The Rising Dominance of Memory in AI Data Center Economics

A significant shift is underway in the cost structure of hyperscale data centers powering the AI revolution. According to a recent analysis from research firm SemiAnalysis, spending on memory—encompassing DRAM, NAND, and High Bandwidth Memory (HBM)—is claiming an increasingly large share of total capital expenditure, a trend that is accelerating faster than many anticipated.

From Skepticism to Sobering Reality

When SemiAnalysis first suggested earlier this year that memory could account for up to 30% of data center capex, the initial reaction from the market was one of disbelief. Industry stakeholders pointed to the traditional server bill of materials, where memory typically represents a mid-teens percentage, questioning how it could possibly consume such a large portion of the overall budget.

Subsequent market developments have forced a reevaluation. The pace and magnitude of memory price increases, particularly for high-performance modules tailored for AI workloads, have surpassed expectations. Micron Technology's latest earnings report added fuel to this discussion, heightening concerns about memory's growing cost burden in the coming year.

The Drivers Behind the Numbers

In a May update, SemiAnalysis clarified the rationale behind its forecast. The key factors include:

  • The AI System Paradigm: Mainstream AI training systems, built around NVIDIA GPUs, have an insatiable appetite for memory bandwidth and capacity. Premium memory like HBM has become a critical performance bottleneck.
  • Evolving Cost Architecture: The traditional server BOM model is obsolete for AI data centers. The computing unit (GPU) and its accompanying memory system now form the core of the cost structure, with memory's per-unit cost rising rapidly.
  • Compounding Pressures: Broad-based price increases for DRAM and NAND are converging with the specialized high cost of HBM, collectively inflating total memory spending.

Projecting forward, the firm now states that memory-related spending in leading NVIDIA-based AI system architectures will exceed 30% of capex by the end of 2026. More strikingly, this share is forecast to surpass the 40% threshold in 2027.

Implications for the Industry

If these projections hold, the economics of building and operating AI data centers will undergo a fundamental change. Memory will transition from a supporting component to a strategic resource on par with—or even more costly than—compute chips. Data center operators, cloud providers, and AI companies must rethink their budgeting and supply chain strategies to manage this structural cost escalation.

SemiAnalysis anticipates that the market will gain a fuller understanding of this trend in the coming months as more financial data and procurement patterns emerge. For investors and industry leaders, recognizing and preparing for the memory cost surge is becoming an immediate priority.