President Trump's administration has announced that NVIDIA's H20 AI chip will require a special export license for any shipment to China, Hong Kong, or Macau for the indefinite future. The Commerce Department delivered the news to NVIDIA on April 14, 2025, citing worries that the H20 could be redirected into Chinese supercomputers with potential military applications. NVIDIA designed the H20 specifically to comply with earlier US curbs by scaling back performance from its flagship H100 model. The H20 features 96 GB of HBM3 memory running at up to 4.0 TB/s, delivers roughly 296 TeraFLOPS of mixed‑precision compute power, and offers a performance density of about 2.9 TeraFLOPS per die. Its single‑precision (FP32) throughput is around 74 TeraFLOPS, with FP16 performance reaching approximately 148 TeraFLOPS. In a regulatory filing on April 15, NVIDIA warned that it will record about $5.5 billion in writedowns this quarter related to H20 inventory and purchase commitments now blocked by the license requirement.

Shares of NVIDIA fell roughly 6 percent in after‑hours trading on April 15, triggering a wider sell‑off in semiconductor stocks from the US to Japan. South Korea's Samsung and SK Hynix each slid about 3 percent, while AMD also dropped on concerns about broader chip‑export curbs. Analysts at Bloomberg Intelligence project that, if the restrictions persist, NVIDIA's China‑related data center revenue could shrink to low‑ or mid‑single digits as a percentage of total sales, down from roughly 13 percent in fiscal 2024. Chinese AI players such as Huawei stand to gain as customers seek alternative inference accelerators. Commerce Secretary Howard Lutnick has pledged to maintain a tough stance on chip exports to China even as NVIDIA commits up to $500 billion in US AI infrastructure investments over the next four years. Everyone is now watching closely to see whether any H20 export licenses are approved and how long the ban might remain in place.

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."

Acer today announced the expansion of its mainstream Nitro gaming line with the launch of the new Acer Nitro AI laptops and the Nitro 20 desktop. The Nitro range brings a full-package gaming experience and great value for gamers and content creators, combining robust processing power, essential computing features, and more without breaking the bank. The addition of slim laptops and a compact Windows PC option also support users with limited space for their gaming set-ups.

The Nitro AI laptops are Copilot+ PCs, powered by AMD Ryzen AI 300 Series processors and equipped with the game-changing NVIDIA GeForce RTX 50 Series Laptop GPUs to make more powerful AI rendering capabilities more accessible. The Nitro 20 desktop also utilizes AI processors and up to an NVIDIA GeForce RTX 5060 GPU, focusing on better energy efficiency with a smaller footprint.

ASUS today announced the latest additions to its comprehensive range of business laptops - ExpertBook P1 (PM1403 and PM1503). Aimed at administrators and budget-conscious professionals who require essential computing services without compromise, and available in 14-inch or 15.6-inch FHD options, the new ExpertBook P1 models blend effective performance with everyday functionality, housed in a practical design that delivers where it counts. The compact and elegant ExpertBook P1 series starts at a lightweight 1.4 kg and features an elegant new design that unlocks impressive efficiency to supercharge daily productivity, empowered by Copilot in Windows - quickly accessed via a single tap of the dedicated Copilot key.

The new ExpertBook P1 laptops are engineered for superb performance, powered by up to an AMD Ryzen 7 processor and offering up to 1 TB of storage with up to dual SSDs for fast operation. They also feature a built-in fingerprint sensor and TPM 2.0 chip to protect privacy and business data - ensuring that ExpertBook P1 is a trusted, reliable traveling companion for modern workflows.

SaxonQ, developer of the first mobile quantum computer, and Quantum Machines, the leading provider of advanced hybrid quantum-classical control solutions, announced today a milestone demonstration of real-time quantum computing on SaxonQ's mobile quantum computer at Hannover Messe 2025. The live demonstrations included a quantum chemical calculation of H₂ energy levels and basic real-time image recognition, marking the first time anyone has shown such applications running on a portable room-temperature quantum computer publicly, demonstrate the potential of mobile quantum computing outside laboratory conditions.

"Developing a mobile quantum computer that runs in real-world environments—without cryogenic cooling and powered by a simple wall plug—is a challenge that requires the best available control technology. Quantum Machines provides exactly that. This successful demonstration proves that we can run reliable quantum operations on-site. It's an exciting step toward bringing quantum computing to industrial applications," says Dr. Frank Schlichting, CEO of SaxonQ.

Samsung Electronics today announced the new Galaxy XCover7 Pro and Galaxy Tab Active5 Pro, enterprise-ready devices designed to meet the demands of today's fast-paced, high-intensity work environments. Continuing the legacy of Samsung's ruggedized devices, these latest Pro models are versatile, optimized and secure—delivering enhanced durability, steady performance and optimized workflow to empower frontline workers, from the office to the field and beyond.

With 5G connectivity, an upgraded processor and increased memory, the XCover7 Pro and Tab Active5 Pro offer enhanced mobility and reliability. The XCover7 Pro features a powerful new stereo speaker system with anti-feedback technology, which minimizes unwanted audio loops for clearer communication. Both devices offer enhanced battery capacity, with the XCover7 Pro equipped with a 4,350mAh battery for longer usage, while the Tab Active5 Pro comes with a 10,100mAh battery set designed to support demanding workflows. The Tab Active5 Pro also supports Dual Hot-Swap battery functionality, allowing workers to replace batteries without powering down their devices and ensuring seamless operation even when battery levels are low.

NVIDIA is working with its manufacturing partners to design and build factories that, for the first time, will produce NVIDIA AI supercomputers entirely in the U.S. Together with leading manufacturing partners, the company has commissioned more than a million square feet of manufacturing space to build and test NVIDIA Blackwell chips in Arizona and AI supercomputers in Texas. NVIDIA Blackwell chips have started production at TSMC's chip plants in Phoenix, Arizona. NVIDIA is building supercomputer manufacturing plants in Texas, with Foxconn in Houston and with Wistron in Dallas. Mass production at both plants is expected to ramp up in the next 12-15 months. The AI chip and supercomputer supply chain is complex and demands the most advanced manufacturing, packaging, assembly and test technologies. NVIDIA is partnering with Amkor and SPIL for packaging and testing operations in Arizona.

Within the next four years, NVIDIA plans to produce up to half a trillion dollars of AI infrastructure in the United States through partnerships with TSMC, Foxconn, Wistron, Amkor and SPIL. These world-leading companies are deepening their partnership with NVIDIA, growing their businesses while expanding their global footprint and hardening supply chain resilience. NVIDIA AI supercomputers are the engines of a new type of data center created for the sole purpose of processing artificial intelligence—AI factories that are the infrastructure powering a new AI industry. Tens of "gigawatt AI factories" are expected to be built in the coming years. Manufacturing NVIDIA AI chips and supercomputers for American AI factories is expected to create hundreds of thousands of jobs and drive trillions of dollars in economic security over the coming decades.

AMD officially released ROCm 6.4, its latest open‑source GPU compute stack, bringing several under‑the‑hood improvements while still lacking official RDNA 4 support. The update improves compatibility between ROCm's user‑space libraries and the AMDKFD kernel driver, making it easier to run across a wider range of Linux kernels. AMD has also expanded its internal testing to cover more combinations of user and kernel versions, which should reduce integration headaches for HPC and AI workloads. On the framework side, ROCm 6.4 now supports PyTorch 2.5 and 2.6 out of the box, so developers can use the latest deep‑learning features without building from source. The Megatron‑LM integration adds three new fused kernels, Attention (QKV), Layer Norm, and ROPE, to speed up transformer model training by combining multiple operations into single GPU passes. Video decoding gets a boost, too, with VP9 support in both rocDecode and rocPyDecode, plus a new bitstream reader module to streamline media pipelines.

Oracle Linux 9 is now officially supported, and the Radeon PRO W7800 48 GB workstation card has been validated under ROCm. AMD also enabled CPX mode with NPS4 memory configurations, catering to advanced memory bandwidth scenarios on MI Instinct accelerators. Despite these updates, ROCm 6.4 still does not officially support RDNA 4 GPUs, such as the RX 9070 series. While community members report that the new release can run on those cards unofficially, the lack of formal enablement means RDNA 4's doubled FP16 throughput, eight times INT4 sparsity acceleration, and FP8 capabilities remain largely untapped in ROCm workflows. On Linux, consumer Radeon support is limited to just a few models, even though Windows coverage for RDNA 2 and 3 families has expanded since 2022. With AMD's "Advancing AI" event coming in June, many developers are hoping for an announcement about RDNA 4 integration. Until then, those who need guaranteed, day‑one GPU support may continue to look at alternative ecosystems.

ADATA Technology Co., Ltd., a global leader in memory and flash storage solutions, today announced its official expansion into the enterprise storage market to address the growing challenges of data processing brought about by the rise of AI and high-performance computing. In strategic partnership with Korean semiconductor company FADU, ADATA will launch a new line of enterprise-grade SSDs at Computex 2025, targeting to capture significant opportunities in the AI server and data center markets.

According to TrendForce, the global AI server market is projected to reach USD 298 billion in 2025, while enterprise IT data storage volume is expected to exceed 8.4 zettabytes. As AI model training demands higher data processing speed and reliability, enterprises are seeking high-performance, energy-efficient, and scalable storage solutions at an unprecedented pace. ADATA's upcoming enterprise-grade SSDs are designed specifically to meet the intensive needs of AI servers, machine learning, and real-time data analytics, and are ideally suited for data-driven verticals such as enterprise IT, financial services, healthcare, and government sectors. The new products will make their debut at Computex 2025, targeting mission-critical workloads in data centers and AI infrastructure environments.

High-performance GPUs manufactured in Taiwan could now enter the US market tariff-free through a technical loophole in the United States-Mexico-Canada Agreement (USMCA), found by a research firm SemiAnalysis. Companies can route Taiwan-made GPUs through assembly facilities in Mexico and Canada, effectively circumventing the 32% import duty that would otherwise apply to direct shipments from Taiwan. The exemption hinges on a Most-Favored-Nation clause within the USMCA framework that specifically classifies digital processing units (HTS 8471.50), automatic data processing machine units (HTS 8471.80), and their associated components (HTS 8473.30) as "originating goods." This classification applies regardless of manufacturing origin, creating a duty-free pathway for NVIDIA HGX boards, GB200 baseboards, and RTX GPU cards that undergo final assembly in North American facilities.

The strategy capitalizes on two complementary policy mechanisms. First, President Trump's March 7 executive orders maintained existing USMCA exemptions, preserving the duty-free status for compliant goods from Canada and Mexico. Second, the USMCA's expanded definition of originating products creates a classification framework that treats assembled servers and related components as North American products despite their core manufacturing in Taiwan. For US technology firms, the additional logistical complexity of cross-border assembly operations is offset by eliminating substantial import duties on these high-value components. This practice mirrors established protocols in agricultural imports, where products like Mexican avocados gain preferential treatment under similar origin rules. The global supply chain is adapting quickly, especially in high-margin areas like GPUs, which power AI workloads. We are yet to see how companies set up manufacturing and logistics in the new era of tariff-driven narrative.

MediaTek today announced the Dimensity 9400+ SoC, the latest addition to MediaTek's Dimensity flagship chipset family. Providing exceptional Generative and agentic AI capabilities as well as other performance enhancements, the Dimensity 9400+ supports the latest Large Language Models (LLM) while sustaining a super power-efficient design. The Dimensity 9400+ features an All Big Core design, integrating one Arm Cortex-X925 core operating up to 3.73 GHz, combined with 3x Cortex-X4 and 4x Cortex-A720 cores. This powerful configuration accelerates single and multithreaded performance for top-tier Android UX experiences.

"The MediaTek Dimensity 9400+ will make it easier to deliver innovative, personalized AI experiences on-device, combined with enhanced overall performance to ensure your device can handle all tasks with ease," said JC Hsu, Corporate Senior Vice President at MediaTek. "We are working closely with developers and manufacturers to continue building a robust ecosystem of AI applications and other features that will bring a number of speed and privacy benefits to consumers."

To effectively train and deploy generative AI, large language models, or agentic AI, it's crucial to build parallel computing infrastructure that offers the best performance to meet the demands of AI/ML workloads but also offers the kind of flexibility that the future of AI demands. A key aspect for consideration is the ability to scale-out the intra-node GPU-GPU communication network in the data center.

At AMD, we believe in preserving customer choice by providing customers with easily scalable solutions that work across an open ecosystem, reducing total cost of ownership—without sacrificing performance. Remaining true to that ethos, last October, we announced the upcoming release of the new AMD Pensando Pollara 400 AI NIC. Today we're excited to share the industry's first fully programmable AI NIC designed with developing Ultra Ethernet Consortium (UEC) standards and features is available for purchase now. So, how has the Pensando Pollara 400 AI NIC been uniquely designed to accelerate AI workloads at scale?

Today, the European Commission unveiled its AI Continent Action Plan, establishing a framework to enhance the EU's artificial intelligence computing infrastructure. The plan centers on developing five "AI Gigafactories," each housing approximately 100,000 specialized AI accelerator chips, quadrupling the training throughput capacity of current infrastructure projects. The €20 billion commitment from the EU's InvestAI fund will finance data center construction and semiconductor procurement, supplementing the €10 billion allocated to thirteen smaller AI factories scheduled to become operational by 2026. Site selection remains pending, though Germany's incoming administration under Chancellor designate Friedrich Merz intends to secure a facility within German territory. The Action Plan addresses data infrastructure through Data Labs tasked with standardizing datasets from research institutions and industry partners.

An upcoming Data Union Strategy will establish protocols for cross-border information sharing, creating a unified market for AI-ready data resources across member states. With only 13.5 percent of EU enterprises currently using AI in production environments, the Commission will implement an "Apply AI" initiative focusing on deployment in strategic sectors. This program will utilize European Digital Innovation Hubs to provide implementation support. For talent development, the plan includes fellowship programs, visa pathways for specialized non-EU professionals, and an AI Skills Academy offering training in generative models and machine learning operations. An AI Act Service Desk will provide technical guidance on regulatory compliance. The AI arms race is currently being fought on the front between the US and China, where AI labs are acquiring more accelerators and outputting better models almost weekly. In the EU, the goal is to have AI development on par with the two superpowers, leading to more competition and advancements.

It seems like Google aims to grab a bit of the market share from NVIDIA and AMD by offering startups large compute deals and allowing them to train their massive AI models on the Google Cloud Platform (GCP). One such case is the OpenAI co-founder Ilya Sutskever's Safe Superintelligence Inc. (SSI) startup. According to a GCP post, SSI is "partnering with Google Cloud to use TPUs to accelerate its research and development efforts toward building a safe, superintelligent AI." Google's latest TPU v7p, codenamed Ironwood, was released yesterday. Carrying 4,614 TeraFLOPS of FP8 precision and 192 GB of HBM memory, these TPUs are interconnected using Google's custom ICI infrastructure and are scaled to configurations in pods of 9,216 chips, where Ironwood delivers 42.5 ExaFLOPS of total computing power.

For AI training, this massive power will allow AI models to quickly go over training, accelerating research iterations and ultimately accelerating model development. For SSI, the end goal is a simple mission: achieving ASI with safety at the front. "We approach safety and capabilities in tandem, as technical problems to be solved through revolutionary engineering and scientific breakthroughs. We plan to advance capabilities as fast as possible while making sure our safety always remains ahead," notes the SSI website, adding that "Our singular focus means no distraction by management overhead or product cycles, and our business model means safety, security, and progress are all insulated from short-term commercial pressures."

Lightmatter has launched its latest photonic processor, representing a fundamental shift from traditional computing architectures. The new system integrates six chips into a single 3D packaged module, each containing photonic tensor cores and control dies that work in concert to accelerate AI workloads. Detailed in a recent Nature publication, the processor combines approximately 50 billion transistors with one million photonic components interconnected via high-speed optical links. The industry has faced numerous computing challenges as conventional scaling approaches plateau, with Moore's Law, Dennard scaling, and DRAM capacity doubling, all reaching physical limits per silicon area. Lightmatter's solution implements an adaptive block floating point (ABFP) format with analog gain control to overcome these barriers. During matrix operations, weights and activations are grouped into blocks sharing a single exponent determined by the most significant value, minimizing quantization errors.

The processor achieves 65.5 trillion 16-bit ABFP operations per second (sort of 16-bit TOPs) while consuming just 78 W of electrical power and 1.6 W of optical power. What sets this processor apart is its ability to run unmodified AI models with near FP32 accuracy. The system successfully executes full-scale models, including ResNet for image classification, BERT for natural language processing, and DeepMind's Atari reinforcement learning algorithms without specialized retraining or quantization-aware techniques. This represents the first commercially available photonic AI accelerator capable of running off-the-shelf models without fine-tuning. The processor's architecture fundamentally uses light for computation to address next-generation GPUs' prohibitive costs and energy demands. With native integration for popular AI frameworks like PyTorch and TensorFlow, Lightmatter hopes for immediate adoption in production environments.

NVIDIA is collaborating with Google Cloud to bring agentic AI to enterprises seeking to locally harness the Google Gemini family of AI models using the NVIDIA Blackwell HGX and DGX platforms and NVIDIA Confidential Computing for data safety. With the NVIDIA Blackwell platform on Google Distributed Cloud, on-premises data centers can stay aligned with regulatory requirements and data sovereignty laws by locking down access to sensitive information, such as patient records, financial transactions and classified government information. NVIDIA Confidential Computing also secures sensitive code in the Gemini models from unauthorized access and data leaks.

"By bringing our Gemini models on premises with NVIDIA Blackwell's breakthrough performance and confidential computing capabilities, we're enabling enterprises to unlock the full potential of agentic AI," said Sachin Gupta, vice president and general manager of infrastructure and solutions at Google Cloud. "This collaboration helps ensure customers can innovate securely without compromising on performance or operational ease." Confidential computing with NVIDIA Blackwell provides enterprises with the technical assurance that their user prompts to the Gemini models' application programming interface—as well as the data they used for fine-tuning—remain secure and cannot be viewed or modified. At the same time, model owners can protect against unauthorized access or tampering, providing dual-layer protection that enables enterprises to innovate with Gemini models while maintaining data privacy.

Google has rolled out its seventh-generation AI chip, Ironwood, which aims to boost AI application performance. This processor focuses on "inference" computing—the quick calculations needed for chatbot answers and other AI outputs. Ironwood stands as one of the few real options to NVIDIA's leading AI processors coming from Google's ten-year multi-billion-dollar push to develop it. These tensor processing units (TPUs) are exclusively available through Google's cloud service or to its internal engineers.

According to Google Vice President Amin Vahdat, Ironwood combines functions from previously separate designs while increasing memory capacity. The chip can operate in groups of up to 9,216 processors and delivers twice the performance-per-energy ratio compared to last year's Trillium chip. When configured in pods of 9,216 chips, Ironwood delivers 42.5 Exaflops of computing power. This is more than 24 times the computational capacity of El Capitan, currently the world's largest supercomputer, which provides only 1.7 Exaflops per pod.

Today, AMD announced the new Google Cloud C4D and H4D virtual machines (VMs) are powered by 5th Gen AMD EPYC processors. The latest additions to Google Cloud's general-purpose and HPC-optimized VMs deliver leadership performance, scalability, and efficiency for demanding cloud workloads; for everything from data analytics and web serving to high-performance computing (HPC) and AI.

Google Cloud C4D instances deliver impressive performance, efficiency, and consistency for general-purpose computing workloads and AI inference. Based on Google Cloud's testing, leveraging the advancements of the AMD "Zen 5" architecture allowed C4D to deliver up to 80% higher throughput/vCPU compared to previous generations. H4D instances, optimized for HPC workloads, feature AMD EPYC CPUs with Cloud RDMA for efficient scaling of up to tens of thousands of cores.

Today, AMD (NASDAQ: AMD) announced "Advancing AI 2025," an in-person and livestreamed event on June 12, 2025. The industry event will showcase the company's bold vision for AI, announce the next generation of AMD Instinct GPUs, AMD ROCm open software ecosystem progress, and reveal details on AI solutions for hyperscalers, enterprises, developers, startups and more. AMD executives and AI ecosystem partners, customers and developers will join Chair and CEO Dr. Lisa Su to discuss how AMD products and software are re-shaping the AI and high-performance computing landscape. The live stream will start at 9:30 a.m. PT on Thursday, June 12.

Samsung Electronics and Google Cloud today announced an expanded partnership to bring Google Cloud's generative AI technology to Ballie, a new home AI companion robot from Samsung. Available to consumers this Summer, Ballie will be able to engage in natural, conversational interactions to help users manage home environments, including adjusting lighting, greeting people at the door, personalizing schedules, setting reminders and more.

"Through this partnership, Samsung and Google Cloud are redefining the role of AI in the home," said Yongjae Kim, Executive Vice President of the Visual Display Business at Samsung Electronics. "By pairing Gemini's powerful multimodal reasoning with Samsung's AI capabilities in Ballie, we're leveraging the power of open collaboration to unlock a new era of personalized AI companion—one that moves with users, anticipates their needs and interacts in more dynamic and meaningful ways than ever before."

TSMC is confronting a potential $1 billion-plus penalty from the US Commerce Department after inadvertently fabricating compute chiplets later integrated into Huawei's Ascend 910 AI processor. The fine, potentially reaching twice the value of unauthorized shipments, reflects the scale of components that circumvented export controls limiting Chinese access to advanced semiconductor technology. The regulatory breach originated in late 2023 when TSMC processed orders from Sophgo, a design partner of crypto-mining firm Bitmain. These chiplets, which are manufactured on advanced process nodes and contain tens of billions of transistors, were identified in TechInsights teardown analysis of Huawei Ascend 910 AI accelerator, revealing a supply chain vulnerability where TSMC lacked visibility into the components' end-use.

Upon discovery of the diversion, TSMC immediately halted Sophgo shipments and engaged in discussions with Commerce Department officials. By January, Sophgo had been added to the Entity List, limiting its access to US semiconductor technology. A Center for Strategic and International Studies report revealed that Huawei obtained approximately two million Ascend 910B logic dies through shell companies that misled TSMC. Huawei's preference for TSMC-made dies was due to manufacturing challenges in domestic chip production. This incident has forced TSMC to strengthen its customer vetting protocols, including terminating its relationship with Singapore-based PowerAIR following internal compliance reviews. The enforcement process typically begins with a proposed charging letter detailing violations and penalty calculations, followed by a 30-day response period. As Washington tightens restrictions on AI processor exports to Chinese entities, semiconductor manufacturers are under increased pressure to implement rigorous controls throughout multinational supply chains.

Ferroelectric Memory Co. (FMC) and Neumonda have formed a partnership to commercialize "DRAM+," a ferroelectric (FeRAM) memory architecture combining DRAM's speed with non-volatile data retention. The technology substitutes conventional capacitors with ferroelectric hafnium oxide (HfO₂) elements, allowing persistent storage without power while maintaining nanosecond access times. This hybrid technology addresses the performance gap between high-speed DRAM and storage-class memory like NAND flash. Unlike previous European DRAM ventures from Infineon and Qimonda that failed against commodity memory economics, FMC targets specialized applications valuing persistence and power efficiency. The HfO₂-based approach resolves limitations of previous FeRAM memory implementations using lead zirconate titanate (PZT) that couldn't scale beyond megabyte capacities.

Prototypes now demonstrate gigabit-range densities compatible with sub-10 nm fabrication of traditional DRAM made by Micron, Samsung, SK Hynix, and others. By eliminating refresh cycles, DRAM+ reduces static power consumption substantially compared to traditional one-transistor/one-capacitor DRAM cells. Primary applications include AI accelerators requiring persistent model weights, automotive ECUs with immediate startup requirements, and power-constrained medical implants. Neumonda will contribute its test platform suite Rhinoe, Octopus, and Raptor for electrical characterization and analytics at lower capital costs than standard semiconductor test equipment. No production timeline has been announced for commercial DRAM+ products.

Monster Energy Supercross 25 - The Official Video Game—available as Dirt Master Edition or and Special Edition—is out now in early access for Xbox Series X|S, and the Standard Edition will be available for everyone on April 10, 2025—on Steam, Epic Games Store, and PlayStation 5. I can't wait to see all of you take the route for the Supercross elite in this new and completely improved game. Today, I want to talk to you about one of the most ambitious and challenging aspects of the game, which made us sweat but filled us with pride once the results started to arrive: Neural AI.

We started using our Neural AI, A.N.N.A (Artificial Neural Network Agent), in our games starting from 2019, but research began before then. Neural AI is a specific kind of AI that can simulate realistic behavior. To do that, it must train and test the tracks to learn the best trajectories to become as fast as possible. Over the years in Milestone we have seen incredible results, with AI capable of controlling vehicles in a realistic manner. But up to this Supercross chapter, A.N.N.A. has only been on tarmac surfaces.

The UALink Consortium today announced the ratification of the UALink 200G 1.0 Specification, which defines a low-latency, high-bandwidth interconnect for communication between accelerators and switches in AI computing pods. The UALink 1.0 Specification enables 200G per lane scale-up connection for up to 1,024 accelerators within an AI computing pod, delivering the open standard interconnect for next-generation AI cluster performance.

"As the demand for AI compute grows, we are delighted to deliver an essential, open industry standard technology that enables next-generation AI/ML applications to the market," said Kurtis Bowman, UALink Consortium Board Chair. "UALink is the only memory semantic solution for scale-up AI optimized for lower power, latency and cost while increasing effective bandwidth. The groundbreaking performance made possible with the UALink 200G 1.0 Specification will revolutionize how Cloud Service Providers, System OEMs, and IP/Silicon Providers approach AI workloads."

IBM today announced the IBM z17, the next generation of the company's iconic mainframe, fully engineered with AI capabilities across hardware, software, and systems operations. Powered by the new IBM Telum II processor, IBM z17 expands the system's capabilities beyond transactional AI capabilities to enable new workloads.

IBM Z is built to redefine AI at scale, positioning enterprises to score 100% of their transactions in real-time. z17 enables businesses to drive innovation and do more, including the ability to process 50 percent more AI inference operations per day than z16.2 The new IBM z17 is built to drive business value across industries with a wide range of more than 250 AI use cases, such as mitigating loan risk, managing chatbot services, supporting medical image analysis or impeding retail crime, among others.