs approximately $300 to manufacture, with a complete GB300 system's BBU configuration totaling around $1,500. The system's supercapacitor requirements are equally substantial, with each NVL72 rack requiring over 300 units, priced between $20-25 per unit during production due to its high-power nature. The GB300, carrying Grace CPU and Blackwell Ultra GPU, also introduces the implementation of LPCAMM on its computing boards, indicating that the LPCAMM memory standard is about to take over servers, not just laptops and desktops. We have to wait for the official launch before seeing LPCAMM memory configurations.

The "Rubin" architecture succeeds NVIDIA's current "Blackwell," which powers the company's AI GPUs, as well as the upcoming GeForce RTX 50-series gaming GPUs. NVIDIA will likely not build gaming GPUs with "Rubin," just like it didn't with "Hopper," and for the most part, "Volta." NVIDIA's AI GPU product roadmap put out at SC'24 puts "Blackwell" firmly in charge of the company's AI GPU product stack throughout 2025, with "Rubin" only succeeding it in the following year, for a two-year run in the market, being capped off with a "Rubin Ultra" larger GPU slated for 2027. A new report by United Daily News (UDN), a Taiwan-based publication, says that the development of "Rubin" is running 6 months ahead of schedule.

Being 6 months ahead of schedule doesn't necessarily mean that the product will launch sooner. It would give NVIDIA headroom to get "Rubin" better evaluated in the industry, and make last-minute changes to the product if needed; or even advance the launch if it wants to. The first AI GPU powered by "Rubin" will feature 8-high HBM4 memory stacks. The company will also introduce the "Vera" CPU, the long-awaited successor to "Grace." It will also introduce the X1600 InfiniBand/Ethernet network processor. According to the SC'24 roadmap by NVIDIA, these three would've seen a 2026 launch. Then in 2027, the company would follow up with an even larger AI GPU based on the same "Rubin" architecture, codenamed "Rubin Ultra." This features 12-high HBM4 stacks. NVIDIA's current GB200 "Blackwell" is a tile-based GPU, with two dies that have full cache-coherence. "Rubin" is rumored to feature four tiles.

NVIDIA and Microsoft today unveiled product integrations designed to advance full-stack NVIDIA AI development on Microsoft platforms and applications. At Microsoft Ignite, Microsoft announced the launch of the first cloud private preview of the Azure ND GB200 V6 VM series, based on the NVIDIA Blackwell platform. The Azure ND GB200 v6 will be a new AI-optimized virtual machine (VM) series and combines the NVIDIA GB200 NVL72 rack design with NVIDIA Quantum InfiniBand networking.

In addition, Microsoft revealed that Azure Container Apps now supports NVIDIA GPUs, enabling simplified and scalable AI deployment. Plus, the NVIDIA AI platform on Azure includes new reference workflows for industrial AI and an NVIDIA Omniverse Blueprint for creating immersive, AI-powered visuals. At Ignite, NVIDIA also announced multimodal small language models (SLMs) for RTX AI PCs and workstations, enhancing digital human interactions and virtual assistants with greater realism.

At SC24, NVIDIA announced its latest compute-dense AI accelerators in the form of GB200 NVL4, a single-server solution that expands the company's "Blackwell" series portfolio. The new platform features an impressive combination of four "Blackwell" GPUs and two "Grace" CPUs on a single board. The GB200 NVL4 boasts remarkable specifications for a single-server system, including 768 GB of HBM3E memory across its four Blackwell GPUs, delivering a combined memory bandwidth of 32 TB/s. The system's two Grace CPUs have 960 GB of LPDDR5X memory, making it a powerhouse for demanding AI workloads. A key feature of the NVL4 design is its NVLink interconnect technology, which enables communication between all processors on the board. This integration is important for maintaining optimal performance across the system's multiple processing units, especially during large training runs or inferencing a multi-trillion parameter model.

Performance comparisons with previous generations show significant improvements, with NVIDIA claiming the GB200 GPUs deliver 2.2x faster overall performance and 1.8x quicker training capabilities compared to their GH200 NVL4 predecessor. The system's power consumption reaches 5,400 watts, which effectively doubles the 2,700-watt requirement of the GB200 NVL2 model, its smaller sibling that features two GPUs instead of four. NVIDIA is working closely with OEM partners to bring various Blackwell solutions to market, including the DGX B200, GB200 Grace Blackwell Superchip, GB200 Grace Blackwell NVL2, GB200 Grace Blackwell NVL4, and GB200 Grace Blackwell NVL72. Fitting 5,400 W of TDP in a single server will require liquid cooling for optimal performance, and the GB200 NVL4 is expected to go inside server racks for hyperscaler customers, which usually have a custom liquid cooling systems inside their data centers.

Supermicro, Inc., a Total IT Solution Provider for AI, Cloud, Storage, and 5G/Edge, is announcing the highest-performing SuperCluster, an end-to-end AI data center solution featuring the NVIDIA Blackwell platform for the era of trillion-parameter-scale generative AI. The new SuperCluster will significantly increase the number of NVIDIA HGX B200 8-GPU systems in a liquid-cooled rack, resulting in a large increase in GPU compute density compared to Supermicro's current industry-leading liquid-cooled NVIDIA HGX H100 and H200-based SuperClusters. In addition, Supermicro is enhancing the portfolio of its NVIDIA Hopper systems to address the rapid adoption of accelerated computing for HPC applications and mainstream enterprise AI.

"Supermicro has the expertise, delivery speed, and capacity to deploy the largest liquid-cooled AI data center projects in the world, containing 100,000 GPUs, which Supermicro and NVIDIA contributed to and recently deployed," said Charles Liang, president and CEO of Supermicro. "These Supermicro SuperClusters reduce power needs due to DLC efficiencies. We now have solutions that use the NVIDIA Blackwell platform. Using our Building Block approach allows us to quickly design servers with NVIDIA HGX B200 8-GPU, which can be either liquid-cooled or air-cooled. Our SuperClusters provide unprecedented density, performance, and efficiency, and pave the way toward even more dense AI computing solutions in the future. The Supermicro clusters use direct liquid cooling, resulting in higher performance, lower power consumption for the entire data center, and reduced operational expenses."

ASRock Rack Inc., a leading innovative server company, today announces its presence at SC24, held at the Georgia World Congress Center in Atlanta from November 18-21. At booth #3609, ASRock Rack will showcase a comprehensive high-performance portfolio of server boards, systems, and rack solutions with NVIDIA accelerated computing platforms, helping address the needs of enterprises, organizations, and data centers.

Artificial intelligence (AI) and high-performance computing (HPC) continue to reshape technology. ASRock Rack is presenting a complete suite of solutions spanning edge, on-premise, and cloud environments, engineered to meet the demand of AI and HPC. The 2U short-depth MECAI, incorporating the NVIDIA GH200 Grace Hopper Superchip, is developed to supercharge accelerated computing and generative AI in space-constrained environments. The 4U10G-TURIN2 and 4UXGM-GNR2, supporting ten and eight NVIDIA H200 NVL PCIe GPUs respectively, are aiming to help enterprises and researchers tackle every AI and HPC challenge with enhanced performance and greater energy efficiency. NVIDIA H200 NVL is ideal for lower-power, air-cooled enterprise rack designs that require flexible configurations, delivering acceleration for AI and HPC workloads regardless of size.

Giga Computing, a subsidiary of GIGABYTE and an industry leader in generative AI servers and advanced cooling technologies, shows off at SC24 how the GIGABYTE enterprise portfolio provides solutions for all applications, from cloud computing to AI to enterprise IT, including energy-efficient liquid-cooling technologies. This portfolio is made more complete by long-term collaborations with leading technology companies and emerging industry leaders, which will be showcased at GIGABYTE booth #3123 at SC24 (Nov. 19-21) in Atlanta. The booth is sectioned to put the spotlight on strategic technology collaborations, as well as direct liquid cooling partners.

The GIGABYTE booth will showcase an array of NVIDIA platforms built to keep up with the diversity of workloads and degrees of demands in applications of AI & HPC hardware. For a rack-scale AI solution using the NVIDIA GB200 NVL72 design, GIGABYTE displays how seventy-two GPUs can be in one rack with eighteen GIGABYTE servers each housing two NVIDIA Grace CPUs and four NVIDIA Blackwell GPUs. Another platform at the GIGABYTE booth is the NVIDIA HGX H200 platform. GIGABYTE exhibits both its liquid-cooling G4L3-SD1 server and an air-cooled version, G593-SD1.

ASUS today announced its next-generation infrastructure solutions at SC24, unveiling an extensive server lineup and advanced cooling solutions, all designed to propel the future of AI. The product showcase will reveal how ASUS is working with NVIDIA and Ubitus/Ubilink to prove the immense computational power of supercomputers, using AI-powered avatar and robot demonstrations that leverage the newly-inaugurated data center. It is Taiwan's largest supercomputing facility, constructed by ASUS, and is also notable for offering flexible green-energy options to customers that desire them. As a total solution provider with a proven track record in pioneering AI supercomputing, ASUS continuously drives maximized value for customers.

To fuel digital transformation in enterprise through high-performance computing (HPC) and AI-driven architecture, ASUS provides a full line-up of server systems—ready for every scenario. ASUS AI POD, a complete rack solution equipped with NVIDIA GB200 NVL72 platform, integrates GPUs, CPUs and switches in seamless, high-speed direct communication, enhancing the training of trillion-parameter LLMs and enabling real-time inference. It features the NVIDIA GB200 Grace Blackwell Superchip and fifth-generation NVIDIA NVLink technology, while offering both liquid-to-air and liquid-to-liquid cooling options to maximize AI computing performance.

CoolIT Systems (CoolIT), the world leader in liquid cooling systems for AI and high-performance computing, introduces the CHx1000, the world's highest-density liquid-to-liquid coolant distribution unit (CDU). Designed for mission-critical applications, the CHx1000 is purpose-built to cool the NVIDIA Blackwell platform and other demanding AI workloads where liquid cooling is now necessary.

"CoolIT created the CHx1000 to provide the high capacity and pressure delivery required to direct liquid cool NVIDIA Blackwell and future generations of high-performance AI accelerators," said Patrick McGinn, CoolIT's COO. "Besides exceptional performance, serviceability and reliability are central to the CHx1000's design. The single rack-sized unit is fully front and back serviceable with hot-swappable critical components. Precision coolant controls and multiple levels of redundancy provide for steady, uninterrupted operation."

According to The Information, NVIDIA's latest "Blackwell" processors are reportedly encountering significant thermal management issues in high-density server configurations, potentially affecting deployment timelines for major tech companies. The challenges emerge specifically in NVL72 GB200 racks housing 72 GB200 processors, which can consume up to 120 kilowatts of power per rack, weighting a "mere" 3,000 pounds (or about 1.5 tons). These thermal concerns have prompted NVIDIA to revisit and modify its server rack designs multiple times to prevent performance degradation and potential hardware damage. Hyperscalers like Google, Meta, and Microsoft, who rely heavily on NVIDIA GPUs for training their advanced language models, have allegedly expressed concerns about possible delays in their data center deployment schedules.

The thermal management issues follow earlier setbacks related to a design flaw in the Blackwell production process. The problem stemmed from the complex CoWoS-L packaging technology, which connects dual chiplets using RDL interposer and LSI bridges. Thermal expansion mismatches between various components led to warping issues, requiring modifications to the GPU's metal layers and bump structures. A company spokesperson characterized these modifications as part of the standard development process, noting that a new photomask resolved this issue. The Information states that mass production of the revised Blackwell GPUs began in late October, with shipments expected to commence in late January. However, these timelines are unconfirmed by NVIDIA, and some server makers like Dell confirmed that these GB200 NVL72 liquid-cooled systems are shipping now, not in January, with CoreWave GPU cloud provider as a customer. The original report could be using older information, as Dell is one of NVIDIA's most significant partners and among the first in the supply chain to gain access to new GPU batches.

We know that NVIDIA's latest "Blackwell" GPUs are fast, but how much faster are they over the previous generation "Hopper"? Thanks to the latest MLPerf Training v4.1 results, NVIDIA's HGX B200 Blackwell platform has demonstrated massive performance gains, measuring up to 2.2x improvement per GPU compared to its HGX H200 Hopper. The latest results, verified by MLCommons, reveal impressive achievements in large language model (LLM) training. The Blackwell architecture, featuring HBM3e high-bandwidth memory and fifth-generation NVLink interconnect technology, achieved double the performance per GPU for GPT-3 pre-training and a 2.2x boost for Llama 2 70B fine-tuning compared to the previous Hopper generation. Each benchmark system incorporated eight Blackwell GPUs operating at a 1,000 W TDP, connected via NVLink Switch for scale-up.

The network infrastructure utilized NVIDIA ConnectX-7 SuperNICs and Quantum-2 InfiniBand switches, enabling high-speed node-to-node communication for distributed training workloads. While previous Hopper-based systems required 256 GPUs to optimize performance for the GPT-3 175B benchmark, Blackwell accomplished the same task with just 64 GPUs, leveraging its larger HBM3e memory capacity and bandwidth. One thing to look out for is the upcoming GB200 NVL72 system, which promises even more significant gains past the 2.2x. It features expanded NVLink domains, higher memory bandwidth, and tight integration with NVIDIA Grace CPUs, complemented by ConnectX-8 SuperNIC and Quantum-X800 switch technologies. With faster switching and better data movement with Grace-Blackwell integration, we could see even more software optimization from NVIDIA to push the performance envelope.

During the 2024 RISC-V Summit in Santa Clara, California, NVIDIA was one of the presenting members. RISC-V, being a free and open-source instruction set architecture, is an interesting choice for many companies looking to develop custom solutions. NVIDIA designs accelerators for AI and graphics processing, all of which are equipped with up to tens of thousands of cores. To manage these cores, NVIDIA has developed a custom RISC-V processor called "NV-RISCV," which is a replacement for its predecessor "Falcon." Unlike Falcon, NV-RISCV is based on an open-source ISA and is customized much more deeply, with features like more customized caches and special instructions. Initially, the company reported better performance over its Falcon GPU System Processor (GSP), and NV-RISCV is now running in millions of NVIDIA chips.

Thanks to a post on X by Nick Brown, we learn that NVIDIA is shipping roughly one billion RISC-V cores in the year 2024. Each NVIDIA chip includes between 10 and 40 RISC-V cores, depending on the chip size and complexity. Some more complex designs, like GB200, require massive data coordination, meaning that more cores are needed to handle these requests and distribute them. This includes chip-to-chip interfaces, context switching, memory controller, camera handling, video codecs, display output, resource management, power management, and more. NVIDIA has developed a total of over 20 custom extensions for RISC-V cores, which all serve their specific use cases.

Micron Technology, Inc., today announced that its 9550 PCIe Gen 5 E1.S data center SSDs have been added to the NVIDIA recommended vendor list (RVL) for the NVIDIA GB200 NVL72 system and its derivatives. The GB200 NVL72 uses the GB200 Grace Blackwell Superchip to deliver rack-scale, energy-efficient AI infrastructure. The enablement of PCIe Gen 5 storage in the system makes the Micron 9550 SSD an ideal fit for optimizing performance and power efficiency in AI workloads like large-scale training of AI models, real-time trillion-parameter language model inference and high-performance computing (HPC) tasks.

Micron 9550 delivers world-class AI workload performance and power efficiency:
Compared with other industry offerings, the 9550 SSD delivers up to 34% higher throughput for NVIDIA Magnum IO GPUDirect (GDS) and up to 33% faster workload completion times in graph neural network (GNN) training with Big Accelerator Memory (BaM). The Micron 9550 SSD saves energy and sets new sustainability benchmarks by consuming 81% less SSD energy per 1 TB transferred than other SSD offerings with NVIDIA Magnum IO GDS and up to 43% lower SSD power in GNN training with BaM.

During the Open Compute Project (OCP) Summit 2024, Meta, one of the prime members of the OCP project, showed its NVIDIA "Blackwell" GB200 systems for its massive data centers. We previously covered Microsoft's Azure server rack with GB200 GPUs featuring one-third of the rack space for computing and two-thirds for cooling. A few days later, Google showed off its smaller GB200 system, and today, Meta is showing off its GB200 system—the smallest of the bunch. To train a dense transformer large language model with 405B parameters and a context window of up to 128k tokens, like the Llama 3.1 405B, Meta must redesign its data center infrastructure to run a distributed training job on two 24,000 GPU clusters. That is 48,000 GPUs used for training a single AI model.

Called "Catalina," it is built on the NVIDIA Blackwell platform, emphasizing modularity and adaptability while incorporating the latest NVIDIA GB200 Grace Blackwell Superchip. To address the escalating power requirements of GPUs, Catalina introduces the Orv3, a high-power rack capable of delivering up to 140kW. The comprehensive liquid-cooled setup encompasses a power shelf supporting various components, including a compute tray, switch tray, the Orv3 HPR, Wedge 400 fabric switch with 12.8 Tbps switching capacity, management switch, battery backup, and a rack management controller. Interestingly, Meta also upgraded its "Grand Teton" system for internal usage, such as deep learning recommendation models (DLRMs) and content understanding with AMD Instinct MI300X. Those are used to inference internal models, and MI300X appears to provide the best performance per Dollar for inference. According to Meta, the computational demand stemming from AI will continue to increase exponentially, so more NVIDIA and AMD GPUs is needed, and we can't wait to see what the company builds.

SK hynix is showcasing its leading AI and data center memory products at the 2024 Open Compute Project (OCP) Global Summit held October 15-17 in San Jose, California. The annual summit brings together industry leaders to discuss advancements in open source hardware and data center technologies. This year, the event's theme is "From Ideas to Impact," which aims to foster the realization of theoretical concepts into real-world technologies.

In addition to presenting its advanced memory products at the summit, SK hynix is also strengthening key industry partnerships and sharing its AI memory expertise through insightful presentations. This year, the company is holding eight sessions—up from five in 2023—on topics including HBM and CMS.

MSI, a leading global provider of high-performance server solutions, proudly announced it is showcasing new AI servers powered by the NVIDIA MGX platform—designed to address the increasing demand for scalable, energy-efficient AI workloads in modern data centers—at the OCP Global Summit 2024, booth A6. This collaboration highlights MSI's continued commitment to advancing server solutions, focusing on cutting-edge AI acceleration and high-performance computing (HPC).

The NVIDIA MGX platform offers a flexible architecture that enables MSI to deliver purpose-built solutions optimized for AI, HPC, and LLMs. By leveraging this platform, MSI's AI server solutions provide exceptional scalability, efficiency, and enhanced GPU density—key factors in meeting the growing computational demands of AI workloads. Tapping into MSI's engineering expertise and NVIDIA's advanced AI technologies, these AI servers based on the MGX architecture deliver unparalleled compute power, positioning data centers to maximize performance and power efficiency while paving the way for the future of AI-driven infrastructure.

At Lenovo Tech World 2024, we announced new Supercomputing servers for HPC and AI workloads. These new water-cooled servers use the latest processor and accelerator technology from Intel and NVIDIA.

ThinkSystem SC750 V4
Engineered for large-scale cloud infrastructures and High Performance Computing (HPC), the Lenovo ThinkSystem SC750 V4 Neptune excels in intensive simulations and complex modeling. It's designed to handle technical computing, grid deployments, and analytics workloads in various fields such as research, life sciences, energy, engineering, and financial simulation.

Last week, we got a preview of Microsoft's Azure production-ready NVIDIA "Blackwell" GB200 system, showing that only a third of the rack that goes in the data center is actually holding the compute elements, with the other two-thirds holding the cooling compartment to cool down the immense heat output from tens of GB200 GPUs. Today, Google is showing off a part of its own infrastructure ahead of the Google Cloud App Dev & Infrastructure Summit, taking place on October 30, digitally as an event. Shown below are two racks standing side by side, connecting NVIDIA "Blackwell" GB200 NVL cards with the rest of the Google infrastructure. Unlike Microsoft Azure, Google Cloud uses a different data center design in its facilities.

There is one rack with power distribution units, networking switches, and cooling distribution units, all connected to the compute rack, which houses power supplies, GPUs, and CPU servers. Networking equipment is present, and it connects to Google's "global" data center network, which is Google's own data center fabric. We are not sure what is the fabric connection of choice between these racks; as for optimal performance, NVIDIA recommends InfiniBand (Mellanox acquisition). However, given that Google's infrastructure is set up differently, there may be Ethernet switches present. Interestingly, Google's design of GB200 racks differs from Azure's, as it uses additional rack space to distribute the coolant to its local heat exchangers, i.e., coolers. We are curious to see if Google releases more information on infrastructure, as it has been known as the infrastructure king because of its ability to scale and keep everything organized.

Supermicro, Inc., a Total IT Solution Provider for AI, Cloud, Storage, and 5G/Edge, is accelerating the industry's transition to liquid-cooled data centers with the NVIDIA Blackwell platform to deliver a new paradigm of energy-efficiency for the rapidly heightened energy demand of new AI infrastructures. Supermicro's industry-leading end-to-end liquid-cooling solutions are powered by the NVIDIA GB200 NVL72 platform for exascale computing in a single rack and have started sampling to select customers for full-scale production in late Q4. In addition, the recently announced Supermicro X14 and H14 4U liquid-cooled systems and 10U air-cooled systems are production-ready for the NVIDIA HGX B200 8-GPU system.

"We're driving the future of sustainable AI computing, and our liquid-cooled AI solutions are rapidly being adopted by some of the most ambitious AI Infrastructure projects in the world with over 2000 liquid-cooled racks shipped since June 2024," said Charles Liang, president and CEO of Supermicro. "Supermicro's end-to-end liquid-cooling solution, with the NVIDIA Blackwell platform, unlocks the computational power, cost-effectiveness, and energy-efficiency of the next generation of GPUs, such as those that are part of the NVIDIA GB200 NVL72, an exascale computer contained in a single rack. Supermicro's extensive experience in deploying liquid-cooled AI infrastructure, along with comprehensive on-site services, management software, and global manufacturing capacity, provides customers a distinct advantage in transforming data centers with the most powerful and sustainable AI solutions."

To drive the development of open, efficient and scalable data center technologies, NVIDIA today announced that it has contributed foundational elements of its NVIDIA Blackwell accelerated computing platform design to the Open Compute Project (OCP) and broadened NVIDIA Spectrum-X support for OCP standards.

At this year's OCP Global Summit, NVIDIA will be sharing key portions of the NVIDIA GB200 NVL72 system electro-mechanical design with the OCP community — including the rack architecture, compute and switch tray mechanicals, liquid-cooling and thermal environment specifications, and NVIDIA NVLink cable cartridge volumetrics — to support higher compute density and networking bandwidth.

Western Digital Corp. today announced that its PCIe Gen 5 DC SN861 E.1S enterprise-class NVMe SSDs have been certified to support the NVIDIA GB200 NVL72 rack-scale system.

The rapid rise of AI, ML, and large language models (LLMs) is creating a challenge for companies with two opposing forces. Data generation and consumption are accelerating, while organizations face pressure to quickly derive value from this data. Performance, scalability, and efficiency are essential for AI technology stacks as storage demands rise. Certified to be compatible with the GB200 NVL72 system, Western Digital's enterprise SSD addresses the growing needs of the AI market for high-speed accelerated computing combined with low latency to serve compute-intensive AI environments.