NAND 플래시와 Pascari aiDAPTIV™로 지속 가능한 AI 인프라 구축

Find out how memory optimization and infrastructure design can expand AI access while improving efficiency and sustainability.

Introduced in 2015, the United Nations’ Sustainable Development Goals (SDGs) provide a global framework for addressing some of the world’s most important challenges, including quality education, affordable energy, climate action, innovation, and reduced inequality. 

As AI rapidly transforms the world, these 17 far-reaching objectives raise an important question: How can AI become a shared resource for all nations, rather than a capability available only to wealthy countries and large enterprises? Advancing sustainable AI infrastructure will play a critical role in answering that question. 

The infrastructure barrier to AI access

Modern AI infrastructure often requires significant investment in GPUs, memory, power, and cooling. These requirements create real barriers for organizations working to deploy AI systems while balancing cost, performance, and infrastructure requirements:

• • • Universities and research institutions
    • Startups and smaller organizations
    • Public sector teams
    • Developing regions

As a result, AI capability can become concentrated among a relatively small number of organizations and countries, limiting broader progress in AI infrastructure sustainability. 

Expanding access through smarter infrastructure

As a global leader in NAND flash solutions, Phison has spent decades advancing storage innovation. In the era of generative AI, we are extending the role of NAND flash beyond traditional storage with Pascari aiDAPTIV™, a solution designed to expand the usable memory capacity of AI systems. 

aiDAPTIV improves AI efficiency through a multi-tiered memory approach that coordinates GPU memory (VRAM), system memory (DRAM), and NAND flash. Instead of relying solely on limited and expensive VRAM, this architecture intelligently moves and stages data across these tiers, enabling more effective AI memory optimization. 

This approach expands usable AI memory capacity, helping organizations run larger AI workloads without scaling GPU infrastructure solely to add more memory capacity. By making better use of available resources, aiDAPTIV improves overall infrastructure efficiency and can help reduce the number of GPUs, power delivery systems, cooling infrastructure, and facility resources required to deploy AI at scale.

The result is a more balanced and efficient system design that supports energy efficient AI systems while lowering the barrier to entry for AI adoption. It also means that more institutions and organizations can begin adopting AI for education, research, and productivity. 

This directly supports SDG 4: Quality Education by helping broaden access to AI learning tools and research capabilities. 

Supporting innovation and reducing inequality

As AI becomes a core driver of economic growth and competitiveness, unequal access to AI infrastructure risks widening the global digital divide. 

By making local AI deployment more practical, aiDAPTIV can help expand participation in AI development beyond traditional technology hubs while reinforcing AI infrastructure sustainability at a global level. 

This contributes to both SDG 9: Industry, Innovation, and Infrastructure 그리고 SDG 10: Reduced Inequalities.  

Enabling more organizations to cost-effectively build and deploy AI locally supports a more distributed and inclusive innovation landscape. 

Improving efficiency and energy use

AI infrastructure is also associated with growing energy consumption. High-performance systems often require substantial electricity, cooling, and ongoing operating cost, making it increasingly important to design energy-efficient AI systems from the ground up. 

Modern AI accelerators and GPUs are becoming increasingly power efficient at the chip level thanks to advances in semiconductor design and manufacturing. However, deploying large-scale AI infrastructure often requires substantial supporting infrastructure, including power delivery, cooling systems, and data center expansion. aiDAPTIV helps organizations run larger AI workloads within existing infrastructure environments, helping reduce infrastructure expansion requirements and enabling broader AI deployment without requiring large-scale new AI infrastructure investments.

Improving memory efficiency is one of the most direct ways to influence overall system performance and energy use. When AI workloads are constrained by limited memory, organizations often compensate by overprovisioning hardware, particularly GPUs. That approach increases power consumption, cooling demand, and total cost. 

By enabling more effective AI memory optimization through its multi-tiered architecture, aiDAPTIV reduces the need for overprovisioned hardware. This has a measurable impact on system design by improving utilization of existing resources and supporting more efficient scaling. 

In practical terms, this means organizations can reduce AI energy consumption by running larger workloads on right-sized infrastructure rather than defaulting to larger, more power-hungry systems. It also contributes to better overall AI infrastructure efficiency, particularly in environments where power and cooling are constrained. 

These capabilities align with SDG 7: Affordable and Clean Energy 그리고 SDG 13: Climate Action.  

AI advancement should be paired with responsible infrastructure decisions. Improving how systems use memory and compute resources is a meaningful step toward long-term AI infrastructure sustainability while balancing performance, cost, and environmental impact. 

How infrastructure design impacts sustainability goals

The connection between AI infrastructure and the United Nations’ SDGs is not just conceptual. It is operational. 

The way AI systems are designed, deployed, and scaled directly influences outcomes related to energy use, accessibility, and global participation in innovation. Infrastructure decisions shape whether AI remains concentrated in a few well-resourced environments or becomes more broadly available across industries and regions. 

Technologies that improve AI memory utilization and overall infrastructure efficiency play a key role in this shift. By reducing reliance on large scale infrastructure expansion and enabling more efficient use of existing resources, organizations can align performance goals with AI sustainability objectives. 

This is where infrastructure design becomes a lever for impact. When systems are built to be more efficient, scalable, and accessible, they contribute not only to technical performance, but also to broader objectives such as reducing inequality, expanding access to education, and supporting more responsible energy use. 

In this way, advancing sustainable AI infrastructure becomes a practical path toward achieving global sustainability goals, not just an abstract ideal. 

From storage leader to sustainable AI enabler

In addition to being a leader in NAND flash technology, Phison is helping redefine how that technology contributes to the future of sustainable AI infrastructure. 

aiDAPTIV represents more than a simple performance improvement. It provides a practical and scalable pathway toward: 

• • • Lower-cost AI deployment
    • More efficient AI systems
    • Wider global access to AI technology

Closing the gap

The future of AI will be defined not only by model capability, but by how efficiently and broadly that capability can be deployed. Infrastructure that expands AI access while reducing infrastructure expansion requirements will play a central role. aiDAPTIV reflects this shift by enabling more capable AI systems within existing infrastructure environments, supporting both innovation and sustainability at scale. 

That is how Phison turns innovation into impact, and technology into sustainability. 

어떻게 살펴보는지 알아보세요 aiDAPTIV enables more efficient and accessible AI infrastructure. Or, 문의하기 to learn how to deploy sustainable AI solutions at scale. 

자주 묻는 질문(FAQ) :