AI Infrastructure

AI infrastructure is a high-performance computing facility specifically designed to support the intensive workloads of AI model training and inference. Traditional data centers are typically CPU-centric, supporting general enterprise IT operations such as databases, virtual machines, and web services. While GPUs may be included, they are not the primary compute engine in most cases. In contrast, AI infrastructure is optimized for large-scale parallel computation, with GPU-based systems commonly used and often augmented by additional AI accelerators to enhance performance.

This high-density architecture poses challenges beyond compute performance—it demands higher throughput for storage and networking, as well as increased power consumption and cooling capacity. As a result, AI infrastructure often adopt advanced cooling technologies such as direct liquid cooling and immersion cooling, and are designed with additional physical space to accommodate GPU racks and thermal management equipment.

In short, AI infrastructure is a next-generation facility built to meet the exponential growth in AI computational needs. It is more than just an upgrade from conventional data centers—it is the foundation of the “AI Factory”that enables enterprises to scale and industrialize their AI capabilities.

From generative AI and AI agents to physical AI (such as robotics), AI applications are rapidly evolving across both software and hardware domains, reshaping the way we work and live. Whether it’s training large language models or continuously improving the decision-making logic of autonomous agents, all of these applications demand tremendous compute power. Only AI infrastructure can effectively support the training and inference workloads of such models.

According to McKinsey & Company, demand for AI-ready data center capacity is projected to grow at an average annual rate of 33% from 2023 to 2030. By 2030, approximately 70% of all data center capacity demand will be for infrastructure capable of supporting advanced AI workloads. Among these, generative AI—currently the fastest-growing use case—is expected to account for around 40% of total demand.

This forecast highlights the growing reliance on AI capabilities and underscores why building dedicated AI infrastructure has become a top strategic priority for enterprises.

GIGABYTE offers end-to-end AI infrastructure deployment services, encompassing consultation, design, implementation, validation, and ongoing operations. Its holistic approach integrates every layer—from hardware and software to cooling systems.

The GIGAPOD cluster computing platform is highly scalable, starting from a single GPU server and expanding up to 8 racks, 32 GPU nodes, and a total of 256 GPUs. This enables it to support not only large-scale AI applications but also complex scientific research. Paired with the GPM (GIGABYTE POD Manager) software, the platform enables real-time monitoring of hardware health and resource utilization, while dynamically allocating compute resources based on demand—greatly enhancing AI infrastructure management efficiency.

GIGABYTE also brings years of expertise in advanced cooling technologies, including full integration of cooling solutions. This ensures that enterprises can simultaneously optimize energy efficiency and performance—paving the way for sustainable, future-ready AI operations.

Reference:
1. McKinsey & Company, AI power: Expanding data center capacity to meet growing demand