The New AI Era: An In-Depth Analysis of Core Technologies and Future Outlook from NVIDIA CEO Jensen Huang's COMPUTEX 2025 Keynote

The global tech industry once again fixed its gaze on COMPUTEX 2025, where NVIDIA founder and CEO Jensen Huang took the stage, captivating the audience with his characteristic charisma and forward-thinking vision. He painted a vivid picture of a computing future driven comprehensively by AI, positioning NVIDIA as the core engine of this transformation. The technologies and platforms unveiled are not mere hardware iterations; they represent profound predictions for the next decade of technological advancement, fundamentally reshaping how industries operate and create value. The far-reaching impact of these developments warrants our detailed analysis.

This article will delve deep into the key technological highlights from Huang's speech. From NVIDIA's transformative journey, the grand concept of the "AI Factory," and the formidable performance of the next-generation Grace Blackwell superchip, to the intelligent evolution of the Isaac robotics platform and how the Omniverse digital twin platform mirrors the real world, we will explore the underlying technical architectures, core principles, challenges, and future potential of each.

NVIDIA's Great Transformation: The Core Path from Graphics Chips to AI Computing Infrastructure

NVIDIA's story is an epic of continuous self-reinvention, adapting to and leading the technological zeitgeist. It has evolved from a company focused on graphics chip design into today's behemoth of AI computing infrastructure. This journey involved several pivotal technological and strategic shifts, each decision cementing its leadership in the artificial intelligence domain and fully showcasing its acute insight into tech trends and unwavering execution.

The CUDA Architecture: A Cornerstone for Unleashing GPU Parallel Computing Potential

When discussing NVIDIA's turning points, the introduction of the CUDA (Compute Unified Device Architecture) in 2006 is an absolute must-mention. Its launch signified that GPUs were no longer exclusive tools for gamers but had become powerful computational instruments for scientists and engineers. Before CUDA, leveraging GPUs for general-purpose computing (GPGPU) was exceedingly difficult; developers often had to disguise complex computational problems as graphics rendering tasks, a cumbersome and inefficient process.

CUDA's core architectural innovation lay in providing a relatively accessible parallel computing platform and programming model. It was the first to allow developers to use high-level languages like C, C++, and Fortran, compiled through NVIDIA's toolchain into instructions GPUs could execute directly. This revolutionary design enabled developers to directly access and flexibly program the thousands of compute cores within a GPU, executing massively parallel computations using the SIMT (Single Instruction, Multiple Threads) paradigm. This "single instruction, multiple threads" model perfectly matched the inherent data parallelism in many scientific computing and, crucially, AI algorithms, thereby vastly expanding the application boundaries of GPUs and laying the essential groundwork for the subsequent AI explosion.

DGX Systems: Pioneering Architecture for Integrated AI Supercomputing

With the breakthrough advancements in deep learning algorithms in the early 2010s, the demand for computational power grew exponentially. NVIDIA, with its keen market perception, quickly realized that merely providing powerful GPU hardware was insufficient to meet the market's voracious appetite for AI development. AI researchers and application developers urgently needed an integrated, user-friendly, and high-performance development platform. Thus, NVIDIA meticulously planned and decisively launched the DGX system, tailor-made for AI research and development.

A DGX system is far more than a simple server; its technical architecture resembles a "full-stack AI supercomputer in a box." It not only houses NVIDIA's latest and most powerful Tensor Core GPUs but also integrates high-speed NVLink and NVSwitch interconnect technologies for multi-GPU communication, storage subsystems optimized for AI workloads, and a complete AI software stack. This stack includes a specialized operating system, the latest CUDA runtime environment, core deep learning libraries like cuDNN and NCCL, containerized versions of mainstream AI frameworks (such as TensorFlow and PyTorch), and convenient workload management tools. This philosophy of "hardware-software co-design and extreme optimization" drastically simplified the AI model training and deployment pipeline, allowing researchers to focus their valuable time and energy on algorithmic innovation rather than time-consuming infrastructure configuration.

The Rise of AI Factories: A New Paradigm Defining Next-Generation Intelligent Productivity

In his keynote, Jensen Huang repeatedly and emphatically highlighted the concept of the "AI Factory". This is not just a vivid metaphor; it profoundly reveals the evolutionary direction and ultimate form of future AI infrastructure. He firmly believes that AI Factories will become the core bases and critical hubs for the scaled production of "intelligence." Their strategic importance in the future society, he argued, will be comparable to that of power plants during the Industrial Revolution, providing an inexhaustible source of intelligent power for the operation and development of society as a whole. NVIDIA is currently going all out, focusing its resources on building this new era's "infrastructure for intelligence," dedicating itself to seamlessly integrating the powerful capabilities of AI into every facet of human life and work, aiming for true ubiquitous intelligence.

The Technical Architecture of AI Factories: Revolutionizing Compute, Networking, and Storage

Traditional data center design philosophy typically centers on general-purpose CPUs as the primary compute units, emphasizing resource elasticity and multi-tenant resource sharing efficiency. However, the technical architecture of an AI Factory represents a fundamental departure, even a complete disruption, from this traditional model. Its compute core has decisively shifted from CPU dominance to being overwhelmingly led by large-scale GPU clusters and other specialized accelerators, deeply optimized for efficiently executing massively parallel AI workloads. In terms of networking, AI model training involves extremely frequent and voluminous data exchanges between GPUs (such as gradient synchronization and model parameter updates). This places almost stringent demands on network bandwidth and latency.

Therefore, AI Factories must employ advanced interconnect technologies like NVIDIA InfiniBand or its Spectrum-X Ethernet platform, which offer ultra-high bandwidth and ultra-low latency, to ensure unimpeded data flow among thousands of GPU nodes. Similarly, storage systems must also be deeply optimized for the unique I/O characteristics of AI applications. For instance, they might use high-speed parallel file systems or specialized flash storage solutions designed for AI, to meet the rapid read demands of massive training datasets and the frequent, high-speed write operations for model checkpoints. These three technological pillars—compute, networking, and storage—undergo a comprehensive revolution and deep integration, collectively forming the solid hardware foundation for the efficient and stable operation of an AI Factory. Undoubtedly, building and operating such a state-of-the-art AI Factory requires enormous upfront capital investment and continuous maintenance costs. However, for generating AI-driven outputs that possess a core competitive advantage in a fiercely contested landscape, they represent indispensable and non-negotiable infrastructure investments.

The "Token" Economy: Intelligence as a New Value Output

The uniqueness of an AI Factory lies not only in its advanced hardware architecture but, more importantly, in its core output. It no longer produces standardized physical goods typical of traditional industries. Instead, it generates "intelligence" in a quantifiable and potentially tradable form, which Huang referred to as "tokens". The term "token" here is a broad concept. It can be a segment of precise, fluent text generated by a Large Language Model (LLM) in response to a user's query or instruction, or an insightful business analysis report rich with data. It could also be a stunning piece of artwork with a unique style and vivid details, or a photorealistic virtual scene created by an advanced AI drawing tool based on a simple text description. Furthermore, it could even be a sequence of control commands that drives an industrial robot to accurately perform complex assembly tasks, or dynamic decision-making advice that optimizes the operational efficiency of a vast supply chain network. This new economic model, with "Intelligence-as-a-Service" and "Intelligence-as-a-Product" at its core, is profoundly reshaping the value creation methods, competitive landscapes, and even social organizational forms across various industries.

Accelerated Computing and AI: NVIDIA's Dual Core Technological Engines

NVIDIA's core technology strategy has always been clearly and steadfastly built around three central pillars: its world-leading accelerated computing hardware platform, continuously innovating artificial intelligence algorithms and models, and the vast, vibrant CUDA X library ecosystem, which millions of developers worldwide participate in, contribute to, and rely upon. Among these, accelerated computing is NVIDIA's foundational strength and the technological bedrock of its success, while artificial intelligence serves as its core driving force for discerning future industry trends and spearheading the next wave of technological change. These two elements are not isolated; rather, they are tightly coupled, mutually reinforcing, and complementary, together forming the powerful "dual-engine" system that enables NVIDIA to maintain its leadership position and continuously explore new frontiers in the turbulent and fiercely competitive global technology landscape.

The Architectural Essence of Accelerated Computing: CPU and GPU Synergy

The essence of accelerated computing and its core system architecture philosophy lies in offloading the computationally intensive, highly parallelizable task workloads within an application from the Central Processing Unit (CPU)—which traditionally handles control flow, sequential tasks, and I/O management—to a Graphics Processing Unit (GPU) or other domain-specific accelerators (DSAs) specifically designed for such massive parallel operations. GPUs typically contain thousands of smaller, simpler processing cores, making them exceptionally well-suited for handling a large number of similar computational tasks simultaneously. Through this heterogeneous computing architecture, where the CPU and GPU (or other accelerators) each perform their specialized roles in close coordination, it's often possible to achieve several-fold, tens-fold, or in some well-optimized scenarios, even hundreds-fold improvements in overall application performance. However, achieving efficient accelerated computing requires more than just powerful hardware. It demands that developers have a profound understanding of the target application's algorithmic characteristics and data flow patterns, মৃত্যু-defyingly supported by advanced software compiler toolchains and runtime libraries for fine-grained task partitioning, resource scheduling, and memory management, to truly unleash the immense potential latent in the underlying hardware.

GeForce RTX and AI Graphics: The Revolutionary Breakthrough of DLSS Technology

In NVIDIA's traditional stronghold—PC gaming and professional visual graphics processing—the deep integration of artificial intelligence technology has likewise brought about revolutionary changes that are nothing short of spectacular. The successful launch and enthusiastic market reception of the latest generation GeForce RTX 50 series graphics cards once again powerfully demonstrate the vast user base and continued importance of technological innovation in the PC gaming market. More significantly, AI has fundamentally altered the design philosophy of modern computer graphics rendering pipelines and the final visual presentation. One of the most representative and widely acclaimed technological innovations in this area is DLSS (Deep Learning Super Sampling).

The core operational principle of DLSS is to ingeniously utilize the Tensor Cores, specialized AI processing units built into the GPU, to efficiently execute a deep neural network model that has been pre-trained on massive amounts of high-quality image data. This model can perform real-time analysis, understanding, and intelligent reconstruction of frames rendered by the game engine at a lower resolution, thereby "inferring" and generating the missing high-resolution detail pixels. This groundbreaking technology can significantly boost game frame rates, ensuring an ultra-smooth operational experience, while simultaneously maintaining, or in some cases even surpassing, the visual quality and finesse achievable through native high-resolution rendering. It has delivered an unprecedented level of immersive interactive entertainment to a vast global audience of gamers.

The CUDA X Library Ecosystem: An Acceleration Engine for Cross-Domain Applications

Possessing the world's fastest and most advanced hardware computing platform alone is not enough to build a truly insurmountable technological moat or sustainable business success. NVIDIA is acutely aware of this and deeply understands the critical importance of a vibrant software ecosystem for platform development. Therefore, over many years, NVIDIA has invested an immeasurable amount of capital and engineering resources around its core CUDA parallel computing architecture to meticulously cultivate and establish an exceptionally rich, functionally complete, and continuously expanding CUDA X AI and HPC (High-Performance Computing) library ecosystem.

The CUDA platform itself, along with its massive and ever-growing installed base worldwide, provides an incredibly solid technological foundation and an inexhaustible source of motivation for third-party developers and NVIDIA's own engineering teams to create a diverse range of accelerated libraries and applications. These libraries, deeply optimized for specific application domains, cover an extremely broad spectrum. They range from the most fundamental mathematical operations (such as cuBLAS for linear algebra and cuFFT for Fast Fourier Transforms), to mid-level image and signal processing (like NPP), and extend to the highest-level complex application areas. These include deep learning model training and inference (e.g., cuDNN, TensorRT), general machine learning algorithms (e.g., cuML), large-scale data analytics and processing (e.g., cuDF), traditional scientific and engineering computing, computational fluid dynamics simulations, quantum computing simulations, and critically important fields for human well-being such as bioinformatics research (e.g., genomic sequence analysis) and advanced medical image processing and diagnostic assistance (e.g., its comprehensive Clara platform).

These highly optimized, high-performance libraries enable application developers across all industries to avoid writing complex low-level GPU acceleration code from scratch. Instead, they can more easily and efficiently leverage the powerful parallel computing capabilities of NVIDIA GPUs to rapidly develop high-performance applications tailored to their specific needs. This has not only vastly expanded the practical application landscape of NVIDIA GPU technology but has also successfully helped NVIDIA to penetrate and establish leadership positions in numerous promising new vertical market segments.

The Evolutionary Ladder of AI Technology: Foresight from Generative AI to Physical AI

The development of artificial intelligence is not a monolithic, linear progression but rather characterized by phased advancements and continuously iterating capabilities. In his COMPUTEX address, Jensen Huang clearly delineated an evolutionary path for AI technology, moving from the current widely acclaimed perceptual and generative capabilities towards higher-order forms possessing greater autonomy, deeper understanding, and more complex interaction with the physical world.

Generative AI: Infinite Possibilities for Content Creation

Currently, the global wave of artificial intelligence technology has successfully advanced from an earlier phase primarily represented by perceptual intelligence (such as image classification, object detection, and speech recognition) to a new zenith marked by Generative AI as its core hallmark. The fundamental capability of Generative AI lies in the model's ability to learn the complex patterns, structures, and distributions latent within massive, diverse datasets. Based on this learned "understanding," it can then create entirely new, and to some extent, original content. The forms of this generated content are extremely rich and varied: it can be text that meets specific thematic and stylistic requirements (e.g., news reports, poetry, marketing copy); it can be images possessing particular artistic styles or photorealistic verisimilitude; it can be lifelike speech or music; it can be captivating video segments; and it can even be computer code that conforms to specific programming language syntax and can execute particular functions. This exhilarating technological breakthrough is bringing unprecedented imaginative scope and enormous potential for productivity leaps to numerous fields, including digital content creation, artistic design, software engineering automation, personalized education, new drug discovery, and advanced materials science exploration.

Agentic AI and Physical AI: Towards More Autonomous Intelligent Systems

Looking towards the next significant phase of AI development, Jensen Huang particularly emphasized the immense developmental potential and strategic importance of Agentic AI and Physical AI. Agentic AI stresses the system's capabilities for autonomous planning, logical reasoning, and effective interaction with external environments and tools. This enables it to function like a truly capable human assistant or intelligent agent, not only understanding complex, multi-step instructions or goals basuraed by the user but also proactively decomposing large tasks into smaller steps and autonomously invoking external tools (such as search engines, calculators, API interfaces) or knowledge bases to assist in task completion.

Physical AI, on the other hand, is dedicated to enabling AI systems to truly "understand" and adhere to the objective laws and principles of the complex and dynamic physical world we inhabit, such as Newtonian classical mechanics, optical and acoustic propagation characteristics, and the physical and chemical properties of different materials. Only when AI possesses a profound and accurate understanding of the physical world can it engage in safe, reliable, and efficient interactive operations with various objects in real, unstructured environments. This will be the critical cornerstone and core challenge for achieving general-purpose autonomous robots, highly environmentally adaptive fully autonomous driving systems, and other more sophisticated autonomous intelligent systems.

The Grace Blackwell Superchip: A Monster Engine Driving Giant AI Models

Facing today's increasingly complex AI models with parameter counts soaring into the trillions or even tens of trillions (such as large language models and multimodal foundation models), coupled with the extreme demands from cloud service providers and end-users for real-time inference responsiveness and high-efficiency model training, traditional CPU-centric or previous-generation GPU computing architectures are increasingly revealing their limitations in terms of memory capacity, memory bandwidth, core compute capability, and system power consumption, gradually feeling strained.

To address this severe challenge and continue to lead the developmental direction of AI computing technology, NVIDIA grandly unveiled its epoch-making new Grace Blackwell superchip platform at this COMPUTEX. This platform, with its unparalleled single-chip compute performance, innovative high-bandwidth memory architecture, and powerful, flexible system-level interconnect capabilities, is designed to provide unprecedented, monstrous levels of raw power to support the next generation of the most demanding artificial intelligence applications and high-performance computing (HPC) workloads.

The Fused Architecture of Grace CPU and Blackwell GPU

The core architectural highlight of the Grace Blackwell superchip platform and the source of its formidable performance lies in the unprecedentedly tight physical fusion and highly efficient synergistic operation achieved by directly connecting NVIDIA's multi-year, independently developed Grace CPU (which is based on the industry-leading high-performance Arm Neoverse core architecture and has been deeply customized and optimized for the characteristics of AI and HPC workloads) with NVIDIA's brand-new, revolutionary GPU architecture codenamed Blackwell.

This connection is facilitated by an ultra-high bandwidth, extremely low-latency NVLink-C2C (Chip-to-Chip) direct interconnect technology. This highly integrated design allows the CPU cores and GPU cores to access a unified, shared, cache-coherent memory address space. This drastically reduces the system latency and extra power consumption bottlenecks typically caused by moving massive amounts of data (such as model parameters, intermediate computation results, and input data) back and forth between different processors. For giant AI applications that need to frequently access and process massive model parameters and enormous training datasets far exceeding the memory capacity of a single GPU (such as training trillion-parameter-scale large language models), the practical performance improvement this brings is particularly significant and crucial.

Technology Feature	Description	In-depth Explanation
CPU	NVIDIA Grace CPU (Arm architecture, optimized for AI/HPC)	Employs high-efficiency Arm cores, specially designed to support GPU large-scale parallel computing and process massive datasets, providing robust scalar compute capability and efficient memory management mechanisms.
GPU	NVIDIA Blackwell architecture GPU	Represents NVIDIA's latest breakthrough in GPU graphics processing and general-purpose parallel computing architecture. Expected to have substantial generational improvements in single/double-precision floating-point performance, AI inference and training acceleration units (like next-gen Tensor Cores), power efficiency, and ray tracing rendering capabilities.
Interconnect Tech	NVLink-C2C (Chip-to-Chip high-speed interconnect)	Delivers ultra-high data transfer bandwidth in the hundreds of GB/s range, enabling CPU and GPU to collaborate with unprecedented tightness, achieving efficient cache coherency and extremely low-latency shared memory access.
Design Goal	Processing giant AI models, high-performance inference & training	Specifically built to address the memory capacity and bandwidth bottlenecks caused by the explosive growth trend in AI model scale, and the increasingly stringent AI inference latency requirements at the cloud and edge.
Core Advantage	High bandwidth, low latency, high efficiency from shared memory	Significantly boosts the practical compute performance of large AI models that need to process data exceeding single GPU onboard memory, while also simplifying the programming model complexity for such systems and helping to reduce overall system power consumption.

GB300: Born for Extreme Inference Performance and Scale

During the keynote, Jensen Huang specifically highlighted the GB300 system-level configuration based on the Grace Blackwell architecture, anticipating that this system will deliver striking practical application performance leaps. According to preliminary data released by NVIDIA, compared to its predecessors, the GB300 can achieve up to a 1.5-fold performance improvement when handling complex inference tasks such as large language models, while its overall system-level network communication bandwidth will also be doubled. These significant performance metric enhancements mean that AI Factories or large data centers built on GB300 systems in the future will be able to more efficiently support the rapid deployment and online servicing of larger-scale, more complex advanced AI models. They will also provide end-users with swifter, lower-latency real-time responsiveness, thereby better meeting the ever-growing global demand for high-performance AI application services.

NVLink and NVSwitch: The Backbone of Large-Scale GPU High-Speed Interconnect

The computational power of a single GPU, no matter how formidable, will eventually encounter its physical limits and power wall constraints. To efficiently train and stably run today's most advanced AI models, which often possess hundreds of billions or even trillions of tunable parameters, it is typically necessary to construct massive compute clusters composed of hundreds, thousands, or even tens of thousands of GPUs. At such a system scale, the efficiency of communication between GPUs (including both bandwidth and latency) becomes the critical bottleneck determining the overall computational performance and scalability of the entire cluster. NVIDIA's NVLink is a proprietary high-speed, point-to-point interconnect technology specifically designed for direct communication between GPUs.

Compared to traditional PCIe (Peripheral Component Interconnect Express) lanes, which are primarily designed for CPU-to-peripheral communication, NVLink can provide several times higher effective data transfer bandwidth and significantly lower communication latency. Technologies like MVLink are also crucial for efficiently connecting a large number of GPUs and enabling high-speed data communication between them. Furthermore, to build even larger-scale and more flexibly-topologized GPU interconnect networks, NVIDIA has also introduced NVSwitch chips. These act similarly to high-performance network switches in traditional Ethernet networks, capable of aggregating multiple NVLink ports and supporting the construction of ultra-large-scale GPU cluster systems with higher-order, more complex interconnect topologies (such as a fully-connected NVLink Fabric or multi-level fat-tree structures). This ensures that data can flow directly between any two GPUs within the cluster at extremely high speeds with high efficiency and low latency.

MVLink Fusion: Towards More Flexible, Semi-Custom AI Infrastructure

To further address the diverse and customized requirements for AI infrastructure from different industry sectors and specific application scenarios—in terms of performance, cost, power consumption, and specific functional integration—NVIDIA also revealed a new technology named MVLink Fusion during its presentation. Judging from its name and the preliminary information released, MVLink Fusion likely represents a more flexible and, to some extent, more open chip-level or board-level interconnect framework, standard interface, or reference design. It might allow enterprise users or system integrators to more easily and efficiently integrate third-party compute units or specialized accelerator chips onto their mainstream AI computing platforms based on NVIDIA GPUs. Examples could include ASICs (Application-Specific Integrated Circuits) designed for specific domains (like network processing or storage acceleration), or CPUs from different architectural families. This capability menuju "semi-custom" AI infrastructure will help the industry create differentiated AI solutions that are more deeply optimized for specific application workloads, thereby achieving higher cost-effectiveness or lower power consumption. It will also foster broader and more diverse industry ecosystem collaboration and innovation.

AI Democratization: New Platform Architectures Empowering Developers and Enterprises

NVIDIA's long-term corporate vision extends beyond merely creating the world's most powerful chips. More importantly, it aims to ensure that powerful AI technology, which represents the future direction of productivity, can truly benefit every creative developer, every enterprise seeking transformation and upgrade, and indeed every corner of society, becoming a universally beneficial force driving human progress. To this end, they continuously introduce a series of AI hardware platforms, software development tools, and cloud services, meticulously positioned for different user groups, varying application scales, and diverse technical backgrounds. The core objective always remains dedicated to constantly lowering the barriers to acquiring AI technology, reducing its developmental complexity, and ultimately minimizing its deployment and usage costs.

DGX Spark and DGX Station: AI Powerhouses Tailored for Diverse Scale Needs

For the broad community of AI-native developers, academic researchers, and startup teams with relatively limited resources, NVIDIA has introduced the DGX Spark solution. From its description, it is highly likely to provide a convenient, user-friendly, and fully pre-configured personalized AI cloud development environment. This allows users to rapidly access powerful NVIDIA GPU AI compute resources and a suite of pre-configured mainstream AI development tools and frameworks over the network, without needing to procure, set up, and maintain complex and expensive on-premises hardware infrastructure. For users who need to perform efficient model development, interactive code debugging, and small to medium-scale model training tasks locally (e.g., in a personal office or laboratory setting), the DGX Station stands as an ideal "deskside AI supercomputer." It requires only a standard office wall power outlet yet can unleash formidable AI compute power comparable to data center-grade systems, sufficiently capable of smoothly running and rapidly iterating on various complex large AI models locally.

RTX Pro Enterprise: The Compute Core for Enterprise AI Agents

For large enterprise users seeking to deeply integrate advanced artificial intelligence capabilities into their core business processes, critical application systems, and professional workflows, NVIDIA offers the RTX Pro Enterprise server solutions. The hardware architecture of this server series (including GPU configuration, memory capacity, network interfaces, etc.) and the professional-grade software stack running on it (such as the NVIDIA AI Enterprise suite) have been meticulously designed, rigorously tested, and continuously optimized. They are specifically tailored for stably and efficiently running enterprise-grade AI Agent applications, complex professional graphics rendering tasks (like film-quality visual effects production, high-fidelity Building Information Modeling (BIM) visualization), high-precision engineering simulations and analyses (such as Computer-Aided Design (CAD)/Computer-Aided Engineering (CAE) analyses), and other high-value workloads that have extremely stringent requirements for computational accuracy, system reliability, and data security. NVIDIA is actively driving the comprehensive and in-depth introduction of AI's transformative power into traditional enterprise IT environments by fundamentally reshaping enterprises' existing compute, storage, and networking infrastructure, thereby assisting them in achieving intelligent transformation and upgrading.

NVIDIA AI Data Platform and IQ: Unleashing Enterprise Data Potential

In today's data-explosive digital age, almost all enterprises inevitably accumulate massive volumes of unstructured data in various formats, often lacking effective organization. This includes diverse types of electronic documents, business reports, customer correspondence emails, user feedback from social media, product images and promotional videos, and operational logs from various IT systems and IoT devices. Behind this seemingly chaotic data lies immense potential business value and decision-making insights. The NVIDIA AI Data Platform and its supercharged IQ intelligent analytics technology aim to provide a complete, end-to-end software platform architecture and solution. Its core objective is to help enterprise customers more efficiently collect, process, manage, and deeply analyze these massive volumes of unstructured data, and to rapidly and accurately extract valuable business insights and actionable intelligence from them. For example, enterprises can leverage the platform's advanced semantic search capabilities to quickly and precisely find all information relevant to a specific business topic or customer need based on natural language queries, going beyond simple keyword-based surface matching. Alternatively, enterprises can utilize pre-trained or custom-trained AI models on the platform to perform automated sentiment analysis, trend forecasting, potential risk identification, or anomaly detection on large volumes of text data, thereby providing strong data-driven support for various aspects of the business, such as marketing, product development, risk control, and strategic decision-making.

The Era of Intelligent Robotics: NVIDIA's Comprehensive Isaac Platform Blueprint

Imbuing physical machines with the powerful cognitive, understanding, and decision-making capabilities demonstrated by AI in the digital world, enabling them to autonomously perceive environments, navigate flexibly, manipulate objects precisely, and collaborate efficiently with humans and other machines in the complex and ever-changing physical world—this has always been NVIDIA's unwavering core pursuit and long-term goal in the field of robotics technology. The Isaac platform is precisely the key cornerstone meticulously built by NVIDIA to realize this grand technological vision. It is not a single product but rather a full-stack solution and ecosystem that encompasses everything from underlying specialized compute chips and mid-level operating systems and software development kits (SDKs) to high-level simulation and training environments.

Jetson Thor: The Edge AI Compute Core Driving Autonomous Robots

Modern autonomous robots (such as warehouse logistics robots, delivery drones, agricultural automation equipment, and companion robots) need to process, in real-time at the edge where they operate, massive, heterogeneous data streams collected from multiple different types of sensors (e.g., high-resolution cameras, LiDAR, Inertial Measurement Units (IMUs), tactile sensors). They must also be able to rapidly execute complex AI algorithms (such as object detection, scene understanding, path planning, motion control) to make timely and accurate decisions and actions.

NVIDIA Jetson Thor is a high-performance System-on-Chip (SoC) specifically designed for such high-end robotics and autonomous machine applications that have extremely high requirements for real-time performance, reliability, and computational power. Its internal hardware architecture is expected to integrate powerful multi-core CPU clusters, NVIDIA's next-generation GPU architecture (which will undoubtedly include advanced acceleration units optimized for AI and machine learning tasks, like Tensor Cores), and other specialized processors and interfaces customized for robotics-specific applications (such as Image Signal Processors (ISPs), safety co-processors, etc.). This will provide robots with sufficient and reliable real-time compute capability at the power-constrained edge. For instance, the Isaac Groot robotics universal foundation model and platform, highlighted in the keynote, features a Jetson Thor computer at its core, running an optimized NVIDIA Isaac operating system and a rich software development stack.

Isaac OS and SDKs: The Software Cornerstone Accelerating Robotics Application Development

Possessing a powerful edge computing hardware platform alone is not sufficient to fully address the immense complexity and challenges involved in modern robotics application development. The NVIDIA Isaac platform deeply understands the importance of software and ecosystems. Therefore, it also provides a complete suite of sophisticated software toolchains and development resources. Among these, Isaac Sim is an exceptionally powerful and physically accurate robotics simulation environment built upon the robust Omniverse platform. Developers can use Isaac Sim to rapidly construct highly realistic virtual test scenarios, import or design 3D models of robots, verify the functionality and evaluate the performance of various perception algorithms (such as camera-based visual perception and LiDAR-based point cloud processing), and iteratively train and refine AI decision-making and control strategies in a safe virtual environment.

Furthermore, it can generate large volumes of synthetic data with precise annotations for model training, thereby effectively reducing reliance on expensive real-world data. In addition, Isaac ROS (Robot Operating System) and a series of other Software Development Kits (SDKs) comprise a comprehensive set of libraries, API interfaces, and utility tools, all pre-optimized for NVIDIA hardware platforms. Their functionalities fully cover the critical modules required for robotics application development, including perception (e.g., object detection and tracking, real-time scene segmentation and understanding), navigation and path planning (e.g., Simultaneous Localization and Mapping (SLAM), dynamic obstacle avoidance), complex object manipulation control (e.g., kinematics and dynamics planning, grasp pose estimation), and natural human-robot interaction (e.g., voice command understanding, gesture recognition). NVIDIA's core objective in releasing these software tools is to significantly simplify the overall robotics application development workflow, markedly shorten product development and iteration cycles, and ultimately enhance the comprehensive performance, stability, and reliability of robotics applications deployed in real-world environments.

AI-Driven Robot Training: Advances in Imitation and Reinforcement Learning

Traditional methods of robot programming often require experienced developers to write precise instructions and logical rules for every minute action and every possible decision branch of the robot. This approach is not only extremely time-consuming and labor-intensive, costly, but the resulting robot systems often lack good adaptability to the complexity and dynamic uncertainties of the real world. To overcome these limitations, NVIDIA is actively exploring and vigorously promoting the use of various advanced artificial intelligence techniques, particularly machine learning methods, to fundamentally accelerate the robot's learning process and enhance the efficiency and autonomy of its skill acquisition. For example, through the technical path of Imitation Learning, robots can directly observe and learn from the operational demonstrations of human experts (e.g., demonstrating how to grasp a specifically shaped object, or how to complete a series of assembly actions), thereby enabling the robot to master specific operational skills more rapidly.

Alternatively, by utilizing the framework of Reinforcement Learning, especially advanced reinforcement learning algorithms that cleverly integrate human feedback mechanisms (Reinforcement Learning from Human Feedback, RLHF), robots can continuously engage in autonomous trial-and-error exploration, accumulate valuable experiential data through extensive interaction with highly realistic simulation environments or safely designed real-world settings, and persistently optimize their internal behavioral decision-making strategies based on predefined reward or penalty signals, with the ultimate aim of autonomously achieving complex goals set by the developers. These new paradigms of robot training, driven by AI at their core, are widely regarded as the key future pathways to significantly enhancing robot autonomy, intelligence levels, and environmental adaptability.

NVIDIA Isaac Platform Component	Functional Positioning	In-depth Explanation
Jetson Thor SoC	Edge AI compute core for robotics	A high-performance, low-power compute unit designed for robotics applications, providing robust real-time processing capabilities. It integrates advanced CPUs, GPUs, and dedicated AI accelerators, serving as the "brain" core for future intelligent robots.
Isaac Sim	Robotics simulation and synthetic data generation platform based on Omniverse	Offers a physically accurate, photorealistic virtual simulation environment for developing, testing, and validating robotics algorithms, and for training AI models. It significantly reduces the risks and costs of real-world testing and can generate vast, diverse synthetic training data on demand.
Isaac ROS/SDKs	Robotics application development software kits, including libraries for perception, navigation, manipulation, etc.	Provides a suite of modular robotics software functional packages and API interfaces, deeply optimized for NVIDIA hardware platforms. It covers the complete development chain from low-level hardware drivers to high-level application logic, aiming to accelerate the development, integration, and final deployment of robotics applications.
AI Training Methods	Imitation learning, reinforcement learning to accelerate robot skill acquisition	Enables robots to learn more quickly, autonomously, and effectively master complex motor skills and decision-making strategies by learning from human demonstrations (imitation learning) or through self-optimization via trial-and-error and reward mechanisms during environmental interaction (reinforcement learning), to cope with increasingly variable real-world application scenarios.

Omniverse Digital Twins: Mirroring the Real World, Simulating Infinite Futures

The NVIDIA Omniverse platform is a highly revolutionary and forward-looking technological achievement. It is not a singular software application but rather a highly scalable, real-time 3D design collaboration and virtual world-building platform that supports physically accurate simulation and full-fidelity rendering. Its most compelling core concept and application value lie in its ability to help users across various industries create "Digital Twins" that are one-to-one correspondences with their real-world physical assets or processes, are physically accurate, and can achieve real-time data synchronization. The emergence and growing maturity of this technology are bringing disruptive operational model changes and unprecedented vast development opportunities to numerous critical industry sectors, including industrial design, engineering construction, manufacturing, supply chain management, urban planning, climate change research, and even entertainment media content creation.

The USD Protocol: An Open Standard Foundation for the Omniverse Platform

One of the key technological cornerstones that enable the Omniverse platform to achieve such powerful cross-tool collaboration and complex scene integration capabilities is its native and deep support for the Universal Scene Description (USD) format and framework, which was invented by animation giant Pixar Animation Studios and eventually open-sourced to the entire industry. USD is not merely a traditional static 3D file format; it is more akin to a powerful, universal language and standardized framework meticulously designed for describing, organizing, assembling, editing, layering, simulating, and collaboratively processing extremely complex 3D virtual world content.

Advanced features inherent in USD, such as non-destructive editing workflows, flexible layer stacking and overriding mechanisms, and efficient lazy loading strategies, allow assets, 3D models, materials, lighting setups, and animation sequences created in different specialized 3D content creation software tools (e.g., CAD design software common in industry, animation production software like Maya or 3ds Max, game development engines such as Unreal Engine or Unity) to be seamlessly and non-transitionally integrated into the Omniverse platform. This truly enables real-time collaborative design, simulation, and content creation workflows across different toolchains, professional teams, and geographical locations.

Applications of Industrial Digital Twins: From Design Optimization to Smart Manufacturing

Through the powerful Omniverse platform, enterprises of all types can create high-fidelity digital replicas—or "industrial-grade digital twins"—of their core physical assets. These could be a brand-new smart factory still in the planning and design phase, a complex automated production line involving hundreds of robots and sensors, a bustling modern warehouse logistics center that needs to operate with peak efficiency, or even an entire city requiring fine-grained management and sustainable development planning, or a vast natural environment needing ecological monitoring and climate change impact assessment. Within this safe, controllable, low-cost, and infinitely repeatable virtual digital twin world, engineering and design teams can conduct numerous design verifications, human factors engineering analyses, and iterative process optimizations before committing to massive investments in physical construction or retrofitting. Enterprise managers can simulate a wide variety of production operational scenarios (such as changing product models, adjusting production line layouts, or responding to sudden supply chain disruptions), accurately test system responses under extreme conditions, and proactively predict and mitigate potential bottlenecks or safety risks.

More revolutionarily, AI developers can safely and efficiently train and validate the AI models they have developed for autonomous robots or automated equipment within this highly realistic virtual environment (e.g., allowing Autonomous Mobile Robots (AMRs) to repeatedly learn optimal navigation paths and efficiently perform loading/unloading tasks in a virtual factory). They can then seamlessly deploy the fully validated and optimized AI models to their corresponding physical equipment in the real world. This significantly shortens the development and deployment cycle of AI applications and enhances their robustness and performance in real-world environments. This new design, development, testing, deployment, and operational model, based on digital twins and driven by data, can markedly improve a company's overall R&D efficiency, effectively reduce manufacturing costs, substantially shorten new product time-to-market, and ultimately help enterprises achieve smarter, more flexible, and more market-resilient production processes and superior operational management. Jensen Huang also noted with satisfaction during his speech that many innovative leading manufacturing enterprises in Taiwan are actively embracing this future-defining technological trend, pioneering the use of the NVIDIA Omniverse platform to explore and implement true Software-Defined Manufacturing and comprehensive smart factory solutions.

NVIDIA's Vision for Taiwan: Deepening Collaboration to Co-Create a New AI Landscape

In the final segment of his address, Jensen Huang dedicated a very significant portion of his time and spoke with genuine, heartfelt emotion, to re-emphasize the irreplaceable and absolutely critical role that Taiwan plays as a core hub in the global technology industry supply chain. He also took this opportunity to express NVIDIA Corporation's unwavering commitment to continuously deepen its presence in the Taiwanese market, expand the breadth and depth of its collaborations with local industry partners, and jointly create and share the boundless prosperity and developmental opportunities brought about by the AI era.

Nvidia Constellation: A Commitment to Deepening Roots in Taiwan

He then, with a tone full of pride and anticipation, excitedly announced to the global audience that NVIDIA Corporation is currently actively investing resources in Taiwan (the location mentioned in the speech was the Shilin Technology Park) to construct a brand-new, state-of-the-art design center and comprehensive office complex named "Nvidia Constellation". This significant initiative represents far more than just an expansion of NVIDIA's tangible asset investments in the Taiwan region. Its deeper strategic implication is that it clearly symbolizes NVIDIA's earnest desire to establish even more profound, extensive, and forward-looking close partnerships with Taiwan's world-class and vibrantly innovative semiconductor manufacturing industry (such as wafer foundries, packaging, and testing), IC design industry, server and various hardware contract manufacturing industries, precision system integration industry, and the burgeoning software development and application ecosystem of talented partners. The goal is to jointly embrace and seize the tremendous historical development opportunities presented by the rapidly approaching era of artificial intelligence. He firmly believes that through such close partnerships based on mutual trust, mutual benefit, and complementary strengths, coupled with continuous encouragement and investment in joint innovation and R&D efforts, they will undoubtedly be able to work hand-in-hand to relentlessly advance the future of global computing technology to new heights.

Conclusion: The Future of Computing is Here, with NVIDIA Leading the AI Revolution

CEO Jensen Huang's spectacular and content-rich keynote address at COMPUTEX 2025 has undoubtedly provided the global technology community and everyone paying attention to the future developments with a clear and systematic outline of a brand-new computing future—one that is comprehensively permeated and deeply driven by artificial intelligence technology, where software innovation and hardware architecture achieve unprecedented tight fusion, and where the virtual digital world and the real physical reality engage in seamless interaction and highly efficient collaboration.

From the revolutionary concept of "AI Factories" aimed at fundamentally reshaping the operational models and productivity frontiers of modern data centers, to the Isaac robotics platform dedicated to endowing physical robots with intelligent perception and autonomous action capabilities, and further to the Omniverse digital twin technology focused on accurately mirroring the complex real world and conducting infinite simulations and optimization explorations within it, NVIDIA Corporation is, by virtue of its decades-long deep technological accumulation and continuous iterative innovation in the field of accelerated computing, its precise insights and forward-looking strategic layout regarding the future development directions and core bottlenecks of artificial intelligence, and its exceptional engineering capabilities and ecosystem-building prowess demonstrated in complex system-level platform architecture design, deservedly standing at the pinnacle of this technological revolution that is sweeping the globe and will profoundly impact every aspect of human society, leading the development direction of the entire industry. And, as Jensen Huang confidently pointed out in his speech, all these exciting advancements and achievements are merely the prologue and overture to an even grander and more magnificent new era.

What are your unique perspectives and profound thoughts on the momentous new AI era envisioned by NVIDIA? Among the many dazzling advanced technologies and grand visions highlighted by CEO Jensen Huang in this keynote, which one excites you the most, or which do you believe holds the greatest potential for disruptive industry impact?