Samsung to Double HBM4 and HBM4E Production by 2027 Amid Surging AI Demand

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Samsung to Double HBM4 and HBM4E Production by 2027 Amid Surging AI Demand

Samsung Electronics plans to more than double its HBM4 and HBM4E high-bandwidth memory output in 2027, driven by surging AI demand. The company will increase outsourced glass-carrier cleaning volume to 50,000 sheets a month in 2027 from 20,000 sheets a month in 2026, with the HBM4 family projected to account for approximately 80% of HBM shipments next year. For broader context, explore our AI News.

Samsung's Production Expansion Targets 2027

Samsung Electronics is preparing to substantially increase its high-bandwidth memory (HBM) production, specifically targeting the HBM4 and HBM4E generations. The company aims to more than double its output of these advanced memory chips by 2027. This strategic move is directly linked to the growing requirements of AI computing, which relies heavily on high-performance memory solutions. For broader context, explore our Top 100 AI Tools.

A key indicator of this expansion is the planned increase in outsourced glass-carrier cleaning volume. Samsung intends to raise this volume to 50,000 sheets per month in 2027, a significant jump from the 20,000 sheets per month recorded in 2026. This represents a 2.5-fold increase in a critical manufacturing step for HBM production.

HBM4 and HBM4E Rollout Timeline

Samsung has already initiated the rollout of its HBM4 technology. Mass-production shipments of HBM4 began in February 2026. These chips utilize 1c-class DRAM and are built on a 4nm base die, indicating advanced manufacturing processes. Following this, 12-layer HBM4E samples were provided to key customers, including Nvidia, in May 2026. This sampling phase is crucial for integrating the new memory into next-generation AI accelerators and other high-performance computing platforms.

The HBM4 family is projected to become the dominant segment of Samsung's HBM shipments. Projections indicate that the HBM4 family will account for approximately 80% of HBM shipments in 2027, a substantial increase from about 40% in 2026. This shift underscores the industry's rapid adoption of newer HBM generations for AI workloads.

Overall HBM Capacity Growth

Beyond the HBM4 family, Samsung's overall HBM production capacity is also set for considerable growth. The company expects its total HBM production to increase by nearly 40% in 2027, reaching approximately 250,000 wafers per month. This is up from roughly 180,000 wafers per month in 2026. This broader expansion reflects a comprehensive strategy to meet the escalating global demand for high-bandwidth memory across various applications, particularly in AI infrastructure.

HBM4E Specifications and Efficiency

Samsung has detailed specific performance metrics for its HBM4E memory. The HBM4E specifications include a pin speed of ">13 Gbps per pin" and a total bandwidth of "3.25 TB/s with 2,048 pins." These figures highlight the significant performance improvements over previous generations. Notably, HBM4E is designed to offer roughly double the energy efficiency compared to HBM3E, a critical factor for reducing power consumption in large-scale AI data centers and high-performance computing environments.

Implications for the AI Industry

Samsung's aggressive expansion in HBM4 and HBM4E production has direct implications for the artificial intelligence industry. Increased availability of these high-performance memory chips can help alleviate potential supply bottlenecks for GPU manufacturers and AI hardware developers. The enhanced bandwidth and energy efficiency of HBM4E are particularly beneficial for training and deploying large language models (LLMs) and other complex AI systems, which require immense data throughput and efficient power management. This move positions Samsung to be a key enabler of future AI advancements.

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About the Author

Albert Schaper avatar

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Albert Schaper

Albert Schaper is a co-founder of Best-AI.org. He focuses on product strategy, AI adoption, practical tool selection, and educational content that helps users compare AI products with clearer context.

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