Taiwan’s Local Governments Are Racing to Adopt AI — But Compute Management Hasn’t Kept Pace
Taiwan’s government has identified Sovereign AI Infrastructure and Smart Government Data Governance as key pillars of its AI strategy, with the “New AI Ten Major Infrastructure Projects” driving AI infrastructure investments across local governments. However, most agencies still procure and manage GPU resources independently. As AI adoption expands, this fragmented approach creates several challenges:
- Resource Silos: GPUs are isolated by department, leaving significant compute capacity idle during off-peak hours while infrastructure costs continue to accrue.
- Resource Contention: Shared GPU pools often become bottlenecks, preventing high-priority workloads from accessing compute resources when needed.
- Limited Visibility: IT teams lack centralized insights into GPU utilization, making capacity planning and procurement decisions difficult.
- Governance Gaps: Without resource quotas, role-based access control, or workload isolation, sensitive government data may be exposed to unnecessary security risks.
AI-Stack: Turning Scattered GPUs Into One Shared Compute Pool
INFINITIX AI-Stack is an AI infrastructure middleware purpose-built for on-premises environments. It enables organizations to build a centralized GPU resource pool with unified resource management, intelligent scheduling, and optimized allocation.
Positioned between infrastructure and AI applications, AI-Stack unifies heterogeneous compute resources—including NVIDIA and AMD GPUs, NPUs, storage, and compute clusters—while providing standardized APIs and a consistent development environment. This allows data scientists, AI engineers, and business teams to access AI tools and frameworks seamlessly, accelerating AI development and deployment.
Admin Console | Centralized AI Resource Governance
- Resource Quotas: Set GPU, vCPU, and RAM limits by project or department.
- Smart Resource Reclamation: Automatically reclaim idle GPUs and remove inactive containers.
- Real-Time Monitoring: Track GPU utilization, cluster health, and usage insights through a unified dashboard.
- Security & Governance: Enforce project isolation, role-based access control (RBAC), audit logs, and multi-factor authentication (MFA).
User Workspace | Flexible Compute for Faster AI Development
- Flexible GPU Allocation:
- Shared Mode: Run multiple AI inference workloads on a single GPU.
- Dedicated Mode: Reserve full GPU resources for high-performance computing.
- Multi-GPU & Multi-Node: Scale across GPUs and nodes for large models and demanding workloads.
- One-Click AI Environments: Instantly deploy standardized AI development environments.
- Intelligent Job Scheduling: Automatically run batch workloads during off-peak hours to maximize GPU utilization.
Use Case | Handling Peak Tax Season Without Buying More GPUs
During tax season, local governments experience a sharp surge in AI workloads. Large volumes of tax filings drive demand for AI-powered services such as OCR document processing, document summarization, and RAG-based citizen assistance.
With AI-Stack’s resource sharing, intelligent scheduling, and on-demand GPU allocation, governments can dynamically reallocate existing compute resources to meet seasonal demand—eliminating the need for additional GPU procurement while maximizing utilization, reducing infrastructure costs, and ensuring reliable AI services during peak periods.
| Scenario | Without AI-Stack | With AI-Stack |
| Peak Tax Season | AI workloads surge, causing GPU bottlenecks. | Dynamically allocate GPUs to support peak demand. |
| Resource Allocation | Purchase extra GPUs for short-term needs, causing idle resources afterward. | Pool existing GPUs and optimize resource utilization. |
| Cross-Department Sharing | Departments manage GPUs separately with no resource sharing. | Share idle compute across departments when needed. |
| After Peak Period | Newly purchased GPUs become underutilized. | Automatically reclaim resources for other AI workloads. |
Benefits | Maximizing AI Infrastructure Efficiency and ROI
By leveraging resource pooling, intelligent scheduling, and automated management, AI-Stack helps government agencies maximize GPU utilization and reduce AI infrastructure costs.
- Increase GPU Utilization from 30% to 90%
Optimize existing GPU resources through GPU partitioning, shared allocation, and off-peak workload scheduling. - Reduce AI Environment Setup Time from 2 Weeks to 1 Minute
Accelerate AI development and testing with standardized container environments and automated deployment. - Achieve 10× Higher ROI
Enable cross-department GPU sharing to reduce redundant procurement and maximize infrastructure investment value.
