From GTC Taipei to Computex 2026: Taiwan’s Strategic Role in the AI Supply Chain
Original Article By SemiVision Research [Reading time: 41 mins]
From GTC Taipei to Computex 2026: Taiwan’s Strategic Role in the AI Supply Chain
Computex 2026, held in Taipei in June 2026, featured a GTC Taipei keynote personally hosted by NVIDIA CEO Jensen Huang, putting Taiwan’s supply chain on the global AI stage. At the opening, Huang emphasized the feeling of “coming home” and thanked Taiwan’s vast ecosystem of partners. This two-hour keynote not only drew global attention to the full-scale development of AI across data centers, consumer electronics, and the physical world, but also provided an in-depth analysis of the Agentic AI era.
During the keynote, NVIDIA announced several major products and platforms, including the DSX AI Factory, the Vera Rubin platform, Vera CPU, RTX Spark AI PC, Nemotron 3 Ultra model, Cosmos 3 Physical AI model, and Isaac GR00T robotics platform. NVIDIA also emphasized that Taiwan is at the heart of the AI revolution, with a supply chain spanning 150 Taiwanese partners, more than 350 factories, and 30 countries.
Below Paywall, we provide the latest updates on selected Taiwanese companies.
Core Themes of GTC Taipei 2026
1. Agentic AI: AI Has Moved from Generation to Execution
During the GTC Taipei keynote, NVIDIA CEO Jensen Huang declared that AI has entered the Agentic era. He pointed out that a standalone large language model can only answer questions, while an agent combines models with a safety framework, or Harness, allowing AI to understand tasks, observe environments, reason, and use tools to complete work. NVIDIA’s Agent Toolkit provides models, runtime, and tool layers to help enterprises build trustworthy agents on their own workloads.
The essence of Agentic AI is a fundamental shift in the computing model. Traditional computing is application-centric: users open an app and enter commands. In the future, users will simply describe their intent, and agents will automatically generate code, call tools, and complete tasks. This intent-to-code model will cause compute, memory, tools, and network resources to switch frequently, requiring data centers to be redesigned at the system level, from chips and racks to power, cooling, and networking.
Huang noted that over the past three years, people have continued to question the practical value of AI, while generative AI has already proven itself in content creation and natural language processing. He further emphasized that Agentic AI is the next computing model: agent = LLM + Harness. A standalone large language model is merely a brain that can chat. To allow AI to truly work for humans, it must be paired with a framework capable of calling tools, managing memory, and executing plans.
This two-layer architecture includes a reasoning/planning model at the bottom and an orchestration engine on top. Users only need to express their needs through voice or text, and AI agents can automatically call software tools to complete tasks.
2. DSX AI Factory: From Data Center to AI Factory
NVIDIA introduced the concept of the DSX AI Factory, arguing that modern data centers are transforming into AI factories that generate intelligence. These factories integrate five layers: energy, chips, networking, software models, and applications. DSX includes the DSXSim design and validation platform and the DSXOS operating system. Before an AI factory is built, Omniverse can be used to simulate facility layout, power, cooling, and network topology. During actual operation, DSXOS is responsible for allocation, monitoring, and remediation, turning the AI factory into an operable computing asset.
The economic significance of the AI factory lies in the concept of “compute per watt as revenue.” As agentic workloads rise sharply, token output becomes a source of enterprise revenue. Investors are no longer comparing the price of a single GPU, but rather how many tokens and how much inference throughput can be generated under a fixed power budget. Therefore, the efficiency of power, cooling, networking, and memory will directly determine the return on investment of AI factories.
DSX is NVIDIA’s end-to-end AI factory solution. It integrates energy, chips, networking, software models, and applications into a five-layer architecture, with the goal of building and operating AI factories at the lowest token cost per watt.
The platform provides tools such as DSX OS, Omniverse digital twins, and DSX Sim. These tools can simulate power, cooling, and network layouts before data centers are built, and monitor resource allocation and dynamic power distribution after deployment. This “build it right the first time” capability can significantly reduce the trial-and-error cost of massive AI factories, where each gigawatt-scale facility may require an investment of US$8 billion to US$10 billion.
3. Vera Rubin Platform and Vera CPU
Vera Rubin is NVIDIA’s next-generation pod-level supercomputer designed for Agentic AI. It consists of five racks and combines the Vera Rubin GPU, Vera CPU, Groq 3 LPU, BlueField-4 STX storage, and Spectrum 6 SPX Ethernet systems into a fully integrated platform.
The Vera Rubin system features 18 compute trays, 9 hot-swappable NVLink switch trays, high-efficiency liquid-cooling manifolds, and busbars. A single MGX rack contains approximately 1.3 million components.
Vera CPU is a new processor designed specifically for Agentic AI. It features 88 cores, 1.2 TB/s of LPDDR5X memory bandwidth, and an on-chip interconnect fabric reaching 3.6 TB/s. Huang emphasized that the future role of the CPU in AI factories will shift from a simple compute core to a “conductor” that coordinates GPUs, manages memory, and handles tool invocation, supporting agent planning and reasoning with extremely low latency.
Full production of Vera Rubin has already begun. Assembly time has been reduced from two hours in the previous generation to five minutes, demonstrating the importance of modular design in improving manufacturing efficiency and reliability.
Vera Rubin: An AI Factory Built for Agentic AI
Vera Rubin is NVIDIA’s largest pod-level platform to date. It is a massive supercomputer composed of five dedicated racks, including the Vera Rubin NVL72 system, Vera CPU, Groq 3 LPX low-latency processor, Vera BlueField 4 STX storage security system, and Spectrum 6 SPX Ethernet rack.
Each rack adopts a cableless, pipeless, and fanless design. Through the use of PCB midplanes, assembly time per rack has been reduced from two hours to five minutes. Compared with the previous Grace Blackwell generation, Vera Rubin delivers a 10x improvement in agent throughput.
Supply Chain Scale and Partners
The success of Vera Rubin depends on a vast supply chain. NVIDIA stated that the supply chain built for Vera Rubin is twice the scale of Grace Blackwell, involving 150 Taiwanese partners across more than 350 factories in 30 countries.
Major system and storage partners include Dell Technologies, HPE, Lenovo, Supermicro, AIC, ASRock Rack, ASUS, Compal, Foxconn, GIGABYTE, Inventec, MiTAC Computing, MSI, Pegatron, Quanta Cloud Technology, Wistron, Wiwynn, and others.
In addition, Quanta Computer founder Barry Lam, TSMC CEO C.C. Wei, and other Taiwanese industry leaders met with Jensen Huang during GTC, highlighting NVIDIA’s deep connection with Taiwan’s manufacturing ecosystem.
Vera CPU: A Processor Built for Agents
Vera CPU is built on the Olympus Core architecture, emphasizing single-thread performance, high bandwidth, and energy efficiency. Its 88 cores provide 1.2 TB/s of LPDDR5X memory bandwidth and a 3.6 TB/s on-chip fabric. The single-die design avoids the latency introduced by traditional chiplet architectures.
NVIDIA emphasized that the number of future agents will far exceed the number of humans. Therefore, CPUs must reduce latency to avoid slowing down expensive GPUs’ token-generation efficiency. For this reason, Vera CPU prioritizes single-core performance and available bandwidth per core, rather than simply increasing core count.
In enterprise application testing, Vera CPU is approximately three times faster than traditional x86 CPUs in SQL workloads, and improves real-time data stream processing speed at the New York Stock Exchange by roughly six times. This demonstrates its advantage in database queries and real-time data analytics.
4. RTX Spark: Redefining the Personal Computer
Beyond data centers, NVIDIA also partnered with MediaTek to launch the RTX Spark chip. This personal AI PC platform features a 20-core CPU, 6,144 CUDA cores, and 200 TOPS of local AI compute. The first wave of products is expected to launch in fall 2026, offering ultra-thin 14- to 16-inch laptops with a thickness of around 14 mm.
The core significance of RTX Spark is that it enables PCs to securely run local agents. Agents can use tools locally, connect to cloud or on-premises models, and rely on sandbox mechanisms to prevent data leakage. This not only creates demand for high-performance CPUs/GPUs, cooling, and memory among Taiwan’s notebook ODMs, but also pushes AI PCs beyond the NPU performance race toward CUDA ecosystems and local agent platforms, expanding the value-add of the PC supply chain.
Hardware Characteristics
RTX Spark is an AI PC platform jointly developed by NVIDIA and MediaTek. It uses TSMC’s 3nm process and integrates a Blackwell RTX GPU with 6,144 CUDA cores and FP4 Tensor Cores capable of up to 1 PFLOPS of AI performance, along with a 20-core Grace CPU. It connects through NVLink C2C and supports 128 GB of unified memory.
The platform supports Windows and is fully compatible with CUDA. Its design goal is to create an always-on device built specifically for personal agents.
The New Role of the PC
RTX Spark signals that the future PC will no longer be just a device for running applications. It will become a “home AI server” that runs AI agents, manages personal data, and coordinates tools.
NVIDIA predicts that every person will have multiple AI agents, and every household will need an always-on AI PC. As a result, the PC market will no longer be limited by the number of people, but will expand with the number of agents.
This also means that PCs will become part of NVIDIA’s product roadmap. In the future, every generation of data center GPU architecture will have corresponding versions for desktops, notebooks, and workstations.
5. Enterprise AI Toolkit and Enterprise Agents
NVIDIA introduced a complete Enterprise AI Toolkit that enables companies to build their own agent operating systems. The major components include:
Model layer: The Nemotron 3 Ultra model adopts a Mixture-of-Experts architecture and is trained for long-horizon reasoning and tool-calling tasks. It delivers inference speeds five times faster than competing models and reduces cost by 30%.
Harness layer: Agent Harness, such as OpenShell, is responsible for workflow orchestration and security management. It provides sandboxing, permissions, and security policies to ensure that agents can operate safely inside enterprises.
Tools/Skills layer: CUDA X Libraries, Cadence EDA tools, enterprise software, and other resources are made available as tools for agents to call. Through Skills, agents are taught how to use these tools. NVIDIA noted that agents will become the most important users of tools, and the value of software companies will rise as agents use their tools more frequently.
Runtime layer: OpenShell provides a secure execution environment that can run in the cloud, on enterprise servers, or on devices.
Through these components, enterprises can build Super Agents based on their own knowledge. For example, NVIDIA’s collaboration with Cadence on a Chip Design Super Agent automates RTL generation, testbench creation, regression testing, and debugging. Processes that previously took weeks can now be shortened to hours, improving efficiency by more than 40 times.
6. Physical AI and Cosmos 3
Agentic AI is also extending into the physical world. At GTC, NVIDIA introduced Cosmos 3, an Omnimodel capable of reasoning, world simulation, and action generation using multimodal data such as vision, motion, and language.
The model comes with an open-source toolkit that simplifies data generation and simulation workflows, helping developers build robots and autonomous systems that can understand, reason about, and simulate the physical world.
The biggest bottleneck for Physical AI is the lack of first-person operational data. NVIDIA addresses this through teleoperation, simulation, and world models that generate physically consistent synthetic data, turning compute into data.
Cosmos 3 uses a hybrid Transformer architecture to process images, actions, audio, and text, generating worlds that conform to physical constraints. NVIDIA also introduced the Isaac GR00T humanoid robotics platform and the Alpamayo autonomous driving reasoning model, extending Agentic AI into robotics and self-driving vehicles.
Physical AI: From Virtual to Physical
Cosmos 3 Omnimodel
NVIDIA introduced Cosmos 3, an Omnimodel that learns from teleoperation, simulation, and third-person video to understand the world. The model has visual reasoning, world simulation, and action-generation capabilities, making it suitable for robotics and autonomous systems.
Cosmos 3 includes an open-source toolkit that simplifies data generation, simulation, training, and validation workflows, helping developers build robots that can understand and simulate the physical world.
Isaac GR00T Robotics Platform
Isaac GR00T is a humanoid robotics development platform that integrates the Jetson Thor computing platform, Isaac Lab simulation environment, Isaac Teleop demonstration learning, Omniverse simulation system, Cosmos World Model, Isaac ROS Runtime, and more.
Together, these components form a complete closed loop from data generation and model training to deployment. The platform also provides reference designs, allowing research institutions and enterprises to begin robotics development without having to build the underlying system from scratch.
Alpamayo 2 Autonomous Driving Platform
In autonomous driving, NVIDIA introduced the Alpamayo 2 open model and the Hyperion vehicle platform. Alpamayo 2 integrates perception, understanding, reasoning, and planning into a single model. Hyperion provides compute resources and sensor integration, while the Halos Operating System handles task scheduling and vehicle control.
Together, these three components form a complete autonomous driving architecture.
Taiwan’s Supply Chain and Industrial Opportunities
Taiwan’s Role in the Global AI Supply Chain
Taiwan is a critical hub in the global AI supply chain. Reuters has noted that Taiwan plays a pivotal role in the global AI supply chains of companies including NVIDIA and Apple, with its position reinforced by companies such as TSMC. NVIDIA plans to invest approximately US$15 billion per year in Taiwan and build a new headquarters in the Beitou-Shilin Technology Park, designed to accommodate 4,000 employees and expected to begin operations in 2030. Jensen Huang has described Taiwan as the “epicentre” of the AI revolution, and this move will further strengthen NVIDIA’s collaboration with manufacturing partners such as TSMC, Quanta, and Foxconn.
Beneficiaries Across the Supply Chain
Semiconductor Manufacturing and Packaging & Testing
Vera Rubin, Vera CPU, and RTX Spark are all manufactured by TSMC using N3 and CoWoS technologies. Testing and packaging are supported by Taiwanese companies such as KYEC and Powertech Technology. The Vera rack design reduces copper wire usage and shortens assembly time, highlighting Taiwan’s advantages in hardware manufacturing and modular system assembly.Memory and Storage
Agentic AI requires extremely high bandwidth, driving strong demand for LPDDR5X and HBM4. Original manufacturers such as Micron, Kioxia, and SanDisk are expected to benefit, while Taiwan’s NAND module supplier Phison and packaging and testing company Powertech Technology may also see opportunities. The high-speed networking and storage processing required by Vera BlueField 4 STX and Spectrum 6 SPX could further benefit memory suppliers such as Nanya Technology and Elite Semiconductor Microelectronics Technology.BMC / IPMI Controllers
AI racks require BMC management and remote monitoring. Taiwan’s ASPEED is the world’s largest supplier of BMC controllers, and its product penetration is expected to rise alongside growing demand for AI server racks.Thermal Management and Power
Vera Rubin and LPU racks adopt liquid-cooling designs, increasing the penetration of both liquid-cooling and air-cooling thermal solutions. Taiwanese thermal solution providers such as Auras, Fujikura, and Jentech have strong competitiveness in cold plates, cold heads, and thermal modules. Power supply companies such as FSP Group and Delta Electronics are also positioned to benefit from rising demand for high-power racks.Server and PC Assembly
The launch of RTX Spark repositions the PC as a home AI server in the era of agentic AI. Taiwanese PC and notebook makers such as ASUS, MSI, ASRock, Quanta, Compal, Pegatron, and GIGABYTE are expected to launch laptops and desktops equipped with RTX Spark. Major server ODMs including Foxconn, Inventec, Quanta QCT, Wistron, and Wiwynn have already partnered with NVIDIA to produce Vera Rubin systems and MGX servers.Networking and Communications
The Spectrum 6 SPX Ethernet rack adopts 200G CPO technology, creating potential opportunities for Taiwanese optical communication suppliers such as Coretronic, Lextar, and Lite-On. The storage acceleration functions of BlueField 4 STX require high-speed chips and FPGA-related solutions, areas where Taiwanese companies such as Accton and MiTAC may provide relevant support.
Driven by the rise of agentic AI and the concept of AI factories, Taiwan’s supply chain showcased a complete ecosystem at Computex 2026, covering chip manufacturing, advanced packaging, system assembly, racks, thermal management, power infrastructure, and end applications.
Below, we provide the latest updates on selected Taiwanese companies at COMPUTEX 2026
