NVIDIA's upcoming GB300 NVL72 "Blackwell Ultra" rack-scale systems are reportedly going to get a humanoid robot assembly, according to sources close to Reuters. As readers are aware, most of the traditional manufacturing processes in silicon manufacturing, PCB manufacturing, and server manufacturing are automated, requiring little to no human intervention. However, rack-scale systems required humans for final assembly up until now. It appears that Foxconn and NVIDIA have made plans to open up the first AI-powered humanoid robot assembly plant in Houston, Texas. The central plan is that, in the coming months as the plant is completed, humanoid robots will take over the final assembly process entirely removing humans from the manufacturing loop.

And this is not a bad thing. Since server assembly typically requires lifting heavy server racks throughout the day, the humanoid robot system will aid humans by doing the hard work, thereby saving workers from excessive labor. Initially, humans will oversee these robots in their operations, with fully autonomous factories expected later on. The human element here will primarily involve inspecting the work. NVIDIA has been laying the groundwork for humanoid robots for some time, as the company has developed NVIDIA Isaac, a comprehensive CUDA-accelerated platform designed for humanoid robots. As models from Agility Robotics, Boston Dynamics, Fourier, Foxlink, Galbot, Mentee Robotics, NEURA Robotics, General Robotics, Skild AI, and XPENG require models that are aware of their surroundings, NVIDIA created Isaac GR00T N1, the world's first open humanoid robot foundation model, available for anyone to use and finetune.

The integration of quantum processors into tomorrow's supercomputers promises to dramatically expand the problems that can be addressed with compute—revolutionizing industries including drug and materials development.

In addition to being part of the vision for tomorrow's hybrid quantum-classical supercomputers, accelerated computing is dramatically advancing the work quantum researchers and developers are already doing to achieve that vision. And in today's development of tomorrow's quantum technology, NVIDIA GB200 NVL72 systems and their fifth-generation multinode NVIDIA NVLink interconnect capabilities have emerged as the leading architecture.

PEGATRON, a globally recognized Design, Manufacturing, and Service (DMS) provider, is showcasing its latest AI server solutions at GTC Paris 2025. Built on NVIDIA Blackwell architecture, PEGATRON's cutting-edge systems are tailored for AI training, reasoning, and enterprise-scale deployment.

NVIDIA GB300 NVL72
At the forefront is the RA4802-72N2, built on the NVIDIA GB300 NVL72 rack system, featuring 72 NVIDIA Blackwell Ultra GPUs and 36 NVIDIA Grace CPUs. Designed for AI factories, it boosts output by up to 50X. PEGATRON's in-house developed Coolant Distribution Unit (CDU) delivers 310 kW of cooling capacity with redundant hot-swappable pumps, ensuring performance and reliability for mission-critical workloads.

Supermicro, Inc., a Total IT Solution Provider for AI, Cloud, Storage, and 5G/Edge, is announcing an expansion of the industry's broadest portfolio of solutions designed for NVIDIA Blackwell Architecture to the European market. The introduction of more than 30 solutions reinforces Supermicro's industry leadership by providing the most comprehensive and efficient solution stack for NVIDIA HGX B200, GB200 NVL72, and RTX PRO 6000 Blackwell Server Edition deployments, enabling rapid time-to-online for European enterprise AI factories across any environment. Through close collaboration with NVIDIA, Supermicro's solution stack enables the deployment of NVIDIA Enterprise AI Factory validated design and supports the upcoming introduction of NVIDIA Blackwell Ultra solutions later this year, including NVIDIA GB300 NVL72 and HGX B300.

"With our first-to-market advantage and broad portfolio of NVIDIA Blackwell solutions, Supermicro is uniquely positioned to meet the accelerating demand for enterprise AI infrastructure across Europe," said Charles Liang, president and CEO of Supermicro. "Our collaboration with NVIDIA, combined with our global manufacturing capabilities and advanced liquid cooling technologies, enables European organizations to deploy AI factories with significantly improved efficiency and reduced implementation timelines. We're committed to providing the complete solution stack enterprises need to successfully scale their AI initiatives."

ASUS today joined GTC Paris at VivaTech 2025 as a Gold Sponsor, highlighting its latest portfolio of AI infrastructure solutions and reinforcing its commitment to advancing the AI Factory vision with a full range of NVIDIA Blackwell Ultra solutions, delivering breakthrough performance from large-scale datacenter to personal desktop.

ASUS is also excited to announce a transformative partnership milestone in its partnership with Nebius. Together, the two companies are enabling a new era of AI innovation built on NVIDIA's advanced platforms. Building on the success of the NVIDIA GB200 NVL72 platform deployment, ASUS and Nebius are now moving forward with strategic collaborations featuring the next-generation NVIDIA GB300 NVL72 platform. This ongoing initiative underscores ASUS's role as a key enabler in AI infrastructure, committed to delivering scalable, high-performance solutions that help enterprises accelerate AI adoption and innovation.

NVIDIA is working with companies worldwide to build out AI factories—speeding the training and deployment of next-generation AI applications that use the latest advancements in training and inference. The NVIDIA Blackwell architecture is built to meet the heightened performance requirements of these new applications. In the latest round of MLPerf Training—the 12th since the benchmark's introduction in 2018—the NVIDIA AI platform delivered the highest performance at scale on every benchmark and powered every result submitted on the benchmark's toughest large language model (LLM)-focused test: Llama 3.1 405B pretraining.

The NVIDIA platform was the only one that submitted results on every MLPerf Training v5.0 benchmark—underscoring its exceptional performance and versatility across a wide array of AI workloads, spanning LLMs, recommendation systems, multimodal LLMs, object detection and graph neural networks. The at-scale submissions used two AI supercomputers powered by the NVIDIA Blackwell platform: Tyche, built using NVIDIA GB200 NVL72 rack-scale systems, and Nyx, based on NVIDIA DGX B200 systems. In addition, NVIDIA collaborated with CoreWeave and IBM to submit GB200 NVL72 results using a total of 2,496 Blackwell GPUs and 1,248 NVIDIA Grace CPUs.

NVIDIA today reported revenue for the first quarter ended April 27, 2025, of $44.1 billion, up 12% from the previous quarter and up 69% from a year ago.

On April 9, 2025, NVIDIA was informed by the U.S. government that a license is required for exports of its H20 products into the China market. As a result of these new requirements, NVIDIA incurred a $4.5 billion charge in the first quarter of fiscal 2026 associated with H20 excess inventory and purchase obligations as the demand for H20 diminished. Sales of H20 products were $4.6 billion for the first quarter of fiscal 2026 prior to the new export licensing requirements. NVIDIA was unable to ship an additional $2.5 billion of H20 revenue in the first quarter.

AI infrastructure buildout is pushing data center desings beyond the limits of conventional power delivery. Traditional in-rack 54 V DC distribution was designed for racks drawing tens of kilowatts and cannot scale to the megawatt requirements for next-generation AI facilities. At GTC and Computex 2025, NVIDIA introduced a comprehensive solution: an end-to-end 800-volt high-voltage DC (HVDC) infrastructure that will support 1-megawatt AI racks and beyond, with deployments planned to begin in 2027. Cooling and cabling already place immense strain on rack designs. NVIDIA's current GB200 and GB300 NVL72 systems can draw up to 132 kW per rack—significantly more than the 50 to 80 kW that most data halls were built to handle. If rack power rises to the 700 kW to 1 MW range under a 54 V distribution, it would require roughly 64 U of chassis space devoted solely to copper busbars, which is almost the entire rack, and about 200 kg of copper per rack. For a 1 GW installation, that adds up to nearly half a million metric tons of copper.

NVIDIA's 800 V HVDC architecture eliminates multiple AC-to-DC and DC-to-DC conversion stages by consolidating them into a single grid-edge rectifier. From a 13.8 kV AC feed, power is converted directly to 800 V DC and then routed through row-level busways to each rack. Compact DC-DC modules in the rack step down the voltage for the GPUs. Fewer power supply units mean fewer fans, lower heat output, and a simpler electrical footprint. Beyond scalability, 800 V HVDC offers up to 5 percent gains in end-to-end efficiency and a 45 percent reduction in copper usage. This results in lower electricity costs and reduced infrastructure buildout costs. To drive industry adoption, NVIDIA has partnered with leaders across the power ecosystem. Silicon and power-electronics specialists such as Infineon, MPS, Navitas, ROHM, STMicroelectronics, and Texas Instruments are contributing components. System integrators, including Delta, Flex Power, Lead Wealth, LiteOn, and Megmeet, are developing power shelves. Data-center infrastructure companies Eaton, Schneider Electric, and Vert iv are standardizing protective devices at every boundary from the power room to the rack. Below you can compare the traditional rack system in the top to the newly proposed variation in the middle and the bottom part of the image. Thanks to HardwareLuxx, we can even see how it looks in reality.

Over a century ago, Henry Ford pioneered the mass production of cars and engines to provide transportation at an affordable price. Today, the technology industry manufactures the engines for a new kind of factory—those that produce intelligence. As companies and countries increasingly focus on AI, and move from experimentation to implementation, the demand for AI technologies continues to grow exponentially. Leading system builders are racing to ramp up production of the servers for AI factories—the engines of AI factories—to meet the world's exploding demand for intelligence and growth. Dell Technologies is a leader in this renaissance. Dell and NVIDIA have partnered for decades and continue to push the pace of innovation. In its last earnings call, Dell projected that its AI server business will grow at least $15 billion this year.

"We're on a mission to bring AI to millions of customers around the world," said Michael Dell, chairman and chief executive officer, Dell Technologies, in a recent announcement at Dell Technologies World. "With the Dell AI Factory with NVIDIA, enterprises can manage the entire AI lifecycle across use cases, from training to deployment, at any scale." The latest Dell AI servers, powered by NVIDIA Blackwell, offer up to 50x more AI reasoning inference output and 5x improvement in throughput compared with the Hopper platform. Customers use them to generate tokens for new AI applications that will help solve some of the world's biggest challenges, from disease prevention to advanced manufacturing.

The world's top provider of AI-centric infrastructure, Dell Technologies, announces innovations across the Dell AI Factory with NVIDIA - all designed to help enterprises accelerate AI adoption and achieve faster time to value.

Why it matters
As enterprises make AI central to their strategy and progress from experimentation to implementation, their demand for accessible AI skills and technologies grows exponentially. Dell and NVIDIA continue the rapid pace of innovation with updates to the Dell AI Factory with NVIDIA, including robust AI infrastructure, solutions and services that streamline the path to full-scale implementation.

NVIDIA is at Computex 2025, with CEO Jensen Huang leading a keynote address. We go live with the various announcements. Jensen takes centerstage NVIDIA began with the announcement is the Grace Blackwell AI inferencing system. NVIDIA began the keynote showing off its latest GeForce RTX 5060 desktop and mobile graphics cards. Each node has the same compute throughput as the Sierra compute from 2018. The NVLink Spine is a backend interconnects 72 GB300 node. 1 NVLink spine moves more traffic than the entire Internet. Each NVL72 rack pulls 128 kVA of power. NVIDIA says the terminology "AI factory" and not "datacenter" is suitable because each of these pulls 100s of megawatts of power to offer compute power of hundreds of datacenters. NVIDIA is now describing the manufacturing process behind Blackwell GPU.

AI is creating value for everyone—from researchers in drug discovery to quantitative analysts navigating financial market changes. The faster an AI system can produce tokens, a unit of data used to string together outputs, the greater its impact. That's why AI factories are key, providing the most efficient path from "time to first token" to "time to first value." AI factories are redefining the economics of modern infrastructure. They produce intelligence by transforming data into valuable outputs—whether tokens, predictions, images, proteins or other forms—at massive scale.

They help enhance three key aspects of the AI journey—data ingestion, model training and high-volume inference. AI factories are being built to generate tokens faster and more accurately, using three critical technology stacks: AI models, accelerated computing infrastructure and enterprise-grade software. Read on to learn how AI factories are helping enterprises and organizations around the world convert the most valuable digital commodity—data—into revenue potential.

NVIDIA today received multiple accolades at COMPUTEX's Best Choice Awards, in recognition of innovation across the company. The NVIDIA GeForce RTX 5090 GPU won the Gaming and Entertainment category award; the NVIDIA Quantum-X Photonics InfiniBand switch system won the Networking and Communication category award; NVIDIA DGX Spark won the Computer and System category award; and the NVIDIA GB200 NVL72 system and NVIDIA Cosmos world foundation model development platform won Golden Awards. The awards recognize the outstanding functionality, innovation and market promise of technologies in each category. Jensen Huang, founder and CEO of NVIDIA, will deliver a keynote at COMPUTEX on Monday, May 19, at 11 a.m. Taiwan time.

GB200 NVL72 and NVIDIA Cosmos Go Gold
NVIDIA GB200 NVL72 and NVIDIA Cosmos each won Golden Awards. The NVIDIA GB200 NVL72 system connects 36 NVIDIA Grace CPUs and 72 NVIDIA Blackwell GPUs in a rack-scale design. It delivers 1.4 exaflops of AI performance and 30 terabytes of fast memory, as well as 30x faster real-time trillion-parameter large language model inference with 25x energy efficiency compared with the NVIDIA H100 GPU. By design, the GB200 NVL72 accelerates the most compute-intensive AI and high-performance computing workloads, including AI training and data processing for engineering design and simulation. NVIDIA Cosmos accelerates physical AI development by enabling developers to build and deploy world foundation models with unprecedented speed and scale.

AMD's Instinct MI325X accelerator has struggled to gain traction with large customers, according to extensive data from SemiAnalysis. Launched in Q2 2025, the MI325X arrived roughly nine months after NVIDIA's H200 and concurrently with NVIDIA's "Blackwell" mass-production roll-out. That timing proved unfavourable, as many buyers opted instead for Blackwell's superior cost-per-performance ratio. Early interest from Microsoft in 2024 failed to translate into repeat orders. After the initial test purchases, Microsoft did not place any further commitments. In response, AMD reduced its margin expectations in an effort to attract other major clients. Oracle and a handful of additional hyperscalers have since expressed renewed interest, but these purchases remain modest compared with NVIDIA's volume.

A fundamental limitation of the MI325X is its eight-GPU scale-up capacity. By contrast, NVIDIA's rack-scale GB200 NVL72 supports up to 72 GPUs in a single cluster. For large-scale AI inference and frontier-level reasoning workloads, that difference is decisive. AMD positioned the MI325X against NVIDIA's air-cooled HGX B200 NVL8 and HGX B300 NVL16 modules. Even in that non-rack-scale segment, NVIDIA maintains an advantage in both raw performance and total-cost-of-ownership efficiency. Nonetheless, there remains potential for the MI325X in smaller-scale deployments that do not require extensive GPU clusters. Smaller model inference should be sufficient for eight GPU clusters, where lots of memory bandwidth and capacity are the primary needs. AMD continues to improve its software ecosystem and maintain competitive pricing, so AI labs developing mid-sized AI models may find the MI325X appealing.

Oracle has stood up and optimized its first wave of liquid-cooled NVIDIA GB200 NVL72 racks in its data centers. Thousands of NVIDIA Blackwell GPUs are now being deployed and ready for customer use on NVIDIA DGX Cloud and Oracle Cloud Infrastructure (OCI) to develop and run next-generation reasoning models and AI agents. Oracle's state-of-the-art GB200 deployment includes high-speed NVIDIA Quantum-2 InfiniBand and NVIDIA Spectrum-X Ethernet networking to enable scalable, low-latency performance, as well as a full stack of software and database integrations from NVIDIA and OCI.

OCI, one of the world's largest and fastest-growing cloud service providers, is among the first to deploy NVIDIA GB200 NVL72 systems. The company has ambitious plans to build one of the world's largest Blackwell clusters. OCI Superclusters will scale beyond 100,000 NVIDIA Blackwell GPUs to meet the world's skyrocketing need for inference tokens and accelerated computing. The torrid pace of AI innovation continues as several companies including OpenAI have released new reasoning models in the past few weeks.

Traditionally, data centers have relied on air cooling—where mechanical chillers circulate chilled air to absorb heat from servers, helping them maintain optimal conditions. But as AI models increase in size, and the use of AI reasoning models rises, maintaining those optimal conditions is not only getting harder and more expensive—but more energy-intensive. While data centers once operated at 20 kW per rack, today's hyperscale facilities can support over 135 kW per rack, making it an order of magnitude harder to dissipate the heat generated by high-density racks. To keep AI servers running at peak performance, a new approach is needed for efficiency and scalability.

One key solution is liquid cooling—by reducing dependence on chillers and enabling more efficient heat rejection, liquid cooling is driving the next generation of high-performance, energy-efficient AI infrastructure. The NVIDIA GB200 NVL72 and the NVIDIA GB300 NVL72 are rack-scale, liquid-cooled systems designed to handle the demanding tasks of trillion-parameter large language model inference. Their architecture is also specifically optimized for test-time scaling accuracy and performance, making it an ideal choice for running AI reasoning models while efficiently managing energy costs and heat.

Huawei announced its CloudMatrix 384 system super node, which the company touts as its own domestic alternative to NVIDIA's GB200 NVL72 system, with more overall system performance but worse per-chip performance and higher power consumption. While NVIDIA's GB200 NVL72 uses 36 Grace CPUs paired with 72 "Blackwell" GB200 GPUs, the Huawei CloudMatrix 384 system employs 384 Huawei Ascend 910C accelerators to beat NVIDIA's GB200 NVL72 system. It takes roughly five times more Ascend 910C accelerators to deliver nearly twice the GB200 NVL system performance, which is not good on per-accelerator bias, but excellent on per-system level of deployment. SemiAnalysis argues that Huawei is a generation behind in chip performance but ahead of NVIDIA in scale-up system design and deployment.

When you look at individual chips, NVIDIA's GB200 NVL72 clearly outshines Huawei's Ascend 910C, delivering over three times the BF16 performance (2,500 TeraFLOPS vs. 780 TeraFLOPS), more on‑chip memory (192 GB vs. 128 GB), and faster bandwidth (8 TB/s vs. 3.2 TB/s). In other words, NVIDIA has the raw power and efficiency advantage at the chip level. But flip the switch to the system level, and Huawei's CloudMatrix CM384 takes the lead. It cranks out 1.7× the overall PetaFLOPS, packs in 3.6× more total HBM capacity, and supports over five times the number of GPUs and the associated bandwidth of NVIDIA's NVL72 cluster. However, that scalability does come with a trade‑off, as Huawei's setup draws nearly four times more total power. A single GB200 NVL72 draws 145 kW of power, while a single Huawei CloudMatrix 384 draws ~560 kW. So, NVIDIA is your go-to if you need peak efficiency in a single GPU. If you're building a massive AI supercluster where total throughput and interconnect speed matter most, Huawei's solution actually makes a lot of sense. Thanks to its all-to-all topology, Huawei has delivered an AI training and inference system worth purchasing. When SMIC, the maker of Huawei's chips, gets to a more advanced manufacturing node, the efficiency of these systems will also increase.

CoreWeave today became one of the first cloud providers to bring NVIDIA GB200 NVL72 systems online for customers at scale, and AI frontier companies Cohere, IBM and Mistral AI are already using them to train and deploy next-generation AI models and applications. CoreWeave, the first cloud provider to make NVIDIA Grace Blackwell generally available, has already shown incredible results in MLPerf benchmarks with NVIDIA GB200 NVL72 - a powerful rack-scale accelerated computing platform designed for reasoning and AI agents. Now, CoreWeave customers are gaining access to thousands of NVIDIA Blackwell GPUs.

"We work closely with NVIDIA to quickly deliver to customers the latest and most powerful solutions for training AI models and serving inference," said Mike Intrator, CEO of CoreWeave. "With new Grace Blackwell rack-scale systems in hand, many of our customers will be the first to see the benefits and performance of AI innovators operating at scale."

In the latest MLPerf Inference V5.0 benchmarks, which reflect some of the most challenging inference scenarios, the NVIDIA Blackwell platform set records - and marked NVIDIA's first MLPerf submission using the NVIDIA GB200 NVL72 system, a rack-scale solution designed for AI reasoning. Delivering on the promise of cutting-edge AI takes a new kind of compute infrastructure, called AI factories. Unlike traditional data centers, AI factories do more than store and process data - they manufacture intelligence at scale by transforming raw data into real-time insights. The goal for AI factories is simple: deliver accurate answers to queries quickly, at the lowest cost and to as many users as possible.

The complexity of pulling this off is significant and takes place behind the scenes. As AI models grow to billions and trillions of parameters to deliver smarter replies, the compute required to generate each token increases. This requirement reduces the number of tokens that an AI factory can generate and increases cost per token. Keeping inference throughput high and cost per token low requires rapid innovation across every layer of the technology stack, spanning silicon, network systems and software.

Supermicro, Inc., a Total IT Solution Provider for AI, Cloud, Storage, and 5G/Edge, is announcing new systems and rack solutions powered by the NVIDIA's Blackwell Ultra platform, featuring the NVIDIA HGX B300 NVL16 and NVIDIA GB300 NVL72 platforms. Supermicro and NVIDIA's new AI solutions strengthen leadership in AI by delivering breakthrough performance for the most compute-intensive AI workloads, including AI reasoning, agentic AI, and video inference applications.

"At Supermicro, we are excited to continue our long-standing partnership with NVIDIA to bring the latest AI technology to market with the NVIDIA Blackwell Ultra Platforms," said Charles Liang, president and CEO, Supermicro. "Our Data Center Building Block Solutions approach has streamlined the development of new air and liquid-cooled systems, optimized to the thermals and internal topology of the NVIDIA HGX B300 NVL16 and GB300 NVL72. Our advanced liquid-cooling solution delivers exceptional thermal efficiency, operating with 40℃ warm water in our 8-node rack configuration, or 35℃ warm water in double-density 16-node rack configuration, leveraging our latest CDUs. This innovative solution reduces power consumption by up to 40% while conserving water resources, providing both environmental and operational cost benefits for enterprise data centers."

Giga Computing, a subsidiary of GIGABYTE and an industry leader in generative AI servers and advanced cooling technologies, today announced participation at NVIDIA GTC 2025 to bring to the market the best in GPU-based solutions for generative AI, media acceleration, and large language models (LLM). To this end, GIGABYTE booth #1409 at NVIDIA GTC showcases a rack-scale turnkey AI solution, GIGAPOD, that offers both air and liquid-cooling designs for the NVIDIA HGX B300 NVL16 system. Also, on display at the booth is a compute node from the newly announced NVIDIA GB300 NVL72 rack-scale solution. And for modularized compute architecture are two servers supporting the newly announced NVIDIA RTX PRO 6000 Blackwell Server Edition.

Complete AI solution - GIGAPOD
With the depth of expertise in hardware and system design, Giga Computing has combined infrastructure hardware, platform software, and architecting service to deliver scalable units composed of GIGABYTE GPU servers with NVIDIA GPU baseboards, while running GIGABYTE POD Manager, a powerful software suite designed to enhance operational efficiency, streamline management, and optimize resource utilization. GIGAPOD's scalable unit is designed for either nine air-cooled racks or five liquid-cooled racks. Giga Computing offers two approaches for the same goal, one powerful GPU cluster using NVIDIA HGX Hopper and Blackwell GPU platforms at scale to meet demand for all AI data centers.

NVIDIA today announced it is building a Boston-based research center to provide cutting-edge technologies to advance quantum computing. The NVIDIA Accelerated Quantum Research Center, or NVAQC, will integrate leading quantum hardware with AI supercomputers, enabling what is known as accelerated quantum supercomputing. The NVAQC will help solve quantum computing's most challenging problems, ranging from qubit noise to transforming experimental quantum processors into practical devices.

Leading quantum computing innovators, including Quantinuum, Quantum Machines and QuEra Computing, will tap into the NVAQC to drive advancements through collaborations with researchers from leading universities, such as the Harvard Quantum Initiative in Science and Engineering (HQI) and the Engineering Quantum Systems (EQuS) group at the Massachusetts Institute of Technology (MIT).

Secular growth of AI is built on the foundation of high-performance, high-bandwidth memory solutions. These high-performing memory solutions are critical to unlock the capabilities of GPUs and processors. Micron Technology, Inc., today announced it is the world's first and only memory company shipping both HBM3E and SOCAMM (small outline compression attached memory module) products for AI servers in the data center. This extends Micron's industry leadership in designing and delivering low-power DDR (LPDDR) for data center applications.

Micron's SOCAMM, a modular LPDDR5X memory solution, was developed in collaboration with NVIDIA to support the NVIDIA GB300 Grace Blackwell Ultra Superchip. The Micron HBM3E 12H 36 GB is also designed into the NVIDIA HGX B300 NVL16 and GB300 NVL72 platforms, while the HBM3E 8H 24 GB is available for the NVIDIA HGX B200 and GB200 NVL72 platforms. The deployment of Micron HBM3E products in NVIDIA Hopper and NVIDIA Blackwell systems underscores Micron's critical role in accelerating AI workloads.

NVIDIA today announced the next evolution of the NVIDIA Blackwell AI factory platform, NVIDIA Blackwell Ultra—paving the way for the age of AI reasoning. NVIDIA Blackwell Ultra boosts training and test-time scaling inference—the art of applying more compute during inference to improve accuracy—to enable organizations everywhere to accelerate applications such as AI reasoning, agentic AI and physical AI.

Built on the groundbreaking Blackwell architecture introduced a year ago, Blackwell Ultra includes the NVIDIA GB300 NVL72 rack-scale solution and the NVIDIA HGX B300 NVL16 system. The GB300 NVL72 delivers 1.5x more AI performance than the NVIDIA GB200 NVL72, as well as increases Blackwell's revenue opportunity by 50x for AI factories, compared with those built with NVIDIA Hopper.

ASUS today announced its showcase of comprehensive AI infrastructure solutions at CloudFest 2025, bringing together cutting-edge hardware powered by Intel Xeon 6 processors, NVIDIA GPUs and AMD EPYC processors. The company will also highlight its integrated software platforms, reinforcing its position as a total AI solution provider for enterprises seeking seamless AI deployments from edge to cloud.

Intel Xeon 6-based AI solutions and Gaudi 3 Acceleration for generative AI inferencing and fine tuning training
ASUS Intel Xeon 6-based servers leverage the Data Center Modular Hardware System (DC-MHS) architecture, providing unparalleled scalability, cost-efficiency and simplified maintenance. ASUS will showcase a comprehensive Intel Xeon 6 family of processors at CloudFest 2025, including the RS700-E12, RS720Q-E12. and ESC8000-E12P-series servers. The ESC800-E12P-series servers will debut the Intel Gaudi 3 AI accelerator PCIe card. This lineup underscores the ASUS commitment to delivering comprehensive AI solutions that integrate cutting-edge hardware with enterprise-grade software platforms for seamless, scalable AI deployments, highlighting Intel's latest innovations for high-performance AI training, inference, and cloud-native workloads.