SK Hynix HBM3E 12-Hi 36GB Memory Stack

The SK Hynix HBM3E 12-Hi 36GB Memory Stack represents a landmark advancement in high-bandwidth memory architecture, becoming the world's first 12-layer HBM3E device to enter mass production as of Q1 2025. By stacking twelve DRAM dies using Through-Silicon Via (TSV) interconnect technology, SK Hynix achieves a 36GB per-stack capacity that directly addresses the exponentially growing memory footprint demands of large language models, generative AI inference engines, and scientific simulation workloads. The 12-Hi configuration surpasses the previous 8-Hi standard, delivering a 50% increase in per-stack capacity without sacrificing the ultra-wide 1024-bit memory bus that defines the HBM standard.

Operating within the HBM3E specification, each stack delivers a peak bandwidth of 1.28 TB/s, enabled by an internal data rate of 9.6 Gbps per pin across the 1024-bit interface. Advanced MR-MUF (Mass Reflow Molded Underfill) packaging technology ensures structural integrity across all twelve die layers while maintaining optimal thermal dissipation characteristics critical for sustained operation inside thermally dense AI accelerator packages. The memory controller interface is fully compliant with JEDEC HBM3E standards, ensuring interoperability with next-generation GPU and AI ASIC silicon from leading semiconductor vendors.

From a system integration perspective, the HBM3E 12-Hi stack is designed for 2.5D integration via silicon interposer alongside compute dies in advanced multi-chip module assemblies. The elevated per-stack capacity means fewer stacks are required to achieve a given total memory configuration, which translates to improved power efficiency per gigabyte and reduced interposer real estate. These characteristics make it an ideal memory solution for training clusters running foundation models with hundreds of billions of parameters, as well as high-throughput inference platforms where memory capacity and bandwidth are co-critical constraints.

Ideal for

• Large language model (LLM) training on clusters requiring massive per-accelerator memory capacity to hold trillion-parameter model shards without off-chip offloading
• Generative AI inference serving where high memory bandwidth enables concurrent multi-user request batching with low latency token generation
• High-performance computing (HPC) simulations in computational fluid dynamics, molecular dynamics, and climate modeling that require fast access to very large working datasets
• Graph neural network training on billion-node graphs where random-access memory bandwidth is the primary throughput bottleneck
• Next-generation GPU integration for scientific computing accelerators used in national laboratories and hyperscale research facilities
• AI supercomputer node construction where maximizing memory capacity per node reduces inter-node communication overhead during distributed training runs

Technical specifications

Available from Omnixon Global. Submit an RFQ and our team will confirm configuration and availability for your order.