Taiwan’s Industry Titans Turbocharge World’s AI Infrastructure Buildout With NVIDIA

<div id="bsf_rt_marker"></div><p><span style="font-weight: 400;">Taiwan is home to more than 500 NVIDIA ecosystem partners. More than 1 million NVIDIA MGX rack components for NVIDIA Vera Rubin infrastructure come together in Taiwan, from across 25 factory sites.</span></p> <p><span style="font-weight: 400;">As Vera Rubin ramps into full production to power agentic AI factories worldwide, that ecosystem spans the full supply chain — from key wafer and chip partners such as </span><span style="font-weight: 400;">TSMC</span><span style="font-weight: 400;">, </span><span style="font-weight: 400;">SPIL</span><span style="font-weight: 400;">, </span><span style="font-weight: 400;">Kinsus</span><span style="font-weight: 400;">, </span><span style="font-weight: 400;">KYEC</span> <span style="font-weight: 400;">and </span><span style="font-weight: 400;">UMTC</span><span style="font-weight: 400;">, to manufacturing and systems leaders including </span><span style="font-weight: 400;">Foxconn</span><span style="font-weight: 400;">, </span><span style="font-weight: 400;">Pegatron</span><span style="font-weight: 400;">, </span><span style="font-weight: 400;">Quanta Cloud Technology (QCT)</span><span style="font-weight: 400;">, </span><span style="font-weight: 400;">Wistron </span><span style="font-weight: 400;">and </span><span style="font-weight: 400;">Inventec</span><span style="font-weight: 400;">.</span></p> <p><span style="font-weight: 400;">But these partners are doing more than building AI factories. They’re applying accelerated computing, simulation, AI agents and physical AI to their own operations, creating a model for how AI can make advanced manufacturing faster, more efficient and adaptive.</span></p> <p><b>Taiwan’s Manufacturing Leaders Build the Future of AI, With NVIDIA AI</b></p> <p><span style="font-weight: 400;">Across chipmaking, server assembly and factory operations, Taiwan’s manufacturing leaders are applying NVIDIA technologies to reshape how AI infrastructure is designed, built, tested and scaled. </span></p> <figure id="attachment_93605" aria-describedby="caption-attachment-93605" style="width: 1200px" class="wp-caption aligncenter"><img decoding="async" class="size-large wp-image-93605" src="https://blogs.nvidia.com/wp-content/uploads/2026/05/TSMC-CPTX26-1680x945.png" alt="" width="1200" height="675" srcset="https://blogs.nvidia.com/wp-content/uploads/2026/05/TSMC-CPTX26-1680x945.png 1680w, https://blogs.nvidia.com/wp-content/uploads/2026/05/TSMC-CPTX26-960x540.png 960w, https://blogs.nvidia.com/wp-content/uploads/2026/05/TSMC-CPTX26-1280x720.png 1280w, https://blogs.nvidia.com/wp-content/uploads/2026/05/TSMC-CPTX26-1536x864.png 1536w, https://blogs.nvidia.com/wp-content/uploads/2026/05/TSMC-CPTX26-1290x725.png 1290w, https://blogs.nvidia.com/wp-content/uploads/2026/05/TSMC-CPTX26-630x354.png 630w, https://blogs.nvidia.com/wp-content/uploads/2026/05/TSMC-CPTX26-300x169.png 300w, https://blogs.nvidia.com/wp-content/uploads/2026/05/TSMC-CPTX26-400x225.png 400w, https://blogs.nvidia.com/wp-content/uploads/2026/05/TSMC-CPTX26.png 1920w" sizes="(max-width: 1200px) 100vw, 1200px" /><figcaption id="caption-attachment-93605" class="wp-caption-text">Image courtesy of TSMC</figcaption></figure> <p><span style="font-weight: 400;">TSMC</span><span style="font-weight: 400;"> is applying </span><a target="_blank" href="https://www.nvidia.com/en-us/technologies/cuda-x/"><span style="font-weight: 400;">NVIDIA CUDA-X</span></a><span style="font-weight: 400;"> libraries and AI models across computational lithography, transistor and process simulation, advanced process control, yield analysis, fab operations and inspection. NVIDIA cuLitho improves cost-effectiveness or cycle time by 20-50% over CPU-based computational lithography at the same cost of ownership, while the NVIDIA cuEST library improves semiconductor material simulation by 50x on average, cuML library, Metropolis platform and TAO Toolkit help accelerate material simulations, improve process control and strengthen rare-defect inspection.</span></p> <p><span style="font-weight: 400;">Foxconn</span> <span style="font-weight: 400;">is using the new NVIDIA Factory Operations Blueprint and NemoClaw blueprints to build MoMClaw, its manufacturing operations management agent, connecting sensor and machine signals with specialized agents that give plant managers and operators real-time answers and action plans through a natural language interface with </span><a target="_blank" href="https://build.nvidia.com/openshell"><span style="font-weight: 400;">NVIDIA OpenShell</span></a><span style="font-weight: 400;"> privacy controls and safety guardrails. </span></p> <p><img decoding="async" class="aligncenter size-full wp-image-93602" src="https://blogs.nvidia.com/wp-content/uploads/2026/05/MomClaw_V03.gif" alt="" width="1920" height="1080" /></p> <p><span style="font-weight: 400;">Foxconn estimates an 80% speed up in root-cause analysis time, a 15% increase in labor productivity and a 10% decrease in machine failure rates.</span></p> <p>&nbsp;</p> <p><span style="font-weight: 400;"><img loading="lazy" decoding="async" class="aligncenter size-full wp-image-93599" src="https://blogs.nvidia.com/wp-content/uploads/2026/05/Foxconn-OV.gif" alt="" width="800" height="450" /></span></p> <p><span style="font-weight: 400;">Foxconn</span><span style="font-weight: 400;"> also uses DeepHow’s SOP Verification vision AI system using NVIDIA Cosmos and the </span><a target="_blank" href="https://build.nvidia.com/nvidia/video-search-and-summarization"><span style="font-weight: 400;">NVIDIA Metropolis Blueprint for video search and summarization (VSS)</span></a><span style="font-weight: 400;"> to gain greater visibility into complex manufacturing processes, resulting in improved manufacturing efficiency and boosting first pass yield by 3%. The company is also applying NVIDIA Isaac Teleop, Isaac Sim, Isaac Lab and ROS 2 to wheeled humanoid robots operating in its factories, supporting precision assembly tasks such as pick and place, dual-arm collaboration and force-controlled screw fastening.</span></p> <p><img loading="lazy" decoding="async" class="aligncenter size-full wp-image-93590" src="https://blogs.nvidia.com/wp-content/uploads/2026/05/LiveSOP_Verification.gif" alt="" width="960" height="540" /></p> <p><span style="font-weight: 400;">Foxconn</span><span style="font-weight: 400;">’s $1.4 billion AI cloud supercomputing center in Taiwan — powered by 10,000 NVIDIA GPUs — is being built with the NVIDIA GB300 NVL72 hybrid cooling architecture.</span></p> <p><span style="font-weight: 400;">Quanta Cloud Technology (QCT)</span><span style="font-weight: 400;"> is using NVIDIA Omniverse-based digital twins to accelerate factory planning, giving engineering, operations and logistics teams shared access to design data for faster layout feedback, optimized workflows and improved space utilization.</span></p> <p><img loading="lazy" decoding="async" class="aligncenter size-full wp-image-93587" src="https://blogs.nvidia.com/wp-content/uploads/2026/05/Untitled2-ezgif.com-optimize-1.gif" alt="" width="800" height="450" /></p> <p><span style="font-weight: 400;">QCT is also working with its subsidiary Techman Robot on a physical AI developer kit that uses QuantaGrid systems for data generation and model training. Techman Robot is using NVIDIA Jetson Thor and the Isaac GR00T platform to support the development of its next-generation robots, including the TM Xplore I humanoid, for advanced industrial tasks such as server fan assembly.</span></p> <p><span style="font-weight: 400;">Wistron </span><span style="font-weight: 400;">is using the </span><a target="_blank" href="https://build.nvidia.com/nvidia/omniverse-dsx-blueprint-for-ai-factories"><span style="font-weight: 400;">NVIDIA Omniverse DSX Blueprint</span></a><span style="font-weight: 400;">, the NVIDIA PhysicsNeMo framework and Cadence Reality DC Design to simulate burn-in environments for stress-testing across global manufacturing sites and to optimize AI server manufacturing. </span></p> <p><span style="font-weight: 400;">Running on </span><a target="_blank" href="https://www.wistron.com/en/Newsroom/2025-08-26"><span style="font-weight: 400;">Wistron’s NVIDIA AI infrastructure</span></a><span style="font-weight: 400;"> with </span><a target="_blank" href="https://www.nvidia.com/en-us/data-center/rtx-pro-6000-blackwell-server-edition/"><span style="font-weight: 400;">NVIDIA RTX PRO 6000 Blackwell Server Edition</span></a><span style="font-weight: 400;"> GPUs, NVIDIA Omniverse and NVIDIA Metropolis libraries, these workflows speed layout analysis by as much as 70% and cut facility power demand by 20% through dynamic rack optimization.</span></p> <p><img loading="lazy" decoding="async" class="aligncenter size-large wp-image-93583" src="https://blogs.nvidia.com/wp-content/uploads/2026/05/Pegatron-1680x938.png" alt="" width="1200" height="670" srcset="https://blogs.nvidia.com/wp-content/uploads/2026/05/Pegatron-1680x938.png 1680w, https://blogs.nvidia.com/wp-content/uploads/2026/05/Pegatron-960x536.png 960w, https://blogs.nvidia.com/wp-content/uploads/2026/05/Pegatron-1280x715.png 1280w, https://blogs.nvidia.com/wp-content/uploads/2026/05/Pegatron-1536x858.png 1536w, https://blogs.nvidia.com/wp-content/uploads/2026/05/Pegatron-scaled.png 2048w, https://blogs.nvidia.com/wp-content/uploads/2026/05/Pegatron-630x352.png 630w, https://blogs.nvidia.com/wp-content/uploads/2026/05/Pegatron-300x169.png 300w" sizes="auto, (max-width: 1200px) 100vw, 1200px" /></p> <p><span style="font-weight: 400;">Pegatron</span> <span style="font-weight: 400;">is adopting the NVIDIA Omniverse DSX Blueprint, developing simulation-ready assets, and connecting design data, thermal simulation, digital twins and physical qualification — accelerating the design and deployment of AI factories. </span></p> <p><span style="font-weight: 400;">Pegatron is also using NVIDIA’s Defect Image Generation physical AI agent skill with NVIDIA Cosmos world foundation models and Isaac Sim to generate synthetic defect data, reducing AI visual inspection deployment time by 67% and operational effort by 10%.</span></p> <p><img loading="lazy" decoding="async" class="aligncenter size-full wp-image-93580" src="https://blogs.nvidia.com/wp-content/uploads/2026/05/InventecAnomalyGen.gif" alt="" width="1080" height="608" /></p> <p><span style="font-weight: 400;">Inventec</span><span style="font-weight: 400;"> is using the Defect Image Generation agent skill in its Observation Agent to generate synthetic defect data for automated optical inspection. In notebook cosmetic inspection, internal validation produced more than 10,000 synthetic defect images and showed the potential to reduce real-world data collection and manual labeling by about 30%, shorten AI deployment time by about 25% and improve anomaly detection by about 10%.</span></p> <p><span style="font-weight: 400;">As NVIDIA Vera Rubin ramps into full production, Taiwan’s manufacturing leaders are showing how AI infrastructure becomes part of its own manufacturing engine — using accelerated computing, simulation, agents and physical AI to build the next generation of AI systems.</span></p> <p><i><span style="font-weight: 400;">Watch the </span></i><a target="_blank" href="https://www.nvidia.com/en-tw/gtc/taipei/keynote/"><i><span style="font-weight: 400;">GTC Taipei keynote</span></i></a><i><span style="font-weight: 400;"> from NVIDIA founder and CEO Jensen Huang and explore </span></i><a target="_blank" href="https://www.nvidia.com/en-tw/gtc/taipei/session-catalog/?tab.catalogallsessionstab=16566177511100015Kus&amp;search=STW61026%2C%20STW61028%2C%20STW61011%2C%20STW61066%2C%20STW61024%2C%20STW61062%2C%20STW61036#/"><i><span style="font-weight: 400;">physical AI sessions</span></i></a><i><span style="font-weight: 400;">.</span></i></p>