Inside an AI Data Center: Key Specs and Cutting-Edge Hardware

As artificial intelligence (AI) continues to drive digital transformation, AI data centers are emerging as specialized facilities built to power advanced machine learning, deep learning, and high‐performance computing applications. In Jakarta—a major digital hub in Southeast Asia—EDGE DC stands out as a leading data center provider, offering cutting‐edge, scalable, and energy‐efficient infrastructure tailored for the next generation of AI applications.

In this article, we explore the key features, design specifications, and state-of-the-art hardware that define an AI data center.

Read more: Driving the Future: Integrating Renewable Energy into Data Centers

Understanding AI Data Centers

Unlike traditional data centers, AI data centers are purpose-built to support the intense computational and storage demands of AI workloads. They feature:

• High-Density Power Supply: AI data centers are designed to accommodate power-hungry components such as GPUs and accelerators. Facilities often boast dual power feeds, redundant generators, and power usage effectiveness (PUE) scores that reflect energy efficiency.
• Advanced Cooling Systems: With components like GPUs generating significant heat, cooling is critical. Modern AI data centers employ advanced solutions such as liquid cooling or immersion cooling to maintain optimal operating temperatures while ensuring uninterrupted performance. 
• High-Speed Networking: To support rapid data transfers between servers and accelerators, these centers integrate low-latency, high-bandwidth interconnects that enable seamless communication.
• Scalability and Redundancy: AI workloads can scale quickly. Facilities must offer modular designs with scalable rack configurations and redundancy in power and connectivity, guaranteeing 99.999% uptime.

These attributes distinguish AI data centers from conventional facilities by ensuring that power, cooling, and networking work together flawlessly to support thousands of GPUs and specialized accelerators.

1. Power and Energy Efficiency

• High Power Density: AI servers can require power capacities of 50–100 kW per rack. AI-ready facilities are engineered to support high IT load capacities, ensuring they can handle the power demands of AI training and inference. 
• Redundant Power Systems: Dual power feeds, N+1 generator configurations, and robust UPS systems minimize downtime risks. Overextended power grids are also pushing AI data centers to adopt microgrids with fuel cells, advanced batteries, and small modular reactors (SMRs) for energy resilience.
• Energy Efficiency Metrics: Achieving a low PUE (often near 1.24 or better) is essential for controlling operational costs and ensuring efficient energy usage. AI is expected to push global data center energy consumption to 3–4% of total electricity by 2030, up from 1–2% today, highlighting the need for renewable energy integration.

2. Cooling Solutions

• Liquid and Immersion Cooling: To efficiently remove the heat generated by high-density GPU deployments, AI data centers employ techniques such as direct-to-chip liquid cooling or immersion cooling. Immersion cooling can reduce energy use by 30% compared to air cooling, and hybrid systems (liquid-to-air/refrigerant) are becoming standard for AI racks.
• Optimized Rack Design: Specialized rack configurations enhance airflow and heat dissipation, which is critical for maintaining high performance in an AI environment. AI-driven cooling systems, powered by cognitive digital twins and predictive analytics, are optimizing cooling in real time to reduce energy waste.
• Heat Reuse: Some facilities are repurposing server heat for district heating systems, aligning with circular economy practices and improving overall sustainability.

3. Networking and Connectivity

• Low Latency and High Bandwidth: Advanced network equipment—incorporating high-speed switches and fiber connectivity—ensures that data flows between GPUs, CPUs, and storage systems with minimal delay. This is vital for AI workloads that rely on rapid, parallel data processing. Hyperscale data centers are now adopting 800G fiber infrastructure to support GPU-to-GPU communication, reducing latency and physical footprint.
• Global Carrier Interconnection: Strategic connectivity to multiple local and international carriers provides fast, reliable data access. EDGE DC, for example, offers rich connectivity options through links to major internet exchanges like IIX and EPIX. Regional edge hubs are also emerging to complement centralized AI “factories,” reducing latency for AI datasets.

4. Scalability and Modular Design

• Expandable Infrastructure: Modular data center designs allow quick scalability; additional racks, power capacity, and cooling can be integrated as AI workloads grow. This modularity is critical as AI workloads are projected to grow exponentially, requiring rapid infrastructure expansion.
• Virtual Campus Approach: Facilities such as EDGE DC’s interconnected sites (e.g., EDGE1 and EDGE2) enhance resource sharing, redundancy, and overall connectivity. This approach ensures seamless scalability and operational resilience.

Read more: Data Center Jakarta: Why Location and Latency Matter for Your Business

Hardware Spotlight: GPUs and Accelerators

At the heart of any AI data center are the GPUs and accelerators that enable rapid computation and model training. Recent innovations include DeepSeek AI’s cost-efficient approach, which optimizes NVIDIA H800 GPUs using techniques like Mixture-of-Experts (MoE) architecture, low-precision computation, and advanced load balancing to reduce training expenses. 

This reflects a broader industry trend where AI firms are refining hardware efficiency to lower costs while maintaining performance. As AI workloads scale, advancements like these will continue shaping the next generation of AI infrastructure.

Nvidia GPUs

• Nvidia A100 and H100: Widely deployed in data centers for AI training and inference, these accelerators deliver unmatched performance with high memory bandwidth and advanced features such as NVLink for inter-GPU communication.
• Nvidia Blackwell Architecture: The next generation of GPUs, built on Nvidia’s Blackwell architecture, promises up to four times the performance of current models—ideal for the increasingly complex AI workloads.

AMD Instinct Accelerators

• AMD Instinct MI300 Series: Designed for high-performance AI and HPC workloads, these accelerators leverage the CDNA 3 architecture with advanced packaging and high-bandwidth memory.

Other Critical Components

• High-Performance CPUs: AI servers often pair high-density GPUs with powerful CPUs (such as AMD EPYC or Intel Xeon processors) to manage orchestration and general-purpose computations.
• Specialized Networking Switches: Advanced switches and interconnect solutions ensure low latency and high throughput across the data center, essential for managing the massive data flows typical of AI workloads. DPUs (Data Processing Units) are also gaining traction, offloading network tasks from CPUs to free resources for AI workloads.
• Arm Servers: While still niche, Arm-based servers (e.g., AWS Graviton) are emerging as energy-efficient alternatives, potentially reducing AI data center power costs .

EDGE DC: Pioneering AI-Ready Data Centers in Jakarta

EDGE DC leverages its strategic downtown Jakarta location to provide a robust digital ecosystem, perfectly suited for AI data center needs. Here’s how EDGE DC stands out:

• Strategic Location: Situated in Jakarta’s central business district, EDGE DC offers unmatched connectivity with access to major internet exchanges such as EPIX. This ensures low latency and high-speed data transfers essential for AI operations.
• State-of-the-Art Infrastructure: Facilities like EDGE2 are purpose-built with over 3,400 racks and an IT load capacity of 23 MW, designed to support hyperscale and AI deployments.
• Low Latency and High Reliability: With a standard SLA of 99.999% power availability, EDGE DC guarantees mission-critical uptime for businesses reliant on AI-driven applications.
• Future-Proof Scalability: EDGE DC’s commitment to continuous expansion ensures that as AI demands grow, its facilities will continue to offer the necessary power, connectivity, and cooling solutions.

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The Future of AI Data Centers

The AI revolution is pushing the boundaries of data center design. Innovations such as GPU disaggregation, modular rack designs, and AI-optimized cooling techniques are redefining the digital infrastructure landscape. AI-driven operations, powered by Large Language Models (LLMs) like ChatGPT, are enabling autonomous management of cooling, load balancing, and predictive maintenance. Additionally, hybrid quantum-classical data centers are emerging, requiring cryogenic cooling and quantum-safe encryption for AI research applications .

With AI workloads growing exponentially, facilities like EDGE DC are leading the way in providing scalable and energy-efficient environments essential for future innovations. Regulatory measures, including Indonesia’s Government Regulation No. 33/2023 on Energy Conservation and Southeast Asia’s push for greener data centers, are influencing design considerations, ensuring sustainability while meeting the demands of AI-driven workloads.

AI data centers represent a significant evolution from traditional facilities. With specialized power, cooling, and networking solutions, along with state-of-the-art hardware such as Nvidia’s A100/H100 GPUs and AMD Instinct accelerators, these centers are built to handle the demanding computational needs of modern AI applications. 

Explore EDGE DC to future-proof your digital business and harness the full potential of AI-driven technologies.