South Korea AI Memory Chip Market Size and Forecast by Memory Types, Packaging Architectures, and End User: 2019-2033

  Dec 2025   | Format: PDF DataSheet |   Pages: 110+ | Type: Niche Industry Report |    Authors: Surender Khera (Asst. Manager)  

 

South Korea AI Memory Chip Market Outlook

  • In 2024, market figures stood at USD 1.24 billion in South Korea.
  • As per our research, the South Korea AI Memory Chip Market to reach USD 16.42 billion by 2033, with a forecasted CAGR of 31.6% across the projection period.
  • DataCube Research Report (Dec 2025): This analysis uses 2024 as the actual year, 2025 as the estimated year, and calculates CAGR for the 2025-2033 period.

Industry Assessment Overview

Industry Findings: National industrial support reshaped demand for AI-optimised memory by reinforcing domestic capacity planning and advanced packaging capabilities. A high-visibility government package rolled out in May-2024 to bolster the semiconductor ecosystem and fund ecosystem-scale projects, which encouraged local integrators to prioritise memory modules that can be sourced and serviced domestically. Procurement teams consequently modelled multi-year supply paths that mix specialised high-bandwidth memory with validated commodity DRAM to balance performance and local-content requirements. The policy-driven capital flows improved medium-term supply visibility and incentivised integrators to shorten memory qualification cycles for mission-critical AI deployments.

Industry Player Insights: South Korea’s market transformation is influenced by Samsung Electronics, SK hynix, Micron Technology, and Kioxia etc. SK hynix began mass production of its 12-layer HBM3E product in Sep-2024 to meet escalating AI training bandwidth needs, which materially improved availability for local hyperscale and research clusters. Separately, Samsung began customer sampling of its 36GB HBM3E 12H devices in Feb-2024, enabling system designers to test next-generation high-bandwidth configurations. These vendor steps tightened local performance ceilings and gave Korean integrators more confidence when specifying memory-dense topologies for large-scale AI workloads.

*Research Methodology: This report is based on DataCube’s proprietary 3-stage forecasting model, combining primary research, secondary data triangulation, and expert validation. [Learn more]

Market Scope Framework

Memory Types

  • Compute-in-Memory (CiM)
  • Near-Memory / On-Package DRAM
  • In-Memory Processing SRAM Blocks

Packaging Architectures

  • 2.5D Co-Packaged AI Memory
  • 3D-Stacked AI Memory
  • AI-Focused Fan-Out Memory Tiles

End User

  • Hyperscalers & Cloud Providers
  • OEMs / System Integrators
  • Accelerator / ASIC Vendors
  • Enterprises / Research Institutions
  • Edge Device Makers
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