The global race to build AI infrastructure continues to intensify, and competition among AI factories is no longer defined by raw compute alone — it increasingly hinges on end-to-end deployment and operational capability. As AI cloud and compute-service providers race to adopt each new generation of GPUs, the ability to convert hardware and compute capacity into usable, operable AI services faster has become the key lever for shortening time-to-market and time-to-revenue. At present, however, most AI cloud operators focus narrowly on compute supply, while traditional OEM offerings are largely built for enterprises' internal use. One-stop solutions that integrate hardware, software, and an operating platform to help service providers stand up operations quickly remain relatively scarce.

To address this gap, ASUS has built the ASUS Full-Stack AI Factory, integrating hardware, software, and a self-service, open-source operating platform, with the ASUS AI Software Stack underpinning the AI factory's overall operation. Built around the principle of one software stack, from plan to production, it connects deployment, management, and service operation into a single continuous workflow, giving service providers one-stop capability that reduces cross-platform integration complexity, accelerates deployment and service launch, and gets AI factories into production — and generating tokens — faster.

To accelerate this transition, ASUS is introducing the ASUS AI Platform, which provides GPU resource management and operational mechanisms that help customers rapidly stand up AI service operations and convert compute capacity into revenue. On the application side, ASUS is simultaneously introducing a governance framework built on ‘harness engineering’ to strengthen the control, orchestration, and governance of AI agents, with ASUS Professional Services rounding out an end-to-end delivery process that accelerates AI factory deployment and commercialization.

ASUS AI Platform: Turning GPU Compute into a Service, Accelerating AI Commercialization

As the core software layer of the ASUS Full-Stack AI Factory, the ASUS AI Software Stack is composed of multiple software layers spanning the AI factory's full lifecycle — from deployment through operations to service delivery. Within this stack, the ASUS Infrastructure Deployment Center (AIDC) handles AI data center deployment, and ASUS Control Center Data Center Edition (ACC DC) supports subsequent operations and management. Building on this foundation, the newly added ASUS AI Platform extends the stack further into service operation and commercialization, converting GPU compute and models into externally operable AI services. Its core functionality spans three areas:

• Resource Management — Supports Kubernetes-based GPU workload scheduling combined with multi-tenant isolation and project-level quota mechanisms, allocating GPU resources according to user and workload requirements. Telemetry tracks GPU utilization and job status in real time, improving scheduling efficiency and resource utilization.

• MLOps Portal — A self-service MLOps entry point that lets individual users and tenants access AI development tools and model services directly. It integrates Jupyter Notebook, model training and fine-tuning, model registration and version management, and vLLM-based inference serving, covering the full model lifecycle from development and training through version management and inference, and accelerating model rollout and service delivery.

• Quota & Billing — Combines resource quota management with GPU usage metering to track consumption by user and tenant, integrating rate configuration, cost allocation, and tenant billing to help emerging cloud service providers establish a complete AI service operating model.

Harness Engineering: Strengthening Enterprise AI Governance

Enterprises are shifting from ‘all in AI’ to ‘AI in all’, pushing AI agents progressively deeper into workflows and job functions. As agent deployment expands, ensuring that agents operate safely and within enterprise governance requirements has become a central concern. The ASUS Turnkey AI Application service is built specifically for this need, applying harness engineering as its core design principle to strengthen the control, orchestration, and governance of AI agents and ensure their operation remains controllable, traceable, and auditable.

ASUS embeds governance directly into the AI agent's task workflow, allowing enterprises to define upfront which models, data, and tools an agent may use. Before task execution, model routing selects the appropriate on-premises or cloud model based on use case, cost, and data sensitivity, while access to data, permitted tools, and executable actions are constrained. These controls are paired with security mechanisms such as least-privilege access, tool allowlisting, and sandboxing to reduce the risk of privilege escalation or misuse.

Once a task enters execution, real-time monitoring and risk control take over. High-risk operations can require manual approval, and any detected anomaly can immediately block the action or halt the task, keeping the agent's operation fully visible and monitorable throughout. AI-generated code must pass manual review and automated testing before deployment. After task completion, the source of the AI's response and key data points can be traced, preserving a complete audit trail. Governance rules themselves can be adjusted to match an enterprise's security policy and specific use case, ensuring alignment with existing security and control requirements.

ASUS Professional Services: Accelerating AI Factory Launch Through End-to-End Delivery

AI infrastructure investment decisions today are no longer about a single server — they hinge on whether an entire AI factory can be built and brought into operation quickly. Taiwan has long held a strong position in hardware manufacturing and systems integration, but as AI infrastructure moves toward large-scale deployment, the real competitive edge lies in converting hardware capability into the ability to deliver a complete AI data center. This represents not just an incremental step but a significant capability leap — and one that ASUS continues to pursue. ASUS Professional Services is the vehicle for this capability, spanning five stages — Consulting & Design, Infrastructure Implementation, Performance Tuning, AI Platform Deployment, and Lifecycle & Maintenance — that run through the entire AI factory lifecycle from planning to operational launch, shortening overall delivery time.

This end-to-end delivery capability is built on technical and operational experience accumulated through long-term involvement by ASUS in large-scale computing projects. From Taiwania 2 and Forerunner 1 (創進一號) to the recently deployed Nano4 system under NCHC's Jingchuang 26 (晶創26) program, ASUS has continued to deepen its experience building large-scale computing systems. The Nano4 system, based on NVIDIA H200 architecture, ranked 29th globally on the TOP500 list with 81.55 petaflops of compute performance and 73rd on the Green500 list, demonstrating strong compute performance alongside energy efficiency and substantially improving build cost-effectiveness. Through successive large-scale computing system deployments, ASUS has progressively built cross-system integration experience spanning servers, racks, thermal management, power delivery, and networking, up through management software, model deployment, and resource scheduling.

From AI Factories to Trusted AI

As AI infrastructure continues to grow in scale and complexity, ASUS has folded this operational experience into ASUS Professional Services, developing end-to-end delivery capability spanning planning, deployment, tuning, and operations. This allows ASUS to deliver not only high-performance, scalable compute platforms, but also the data center management and deployment tooling that helps customers accelerate AI factory build-out, operational launch, and commercialization.

Once an AI factory reaches operational status, enterprise attention shifts from infrastructure toward the deployment and governance of AI agents. As agents move deeper into critical enterprise and national-scale functions, increasing their autonomy while ensuring that ultimate control remains with people and organizations has become central to trustworthy AI deployment. Over the coming year, ASUS plans to move beyond isolated AI proof-of-concepts toward a verifiable, replicable, exportable Taiwan reference architecture for trustworthy AI — integrating servers, data, models, AI harnesses, security governance, and industry applications, built around data sovereignty, supply chain transparency, and AI governance, and validated through flagship deployments in key industries before extending the model globally.

• Read More: NCHC Nano4 — Taiwan’s Liquid-Cooled AI Supercomputer Powered by ASUS