Invalid input. Special characters are not supported.
We are living through a generational shift in computing. AI is no longer a niche workload — it is the defining force shaping infrastructure strategy, silicon roadmaps and business directives. The scale is staggering: Hyperscalers are deploying tens of thousands of AI accelerators per cluster, training trillion-parameter models and consuming megawatts of power per deployment zone.
The industry has rightly celebrated the power of AI — its ability to transform industries, accelerate discovery and augment human capability. But we must now confront a more sobering reality: The power to do AI — the energy required to run these workloads — is becoming one of the most critical constraints on innovation.
The prevailing response has been predictable. Optimize the compute. Cool the racks. Buy more green power. These are necessary steps, but they are no longer sufficient. The assumption that compute is the primary lever for energy efficiency is increasingly outdated. In fact, it may be obscuring one of the most powerful — and underutilized — opportunities for impact.
It’s time to talk about memory.
The hidden energy sink
In AI infrastructure, memory and storage are often treated as supporting actors — essential but not strategic. Yet in modern AI clusters, memory subsystems, including high-bandwidth memory (HBM), DRAM, SSDs and the interconnects that bind them, can account for up to 50% of total system power, depending on the specific configuration and workload. As model sizes grow and data movement intensifies, this share of total system power and the importance of power-efficient memory and storage only increases.
Optimizing compute to support AI has led to the adoption of different compute paradigms, such as edge and distributed architectures. Data has a gravity that pulls processing power toward it, and the sheer volume of data generated daily is staggering. Estimates suggest that in 2025, the world will generate more than 402 exabytes of data each day. In a very real way, AI is moving to where the data is, and data lives in memory and storage. These paradigms have increased the memory footprint, presenting additional power optimization opportunities.
The energy cost of moving data — from memory to accelerator, from SSD to DRAM, across racks and fabrics — is now a dominant factor in total power consumption. According to an independent study from Semianalysis, memory-bound operations — such as checkpointing and collective communication — are now among the largest contributors to power spikes in hyperscale AI clusters. These events can cause instantaneous fluctuations of tens of megawatts, underscoring memory’s growing role in both energy consumption and grid stability. And unlike compute, which benefits from aggressive node scaling and architectural innovation, memory systems have historically evolved more incrementally.
This is the blind spot. And it’s where we must look next to address efficiency issues.
A new playbook: Memory-led efficiency
At Micron, we believe the future of sustainable AI infrastructure will be led by memory. This approach means rethinking the architecture from the memory up — not as an afterthought, but as a strategic foundation for performance and efficiency.
We’re already seeing this shift take shape:
LPDDR and HBM: Our latest memory technologies deliver industry-leading performance per watt, reducing energy draw without compromising bandwidth. This result is not only due to the efficiencies gained by using the most advanced process node but also to our relentless focus on optimizing architectures within each design for power efficiency.
SSD-based memory tiering: By extending memory hierarchies with high-performance SSDs, we can reduce DRAM footprint and idle power. And by optimizing our portfolio of SSDs with our industry-leading 9th-generation NAND and meeting the specific needs of each memory and storage tier, we see power efficiency gains each time data must be stored and moved.
Data movement minimization: Architecting systems to keep data closer to compute — and reducing unnecessary transfers — can yield significant energy savings.
Telemetry and dynamic tuning: Real-time power profiling of memory subsystems enables intelligent throttling and workload-aware optimization.
These are not theoretical ideas. They are being deployed today in some of the world’s most advanced AI clusters — and the results are compelling.
The strategic imperative
For infrastructure leaders, this shift is more than a technical curiosity — it’s a strategic imperative. Power is now a gating factor for scale. Total cost of ownership (TCO) is ballooning. Sustainability is a board-level mandate. And the pace of AI demand is outstripping the ability of traditional infrastructure to keep up. The workload output of hyperscale AI data centers is no longer limited by the compute hardware that they contain, but by the amount of energy they can procure from the grid.
Memory-led efficiency offers a new lever — one that is available now and that scales with the problem. It enables hyperscalers to deploy more capacity within the same power envelope. It reduces cooling and provisioning costs. And it positions infrastructure teams to meet the demands of next-generation AI workloads without compromising on sustainability or economics.
A look ahead
As we look to the future, the question is no longer whether AI will transform the world — which is a given — it’s how we will power that transformation. The answer lies not just in faster chips or colder data centers; it also lies in smarter architectures that elevate and optimize memory and storage to central roles in the efficiency equation.
At Micron, we’re proud to be leading this shift — by investing in the technologies, partnerships and systems required to make memory a driver of sustainable AI at scale. The power of AI is undeniable. But the power to do AI — efficiently, sustainably and at planetary scale — will define the next era of innovation.
Let’s build it together.