Nvidia shares edged slightly lower in early trading as investors reacted to fresh research highlighting growing pressure in the global low-power DRAM (LPDDR) market. While the move in the stock was modest, analysts are increasingly pointing to a deeper structural shift in memory demand driven by artificial intelligence infrastructure.
According to research cited by Hana Securities and Citrini Research, Nvidia is on track to become the largest consumer of LPDDR memory by 2027, surpassing both Apple and Samsung Electronics combined. The projection has raised concerns that surging AI server demand could tighten supply chains and increase costs across the broader semiconductor ecosystem.
LPDDR memory, traditionally used in smartphones and laptops, is now seeing a rapid expansion into data center workloads. The shift is being driven by next-generation AI systems that require significantly higher memory bandwidth and efficiency.
NVIDIA Corporation, NVDA
Nvidia’s upcoming AI platforms, including systems in the Vera Rubin architecture, are expected to consume substantially more LPDDR than previous generations. Analysts note that this change marks a structural break from the historical use case of mobile-focused memory consumption toward high-performance AI computing clusters.
As a result, LPDDR demand is no longer tied primarily to consumer electronics cycles. Instead, AI infrastructure spending is emerging as a dominant force shaping global supply allocation.
Research estimates suggest Nvidia’s LPDDR consumption will nearly double within a two-year span. The company’s usage is projected to rise from approximately 3.144 billion gigabytes in 2026 to 6.041 billion gigabytes in 2027.
For comparison, Apple is expected to account for around 2.966 billion gigabytes, while Samsung Electronics is forecast at 2.724 billion gigabytes during the same period. If these estimates hold, Nvidia alone could represent roughly 36% of total global LPDDR supply.
Such concentration among a single AI-focused company underscores how rapidly artificial intelligence workloads are reshaping semiconductor demand patterns. Analysts argue that this level of dependency could introduce new volatility into memory pricing cycles.
Beyond demand growth, structural constraints within the semiconductor industry are adding further pressure. Memory manufacturers have increasingly shifted production capacity toward high-bandwidth memory (HBM), a more profitable segment used in AI accelerators.
This reallocation has left less capacity available for conventional DRAM and LPDDR production. At the same time, suppliers are gradually moving away from long-term fixed pricing contracts, instead adopting shorter contract cycles and post-settlement pricing models that adjust based on market conditions.
These changes give memory producers greater pricing power but reduce cost predictability for device makers and hyperscale data center operators. In a tightening market, buyers with larger scale, such as Nvidia, may still secure allocation, while smaller manufacturers could face supply constraints.
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