Taiwan will soon have something it has never had before: its own AI Factory — a facility built to produce intelligence at industrial scale. For a nation that has powered the world through semiconductors, systems engineering, and manufacturing excellence, we’re proud to be part of a shift from merely enabling global innovation to generating it.

With a $500 million investment, 7,000 NVIDIA GPUs across 96 high-density racks, close to two million tokens per second of processing capacity, and a 16-megawatt power draw, Taiwan now has the infrastructure to build, train, and deploy advanced AI systems on its own terms. The facility is expected to come online in March 2026.

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Media Event Recap

The media event felt like a boardroom-level gathering disguised as a press briefing. The front rows were packed with senior IT and digital leaders from semiconductors, telecom, and financial institutions— industry titans who came together for the historical announcement.

We’re grateful to the media outlets — Bloomberg, Reuters, Nikkei, major IT and business media — who showed up to cover Taiwan’s next major AI chapter.

The core message: the AI Factory isn’t a routine infrastructure upgrade, it’s Taiwan’s pivot from importing compute to producing it, and from relying on external platforms to owning the foundation of its next decade of AI capability. GMI Cloud is simply the first enabler — a Silicon Valley-born company now embedded across Asia’s telecom, finance, semiconductor, and emerging AI sectors, all of which have been straining under the need for consistent, high-density compute for LLMs and multimodal systems.

That rising pressure is what led to Taiwan’s first AI Factory, powered by NVIDIA Blackwell GPUs and designed to keep data local, guarantee capacity, and give regional industries a platform they can build on. This upcoming AI Factory facility isn’t a finish line, but the start of how Taiwan plans to compete in the global AI economy and equip its enterprises for the decade ahead.

The Demand for an AI Factory

Like other regions, Taiwan’s industrial base is evolving faster than its compute access. Without domestic AI infrastructure, the region has been forced to rely on overseas resources for workloads that are strategically sensitive, operationally urgent, or too large to move.

The AI Factory addresses several converging realities:

• Sovereign compute is no longer optional.
  From telecoms to government agencies, countries and regions are starting to realize critical workloads must stay within the region’s borders.
• Manufacturing competitiveness now depends on AI-integrated operations.
  Predictive maintenance, computer vision, and digital twins cannot depend on foreign compute cycles.
• Data from factories, energy grids, and IoT systems has outgrown traditional data centers.
  High-throughput, low-latency processing requires a fundamentally different architecture.
• Taiwan’s engineering ecosystem is uniquely capable of running an AI Factory.
  Decades of experience in thermal management, dense systems design, and high-reliability manufacturing made this facility possible.

What an AI Factory Actually Is

An AI Factory is not a “data center with more GPUs.” The difference is structural, intentional, and foundational. 

Traditional data centers host compute — they provide space, power, and networking so you can run workloads. An AI Factory produces intelligence — it is engineered to continuously generate, refine, and deploy AI models and inference the same way an industrial plant turns raw materials into finished goods.

This shift from hosting to producing reshapes every layer of the facility:

• High-density GPU racks designed for synchronized operation
  These racks aren’t arranged for generic virtualization. They’re built for full-stack GPU coordination, where thousands of accelerators operate as a single, coherent system rather than isolated nodes.
• Multimillion-token throughput as a baseline, not an aspiration
  The architecture is designed around predictable, high-volume AI output. Multi-million-token-per-second performance is the minimum acceptable operating range for large-scale inference and finetuning workloads.
• Compute, networking, and data pipelines tightly fused instead of loosely integrated
  The AI Factory eliminates the bottlenecks that plague traditional facilities. Data doesn’t “travel” across disconnected systems; it flows through a unified fabric optimized for model serving, multimodal processing, and real-time retrieval.
• Continuous inference and finetuning loops
  Models aren’t just deployed here — they evolve here. The Factory runs training, evaluation, finetuning, and inference in an ongoing cycle, enabling rapid adaptation to new data, new tasks, and new operational contexts.
• Infrastructure designed for sustained, industrialized AI output
  The facility operates the way a semiconductor fab or manufacturing line does: high throughput, low downtime, predictable performance, and tightly controlled conditions. The objective is not to “have GPUs available,” but to run AI as a reliable production process.

In short, an AI Factory is a national-scale intelligence engine, capable of generating and updating AI systems continuously. This is the environment Taiwan needs to build, deploy, and govern AI that reflects its own industrial priorities, security requirements, and regional ambitions.

Tech Ecosystem Showed Up Ready

Pre-launch customers and queued demand makes this AI Factory more interesting than other infrastructure buildouts. While other companies are building infrastructure with hypothetical or projected demand, GMI Cloud’s AI Factory will be serving customers with real demands on day one instead of waiting for hypothetical applications.

Trend Micro (with Magna AI) — Securing the Nation’s Digital Backbone

Trend Micro demonstrated digital-twin cybersecurity simulations capable of modeling real-world cyber threats without exposing production systems. For Asia — a region facing escalating digital risk — this improved capability will produce foundational shifts in how companies and governments can respond to digital threats.

Wistron — Bringing AI Onto Taiwan’s Factory Floors

Wistron outlined AI-powered vision systems, predictive maintenance, and digital twin deployments running directly on domestic production lines. This is a shift from hardware-first manufacturing to AI-integrated manufacturing — a competitive necessity for the next decade.

Chunghwa System Integration (CSI) — Delivering AI to Enterprises and Government

CSI detailed how the AI Factory will power AIoT solutions, telecom modernization, and government agency deployments. They will play a pivotal role in translating the region’s infrastructure into everyday operational capability.

VAST Data — Keeping Taiwan’s AI Pipelines Fed

VAST Data explained the need for exabyte-scale throughput and high-velocity storage to sustain continuous GPU workloads. In a country where real-time industrial data is the norm, this layer is indispensable.

TECO — Powering AI Responsibly and Sustainably

TECO showcased energy-optimized systems, modular DC infrastructure, and Energy-as-a-Service models. Taiwan’s AI ambitions will not come at the expense of its energy stability.

Reflection AI — Delivering Sovereign-Grade, Open-Weight Intelligence

Reflection AI outlined its frontier-level, open-weight models designed for mission-critical environments where customization, privacy, and operational sovereignty are non-negotiable. Backed by a research team drawn from DeepMind, OpenAI, and Anthropic, they emphasized deployments that run where sensitive data actually lives — from sovereign clouds to on-prem racks and secure facilities. 

Leadership Comments

CHS (Chunghwa System Integration): “Working with a company with this level of drive is energizing. The founder is only 30, yet he’s aiming to use Taiwan’s telecom backbone to unlock new AI opportunities. The potential is enormous.”

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‍Trend Micro: “We’re listed in Japan, but our R&D is rooted in Taiwan. Partnering with the GMI Cloud AI Factory makes our cybersecurity smarter—and it’s a meaningful contribution to Taiwan’s digital safety.”

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The Use Cases Reveal Taiwan’s Direction

Taken together, the use cases showed a broader pattern emerging across regions that are investing in sovereign AI capability. As industries adopt AI at scale, the center of gravity shifts from hardware production to intelligence production — and the infrastructure required to support it becomes part of the industrial base, not an auxiliary service.

Taiwan intends to become an AI production hub, not merely a hardware hub.
For decades, Taiwan’s advantage has been precision manufacturing and silicon excellence. Now the emphasis is shifting to what that hardware enables: large-scale intelligence generation, model deployment, and AI-integrated industrial workflows. The Factory is a declaration that Taiwan intends to own this next layer of value creation, not leave it to other regions.

Compute is becoming a core part of Taiwan’s industrial supply chain.
Manufacturers, semiconductor firms, telecoms, and energy providers all expressed the same reality: AI is no longer an “add-on.” It is becoming a requirement for competitiveness — and that means compute must be reliable, sovereign, and built into the region’s operational fabric the same way PCB fabrication and logistics flows already are.

Enterprises want rapid, locally controlled AI deployment cycles.
The days of waiting for offshore compute cycles — or exposing sensitive workloads to foreign jurisdictions — are ending. Taiwanese enterprises need sub-day iteration loops, on-demand model fine-tuning, and guaranteed throughput that matches real production timelines. Local access to an AI Factory compresses those cycles dramatically.

The public sector is preparing for AI-driven modernization that protects sovereignty.
Government agencies, energy operators, and telecoms made it clear: if AI is becoming infrastructure, then the infrastructure must live within the region. That means local model training, local inferencing, and local data governance. The Factory becomes a strategic asset, not just a technical one.

Manufacturing, energy, telecom, and cybersecurity are emerging as the first sectors to scale.
Computer vision on production lines, real-time digital twins, grid optimization, cyber threat simulation, AIoT rollouts, and next-gen telecom workloads are all preparing to move into the Factory the moment it comes online.

The media conference didn’t shy away from the friction points: energy orchestration, governance clarity, workload prioritization. What stood out wasn’t unanimity, but alignment — an understanding that these issues aren’t barriers, they’re the work that comes with scale. The conversations carried a tone of “we’re moving, and we’ll refine as we go,” which creates a natural bridge to the next phase of deployment.

What Happens Next

The path forward is taking shape with clear milestones and an unusually aligned set of corporate stakeholders preparing for the next phase.

March 2026: The AI Factory comes online.
Once the facility is live, the shift from construction to production begins. This is when the real testing happens: sustained throughput, multimodal workloads, and the first intelligence “runs” that will signal whether the region’s infrastructure can reliably operate at industrial scale.

Government and enterprise pilot programs begin.
Multiple agencies and enterprises have already indicated which workloads they want to move first — cybersecurity simulations, energy optimization models, large-scale retrieval systems, and AI-driven manufacturing flows. These early pilots will determine best practices for on-prem, hybrid, and Factory-native deployments.

Manufacturing and telecom integrations roll out.
Manufacturing lines will begin feeding vision data into Factory pipelines, enabling real-time digital twins and predictive maintenance models. Telecom operators will test large-scale AI inference across network management, customer operations, and 5G/IoT orchestration. These sectors will likely be the earliest proof points of Factory-scale ROI.

Additional regional partners join the ecosystem.
Several companies across components, energy, system integration, and security have already begun exploratory conversations. As soon as the Factory demonstrates stable production cycles, the ecosystem is expected to widen — not slowly, but in waves.

New sectors — finance, healthcare, logistics — begin evaluations.
Banks are exploring large-scale fraud detection and risk modeling. Hospitals want multimodal clinical support tools. Logistics firms are looking at route optimization, simulation, and computer-vision-driven warehouse intelligence. These sectors tend to move only when infrastructure is proven — but once it is, adoption accelerates quickly.

Regional policy alignment discussions accelerate.
Governments and regulators are beginning to evaluate how AI Factories fit into regional security models, data residency frameworks, industrial policy, and long-term energy planning. These conversations aren’t ancillary; they are becoming a central part of how AI infrastructure is governed at scale.

Together, these steps form the early architecture of a region preparing not just to use AI — but to produce it. The next phase will be defined by the workloads that start running, the industries that transition, and the operational lessons learned as the AI Factory becomes a living part of the region’s technological backbone.

A Foundation for an AI Future

This regional infrastructure milestone represents a shift in what the Asia region can choose to build for itself and what it can now choose to build for others.

Countries and companies across Asia are reaching the same inflection point Taiwan faced: traditional data centers are no longer enough, sensitive workloads can’t live offshore, and AI-integrated industries require infrastructure built specifically for the production of intelligence.

For organizations looking to build their own AI Factory — whether to secure sovereign compute, modernize national infrastructure, or accelerate industrial AI — GMI Cloud is already supporting partners in designing, deploying, and operating facilities modeled after Taiwan’s blueprint. The path forward is real, repeatable, and ready for those who want to take the same decisive step.

If your company or country is exploring its own AI Factory, GMI Cloud can help you build it — and build it fast.

Come talk to us about how you can also Build AI Without Limits.