What I have recently felt through exhibitions and various business negotiations is that the utilization of generative AI is becoming not just a means of improving operational efficiency, but rather a fundamental strategy for achieving significant performance enhancement for companies. However, many companies are caught in a serious dilemma where, while there are directives from management to "actively adopt AI," the information systems department and on-site staff are hesitant due to various concerns. The background to this includes fears of confidential information leaks, the complexity of various business flows that have become difficult for on-site staff to explain, and subscription models that are hard for management to predict, such as pay-per-use based on token quantities, which are structural risks associated with traditional cloud-based AI. Therefore, in this blog, I will outline the "four critical challenges" that companies face when they begin to fully utilize AI, and introduce the advantages of a "completely closed platform" as a strategic option to address these challenges.

With the spread of generative AI, including ChatGPT, AI has begun to be utilized in various scenarios such as text generation, summarization, translation, and programming assistance. On the other hand, in corporate and research and development settings, there is an increasing expectation for more advanced AI applications, not just to answer questions, but to: - Automatically search for necessary information from internal technical documents - Compare multiple pieces of information to determine the next course of action - Operate external tools and business systems - Progress through a series of tasks including research, analysis, code generation, and report creation This system, which infers based on objectives and utilizes necessary information and tools to advance tasks, is referred to as "Agentic AI." Supporting this Agentic AI is NVIDIA's open model family, known as NVIDIA Nemotron. In this article, we will introduce what NVIDIA Nemotron is, how it differs from traditional generative AI, and what applications can be considered in manufacturing and research and development.

There are blueprints and materials available. Yet, necessary information cannot be found... This is a groundbreaking solution that can quickly resolve such issues faced by the manufacturing industry using AI. "AnnoLink," a new information management tool for the manufacturing industry, has started!! In this article, we will introduce its features and use cases.

In recent years, have you noticed the changes in data centers? The needs for data centers are currently fluctuating significantly due to the explosive spread of AI. This time, we will introduce the changes in data centers in the IT industry, which can be seen as a reflection of the trends of the times.

In modern edge AI and automation systems, PCs are increasingly required not only to have "high processing power" but also to possess "robustness that can withstand harsh environments." The challenges of installation environments vary widely, including dust in factories, vibrations in transportation, and extreme temperature differences outdoors. Today, we would like to introduce MSI's industrial PC "MS-C906," which packs overwhelming performance and durability into an ultra-compact fanless chassis. We will explain the overview in this blog.

The term "generative AI" has become something we hear in our daily lives. Generative AI, including ChatGPT and Gemini, is being utilized in various situations such as writing and summarizing text, information retrieval, image generation, and program creation. In companies, expectations for generative AI are rising for purposes like improving operational efficiency, utilizing information, and creating new services. On the other hand, because generative AI has become more accessible, many people may want to clarify the basics: "What exactly is generative AI?" "What is different from previous AI?" "How can it be specifically utilized in companies?" "What kind of environment is necessary to fully leverage generative AI?" This time, as the use of generative AI in businesses is progressing, we will revisit and organize our understanding of generative AI, its potential, and the computing environment that supports it.

Attention companies using Autodesk products such as AutoCAD, Inventor, and Revit! Do you face any of the following challenges? "I don't know which is the latest drawing." "It takes time to share data between locations." "I accidentally overwrote the data." "I'm struggling to find past similar designs." To solve these issues and maximize your team's productivity, we recommend the product data management (PDM) software "Autodesk Vault." What is Autodesk Vault? Autodesk Vault is a data management software for engineers that can easily and smoothly integrate with Autodesk products such as AutoCAD, Inventor, and Revit.

In materials development and nanoscale research, the limitations of "trial-and-error development relying solely on conventional experiments" and the "dramatic evolution of computational techniques" have led to an increasing importance of "first-principles calculations" and "molecular dynamics simulations" day by day. The effects obtained through simulations include: - Dramatic reduction in development time and costs - Understanding atomic and electronic-level behaviors that are "invisible" in experiments - Integration with materials informatics (MI) - Considerations for sustainability and environmental and safety constraints However, for those who want to start simulations, the reality is that operating simulation software, creating input files, and setting up the environment present significant hurdles. Therefore, we would like to introduce the integrated GUI environment for nanomaterials analysis, "Advance/NanoLabo," provided by Advance Soft Corporation. Additionally, we will provide a beginner-friendly explanation of the world-standard computational engines "Quantum ESPRESSO" and "LAMMPS," which operate behind NanoLabo.

While the use of generative AI is expanding, manufacturing industries and research institutions are facing challenges such as: - Inability to upload confidential data and design information to the cloud - Desired AI models not fitting into GPU memory - Desire to reduce cloud usage costs and waiting times In particular, large-scale language models (LLMs) and vision-language models (VLMs), which process images and text together, are becoming larger. It is not uncommon for general 48GB or 96GB class GPUs to be unable to load the entire model. A new option to address these challenges is the "MSI XpertStation WS300," equipped with 748GB of coherent memory. This article will clearly explain the features of the WS300 and what types of AI development it is suitable for.

We exhibited at the "49th Annual Meeting of the Japanese Neuroscience Society (NEURO2026)" held at the Kobe International Conference Center from July 30 to August 2, 2026. https://neuro2026.jnss.org/ This time, we participated in the exhibition booth of our partner company, Masako Tech Co., Ltd., showcasing the "AI Image Analysis Software AIVIA" together with our recommended HPC. Thank you very much to everyone who stopped by our booth despite your busy schedules. Today, we will deliver a report on the booth's activities and the exhibition content.

In recent years, the use of "generative AI" has rapidly advanced as a trump card for business efficiency. However, when companies try to implement it seriously within their organizations, they often encounter various "walls." "What if customer data or confidential information leaks to the outside...?" "The costs of pay-per-use increase the more we use it, which is burdensome..." "What if the information generated by AI is false (hallucination)...?" As a breakthrough that simultaneously breaks through these three walls of "security," "cost," and "reliability," the combination of "Local LLM × RAG (Retrieval-Augmented Generation)" is currently receiving a lot of attention. In this blog, we will thoroughly explain the topic of 'Local LLM × RAG'!

Recently, there has been a significant increase in inquiries from companies that are advancing DX in manufacturing sites, stating, "We are considering a local LLM to safely handle confidential information such as CAD/CAM-related data, design drawings, specifications, processing condition tables, and technical documents!" Indeed, a local LLM that does not send data outside is a very strong option in terms of security. However, when actually starting operations, it is necessary to anticipate the possibility of encountering new obstacles such as: "If there is a misinterpretation of drawings due to hallucinations (plausible falsehoods), it will ultimately be a waste of time and unusable." "We want to establish a system that allows for the safe utilization of AI by setting conditions such as strict internal drafting rules and compliance with JIS standards." The local AI platform equipped with hallucination reduction features and safety functions aims to solve these challenges in a one-stop manner and maximize the value of local LLMs. In this article, we will explain why GENFLUX Local can be considered an "essential partner" for the operation of CAD/CAM-related data and design/manufacturing data with local LLMs.

One of the current demands for AI is "physical AI," and I believe there are increasing opportunities to hear related terms such as "Omniverse" and "Isaac Sim." This time, I would like to unravel the current role and position of "Omniverse."

We exhibited at the "Robotics and Mechatronics Conference" held at the Hakata International Exhibition Hall & Conference Center from June 30 to July 1, 2026. Thank you very much to everyone who took the time to visit our booth despite your busy schedules! Today, we will provide a report on the atmosphere of the booth and the exhibits.

In the research and development (R&D) department of the manufacturing industry, the demand for computational resources has reached unprecedented levels. In addition to traditional fluid analysis (CFD), structural analysis, and materials informatics, there has been a surge in the need to build "secure local LLMs (RAG)" trained on internal technical standards and confidential data. However, attempting to execute these advanced processes on-premises requires SXM-based ultra-high-end servers, leading to practical barriers such as "data center power and cooling facilities (like water cooling) not keeping up" and "installation costs being too high." Currently, the "NVIDIA H200 NVL," which is limited to a TDP of up to 600W (air-cooled/PCIe-based) and is easier to implement in existing server room environments, is attracting significant attention from R&D departments. This article will explain the overwhelming advantages gained by bundling four units together using "4-Way NVLink," rather than viewing the H200 NVL merely as an expansion card, as well as the "unexpected pitfalls" that field engineers are likely to encounter during implementation.

The latest AI machines "RTX Spark" and "DGX Station for Windows" were announced at NVIDIA GTC Taipei (at COMPUTEX) held in June 2026. These are not just "fast new computers." While previous computers were "tools operated by humans," this marks the beginning of a major revolution in 40 years, transforming them into "places where AI agents work around the clock." CEO Jensen Huang introduces them in the context of redefining PCs in collaboration with Microsoft after 40 years, unveiling three new Windows machines: "Desktop," "Notebook," and "Workstation." I have summarized their features and what makes them remarkable in this blog.

Modern AI development, particularly the inference and fine-tuning of large-scale language models (LLMs), has become a significant challenge in securing development environments. Until now, handling models in the hundreds of billions of parameters has required substantial ongoing expenditures on cloud services or massive infrastructure investments equivalent to data center levels. However, MSI's "EdgeXpert" transforms this conventional wisdom by presenting an extremely powerful and practical option that places a supercomputer directly at the developer's "desk side." Built on the NVIDIA DGX Spark platform, EdgeXpert is not just an extension of a high-performance PC. It condenses top-tier computational power into a compact chassis, establishing its identity as a "next-generation AI desktop supercomputer." This device enables cutting-edge AI research while protecting highly confidential data in a local environment, making it a significant presence capable of reshaping the landscape of AI development.

On July 2, 2026, I visited the "Monozukuri World Physical AI Exhibition" being held at Tokyo Big Sight. The evolution of generative AI and reinforcement learning is breaking out of the digital world and exploring how to intelligently operate robots in the real world (physical). This exhibition was a very vibrant space that truly hinted at the next generation of manufacturing and the automation of R&D (research and development). I will report on the latest trends that particularly caught my eye on-site!

Now, AI is trying to leap from "the screen" into "the real world." Local LLMs, which excel in security and high-speed processing, have now become a powerful practical engine that goes beyond mere dialogue tools, playing a central role in corporate DX and products. Many of you may have seen examples like "life-sized AI avatars for customer service" being introduced in convenience stores. What happens when local LLMs are further integrated into hardware such as communication robots? AI will evolve into "Physical AI," capable of recognizing the real world and physically interacting with it. The key to this paradigm shift lies in cutting-edge technology known as the "VLA (Vision-Language-Action) model." In this context, we have created an experimental prototype to actually experience the "sense of AI connecting with the real world." We would like to introduce you to its operation and potential!

What is the key to dramatically improving development speed in R&D? The R&D (Research and Development) in manufacturing and mobility development is currently undergoing a significant transformation. With the increasing performance and complexity of products, the amount of data and the computation time required for simulations have exploded. In traditional CAE, even with the use of high-performance HPC, it was necessary to spend enormous computational resources and lead time to obtain high-precision analysis results, taking "days to weeks for a single computation," which has become a major bottleneck in the design cycle. In this "AX (AI Transformation) era," the key to dramatically accelerating the speed of research and development is the "Surrogate Model AI." This article will explain the technological breakthroughs that AI-driven simulations bring to real design and the specific approaches for their implementation.

Many people may find it hard to grasp the concept of carbon neutrality (reducing environmental impact) in a nutshell. "The goal is to achieve net-zero greenhouse gas emissions overall by 2050." The government announced this goal in October 2020. Those engaged in the research and development sectors of the manufacturing industry are facing various challenges daily in pursuit of this goal. In the field, CAE (Computer Aided Engineering) is currently evolving from a mere "tool for efficiency" to an "essential sustainable strategy." Why is simulation technology the key to saving the planet? This time, I would like to explore the deep relationship between "carbon neutrality and the power of CAE."

Key Points to Consider When Building an HPC Cluster for AI and LLMs In the development of generative AI and large language models (LLMs), there is an increasing number of cases where a single GPU workstation or GPU server cannot provide the necessary processing power due to the growth in model size and the amount of training data. As a solution, an HPC cluster that connects multiple GPU servers becomes an option. However, simply adding multiple GPU servers does not guarantee an increase in processing performance proportional to the number of units. If there are issues with the inter-node network, storage, software environment, or resource management, the GPUs may not be able to fully utilize their inherent performance. This article will explain the common bottlenecks that arise in HPC clusters for AI and LLM development, along with key points to check when considering the configuration, incorporating insights from actual consultations.

The selection of an operating system for industrial systems varies based on the system's purpose (control, monitoring, communication, etc.), operational duration, and development costs. For strict real-time control in milliseconds, an RTOS is suitable; for leveraging Windows assets and long-term stable operation over ten years, Windows IoT Enterprise LTSC is ideal; and for high customizability and cost efficiency, Linux should be chosen. It is important to select according to the system's objectives and priorities. After selecting the optimal OS, it is essential to choose the right hardware (industrial PC) that can maximize the OS's capabilities and ensure stable operation 24/7, even in harsh industrial environments.

In the research of cell biology and neuroscience, an inseparable aspect is the "analysis of microscopic images." "Faced with a vast number of continuous slice images, I am tracing each one by hand..." "I have given up on advanced AI analysis because I lack programming knowledge..." Are you struggling with such concerns? Now, in the field of bioimaging, the evolution of software equipped with AI (artificial intelligence) technology is making these arduous manual tasks, which could be called "artisan skills," a thing of the past. This time, we will introduce the powerful workflow of "AIVIA," which achieves high-precision AI analysis without the need for programming, and "SegRef3D," which dramatically accelerates 3D reconstruction with the latest AI from Meta (SAM2).

In the manufacturing industry and advanced research fields, the use of real machines for trial and error is already reaching its limits. NVIDIA's "Physical AI Platform" aims to completely eliminate the heavy constraints of "time, cost, and risk," elevating development speed to an entirely new dimension. This approach, known as "Sim2Real," allows for safe and instantaneous learning in virtual spaces, conducting tens of thousands to hundreds of millions of iterations, and seamlessly implementing the results into the real world. This is not just a concept; it is a powerful ecosystem that has already been put into practical use. In this article, we will elucidate the three phases that make up this platform and the roles of specific libraries that hold the key to its implementation.

Transforming the very foundation of scientific research in Japan—this is the significant expectation and attention surrounding the Ministry of Education, Culture, Sports, Science and Technology's innovative grant program, "AI for Science Seed Challenge Research Creation Project (SPReAD)." The first public call, which generated a great response, concluded on April 17, 2026, and today, June 2, the long-awaited second public call has begun! This is an excellent opportunity for researchers and teams leading the next generation of science and technology to utilize AI and challenge uncharted territories, allowing them to bring their groundbreaking ideas to fruition. The deadline for the second round of applications is noon on July 3 (Friday). With a limited period of about one month, early information gathering and preparation will be key to successful adoption. In recent years, the remarkable advancement of AI technology has significantly changed research processes across all academic fields. In this context, we will provide a clear explanation of the SPReAD program's outline and offer detailed proposals on how to maximize the adopted budget (up to 5 million yen) with a "complete set of equipment and environment construction package that allows for immediate AI research to start from zero knowledge."

This time, I participated in the "Automotive Technology Exhibition 2026 YOKOHAMA," held on May 28, 2026, at Pacifico Yokohama. This exhibition is organized by the Japan Society of Automotive Engineers and is one of the largest specialized exhibitions in the automotive technology field in Japan, bringing together automobile manufacturers, parts manufacturers, material manufacturers, software vendors, research and development institutions, and more, symbolizing Japan's mobility industry.

This time, we bring you a report from the exhibition hall of the "82nd Annual Meeting of the Japanese Society of Microscopy," held at the Sendai International Center on May 26, 2026. This conference is a highly specialized venue where the latest insights on various microscopy technologies, including electron microscopy, and their applied research gather. I participated on the second day and experienced the "enthusiasm on the ground" that supports cutting-edge research in Japan!

The development of autonomous driving systems is transitioning from traditional rule-based and modular approaches to an end-to-end "AX (AI Transformation)" environment where AI comprehensively understands situations and makes decisions. NVIDIA's offerings, "Alpamayo," "AlpaSim," and "NuRec," represent the latest ecosystem aimed at the societal implementation of level 4 autonomous driving, balancing safety and explainability. NVIDIA's autonomous driving AI platform "Alpamayo" moves away from reliance on traditional pattern recognition in autonomous driving by incorporating a mechanism where AI "infers (thinks and judges)" like a human, dramatically enhancing safety and reliability. The R&D departments of enterprises will report on the HPC requirements necessary to maximize the use of these technologies, integrating physics-based simulations (CAE) with AI models.

Intel has launched the new Xeon 600 series as the successor to the Xeon W series CPU for workstations. Hello everyone! In this article, we will provide a thorough explanation of Intel's next-generation workstation CPU, the Xeon 600 series, which was unveiled in March 2026. We will introduce the features and significant benefits of this product, which has undergone a dramatic evolution worthy of being called a "paradigm shift" in the professional workstation market.