Applied PC equipped with NVIDIA RTX Pro 6000

Applied Corporation exhibited at the "44th Annual Conference of the Robotics Society of Japan (RSJ 2026)" held at Kanazawa University from September 1 to 4, 2026. At the venue, numerous cutting-edge robotic technologies were showcased, including AI-equipped autonomous patrol robots and industrial humanoid robots, reflecting a high level of interest in physical AI and local LLMs. At our booth, we displayed the MSI "EdgeXpert," an OEM product of NVIDIA DGX Spark, and the next-generation AI workstation "XpertStation WS300" equipped with the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip. We received many inquiries regarding the GPU and computing resources necessary for AI inference and robot development. At Applied, we offer AI servers and GPU workstations tailored to various applications, from AI learning uses such as VLM/VLA to Sim2Real simulations utilizing Isaac Sim. If you are considering building a next-generation R&D environment using physical AI, please feel free to consult with us.

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.

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.

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."