As the global race for leadership in artificial intelligence (AI) accelerates, countries face demographic challenges such as aging populations and shrinking workforces. The emergence of agentic AI, which not only responds to queries but also reasons, plans, and acts autonomously, could be pivotal in reshaping productivity and addressing these challenges. Unlike traditional AI, which merely answers questions, agentic AI systems can undertake a series of tasks without constant human input, from booking flights to adapting itineraries based on real-time data like weather or delays.
This evolution signals a significant shift toward proactive AI, necessitating a robust computational infrastructure capable of supporting complex workflows that require extended reasoning and planning. As these systems mature and their adoption grows, countries like the Philippines must assess whether their AI infrastructure can handle the anticipated increase in virtual users and the intricate demands of this technology.
While discussions around AI often center on high-performance graphics processing units (GPUs), central processing units (CPUs) are equally vital. CPUs manage essential tasks behind the scenes, including data movement and memory management, and are crucial for running various AI workloads efficiently. For instance, advanced language models, image recognition systems, and fraud detection can operate effectively on CPU-only servers powered by modern processors such as the AMD EPYC 9005 Series.
As AI models transition to modular architectures like mixture-of-experts systems, the orchestration of resources becomes increasingly important. High instruction per clock and fast input/output capabilities are essential for CPUs to manage multiple concurrent tasks with precision. In this context, connectivity serves as the “glue” that integrates AI systems, with advanced networking components facilitating efficient and secure data routing, thereby minimizing latency and enhancing performance.
In the era of agentic AI, a heterogeneous system design that includes CPUs, GPUs, networking, and memory is necessary. Such integrated systems can deliver the required speed and throughput for real-time interactions among billions of intelligent agents. As adoption scales, optimizing the entire rack-level architecture—where computing, storage, and networking are co-designed—will become crucial for maximizing performance and efficiency.
As AI systems grow in complexity, the necessity for openness in software and hardware becomes a strategic imperative. Closed ecosystems can lead to vendor lock-in and limit innovation. Open software stacks, such as AMD ROCm, are essential for developers, enabling the optimization and deployment of AI models across various environments and supporting popular frameworks like PyTorch and TensorFlow. For a country like the Philippines, fostering an open AI software ecosystem can enhance accessibility and lower barriers to entry, promoting innovation across academia, startups, and industry.
Moreover, openness at the hardware and systems level is crucial as AI compute moves toward large-scale, heterogeneous deployments. Open standards from initiatives like the Open Compute Project support modular system design, while collaborations such as the Ultra Accelerator Link aim to establish high-bandwidth connections between AI accelerators. This evolution allows cloud and data center operators to construct flexible, interoperable infrastructures that align with AI’s rapid growth. By embracing an open ecosystem, the Philippines can leverage global innovation while simultaneously cultivating local differentiation.
As the landscape of multi-agent AI develops, openness will be essential for scaling, sovereignty, and maintaining leadership in the field. Looking ahead, the focus must extend beyond GPUs to include CPUs, advanced interconnects, and smart networking—all critical for enabling complex, real-time decision-making at scale. Open software like ROCm, industry standards for rack-scale designs, and collaborative initiatives will facilitate greater flexibility and faster innovation across the AI spectrum.
In line with these developments, AMD is advancing its vision with “Helios,” a next-generation rack-scale reference design for AI infrastructure scheduled for release in 2026. This design aims to unify high-performance computing, open software, and scalable architecture, tailored to the requirements of agentic AI.
For the Philippines, establishing an open, heterogeneous, and scalable AI infrastructure is not merely a technological choice but a strategic foundation for national competitiveness. As the country navigates the growing demands of automation and regional AI ambitions, building next-generation AI infrastructure will be vital for unlocking sustainable growth, enhancing innovation, and fostering resilience.
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