Taiwan's AI Industry: Infrastructure Needs and Challenges

Summary

Taiwan, as a global hub for semiconductor and hardware manufacturing, holds a key position in the AI wave. However, for local AI startups and enterprises, obtaining high-performance, compliant, and cost-effective AI infrastructure still faces many challenges. This article provides an in-depth analysis of the current state of Taiwan's AI industry infrastructure needs.

I. Current State of Taiwan's AI Industry

1.1 Industry Clusters

• Hardware Manufacturing: TSMC, Foxconn, Quanta control the global AI server supply chain
• AI Startups: Focus on LLM applications, smart healthcare, smart manufacturing, and other vertical sectors
• Traditional Enterprises: Finance, retail, and manufacturing actively seeking AI transformation

1.2 Key Pain Points

Despite strong hardware manufacturing, "compute" as a service (Computing as a Service) still has gaps locally:

1. GPU Acquisition Difficulties: High-end chips like H100 have long lead times, making it difficult for SMEs to build their own
2. High Construction Costs: Building own data centers requires massive CapEx investment and high operational barriers
3. Data Sovereignty Concerns: While public cloud (AWS/GCP/Azure) has local presence, there are still compliance challenges under specific regulations (e.g., finance, healthcare)

II. Infrastructure Needs Analysis

2.1 Compute Needs

• Training: Requires large-scale, low-latency interconnected GPU clusters (e.g., H100 NVL)
• Inference: Requires cost-effective, low-latency edge computing nodes (e.g., L40S, A100)
• Flexibility: Pay-as-you-go model to adapt to R&D cycle fluctuations

2.2 Data Needs

• Localization: Sensitive data must remain within Taiwan (Data Residency)
• Transmission Bandwidth: Large training datasets require high-speed local direct connections (Direct Connect)
• Security: Must comply with ISO 27001, Cybersecurity Act, and other regulations

2.3 Energy and Sustainability

• Power Stability: AI training cannot be interrupted, requiring high-reliability power
• Green Energy Requirements: International supply chains require ESG compliance, need to use renewable energy or purchase green energy certificates

III. Solution Directions

3.1 AI Compute Leasing

Professional service providers build large-scale GPU clusters and provide them to enterprises through leasing:

• Advantages: Lower barriers, rapid activation, flexible scaling
• Trend: Combining bare metal (Bare Metal) with containerized (K8s) services

3.2 Hybrid Cloud Architecture

• Core Training: Conducted in local IDC or private cloud to ensure data security
• Flexible Inference: Utilize public cloud for global distribution
• Data Tiering: Sensitive data localized, de-identified data on cloud

3.3 Building-to-IDC Transformation

Utilize existing idle assets to transform into edge data centers:

• Advantages: Asset activation, shortened construction cycle, proximity to users
• Challenges: Power and cooling transformation (see our other article on Building-to-IDC case study)

IV. HuiSuan Global's Role

As an AI infrastructure service provider connecting Taiwan and Southeast Asia, we are committed to:

1. Providing Local Compute: Building high-density GPU nodes in Taiwan to serve local needs
2. Cross-border Scheduling: Connecting Southeast Asia's low-cost compute resources, providing large-scale training options
3. Technology Enablement: Providing end-to-end consulting services from data center construction to cluster optimization

V. Conclusion

Taiwan has a world-class ICT industry chain and also has great potential in the AI infrastructure services field. By integrating hardware advantages, software talent, and innovative business models, we have the opportunity to build an AI compute ecosystem with regional influence.

About HuiSuan Global
We provide professional AI compute leasing and IDC solutions. For inquiries, please contact contact@huisuanlabs.com