Transforming Data Center Infrastructure for AI
In just 75 days, BDx Indonesia completed the first phase of its NVIDIA-powered GPU as a Service (GPUaaS) infrastructure — marking a major milestone in Indonesia’s journey toward AI-ready data centers.
This achievement reflects how the rapid rise of Artificial Intelligence (AI) is reshaping the data center industry, demanding faster, smarter, and more sustainable infrastructure development.
In Podcast Nusantara Episode 8A, industry leaders Andry Gunawan (Country Manager, NVIDIA Indonesia) and Agus Hartono Wijaya (CEO, BDx Indonesia) share insights on how strategic collaboration is building a stronger foundation for Indonesia’s AI ecosystem.
The discussion is hosted by Dr. Dini Fronitasari, CEO of PT. Goritma Teknologi Indonesia and an AI Researcher at BRIN (National Research and Innovation Agency).
Andry highlights that the exponential growth of AI has dramatically increased the demand for GPU-based computing, while Agus emphasizes the importance of infrastructure readiness — covering power, cooling, and energy efficiency — to ensure that AI deployment is both scalable and sustainable.
GPU as a Service: Meeting the Demands of Modern Computing
The GPU as a Service (GPUaaS) model allows organizations to access cloud-based GPU resources without major capital investments in hardware.
This model offers scalability for machine learning training, large-scale data analytics, and generative AI development — enabling enterprises to adjust computing power as needed while controlling costs.
Beyond cost efficiency, GPUaaS reduces deployment time and infrastructure downtime, making it the ideal solution for companies aiming to stay agile in the fast-evolving AI landscape.
Power and Cooling Challenges in the GPU Era
The rising GPU density per rack presents new challenges in power distribution and thermal management.
As discussed in the podcast, liquid cooling systems are now the preferred solution for achieving superior thermal efficiency while supporting data center sustainability goals in Indonesia.
Source:
NVIDIA Data Center Design Guide for DGX Systems (2023);
ASHRAE Thermal Guidelines for Data Processing Environments (5th Edition, 2021).
Choosing the Right AI Use Cases
Both speakers stress that AI adoption should not be driven by hype.
Businesses need to ensure their AI use cases provide real business value rather than serving as experimental projects.
Examples of high-impact AI implementations in Indonesia include:
- Predictive maintenance in manufacturing and industrial sectors
- Customer analytics in financial services
- Natural language processing (NLP) for public service chatbots
The Future of AI and National Infrastructure
The podcast raises a key question: Who should lead Indonesia’s AI infrastructure development — government, private sector, or both?
Both Andry and Agus agree that cross-sector collaboration is vital.
The government must establish supportive policies and regulations, while the private sector, represented by BDx and NVIDIA, provides technology, innovation, and investment.
With ambitious milestones — such as the completion of a GPU-ready data center in just 75 days — Indonesia is positioning itself as a regional AI innovation hub.
Conclusion
Indonesia’s journey toward a sustainable AI and data center ecosystem has only just begun. The collaboration between BDx and NVIDIA proves that with the right strategy, efficiency, and sustainability focus, GPU as a Service is more than a trend — it is the foundation of Indonesia’s digital economy of the future.
For more details, listen directly to the podcast on YouTube Nusantara Academy and don’t forget to register for training by contacting https://wa.me/6285176950083
