SK Hynix HBM3E 36GB Stack (Mach-1)

The SK Hynix HBM3E 36GB Mach-1 stack represents the fourth generation of High Bandwidth Memory technology, built on SK Hynix's advanced 10nm-class DRAM process and stacked using Through-Silicon Via (TSV) interconnect technology across 12 active DRAM dies plus a base logic die. Each stack provides 36GB of capacity, achieved through a 12-high die configuration using 3GB per die, making it the highest-capacity single HBM3E stack commercially available at launch in Q1 2025. The architecture implements a 1024-bit wide memory interface operating at up to 9.2 Gbps per pin, delivering a peak aggregate bandwidth of 1.18 TB/s per stack — a roughly 50% improvement over HBM3 baseline specifications and critical for feeding the ever-expanding compute appetites of modern AI training and inference accelerators.

The Mach-1 designation reflects SK Hynix's internal product naming for its first-generation HBM3E offering, engineered specifically to integrate with next-generation GPU and AI ASIC packages from major silicon vendors deploying 2.5D advanced packaging via silicon interposers or silicon bridges. The memory stack is designed to operate within tight thermal envelopes common in high-density accelerator modules, featuring refined power delivery and thermal management characteristics compared to prior HBM generations. Error Correction Code (ECC) functionality is embedded throughout the die stack, ensuring data integrity for mission-critical AI model training and scientific workloads where bit errors could corrupt multi-day training runs.

From a system-level perspective, the HBM3E 36GB Mach-1 stack is purpose-built for deployment in data center AI infrastructure at scale — including large language model (LLM) training clusters, high-performance inference engines, and scientific simulation platforms. Its combination of extreme bandwidth, high per-stack capacity, and compatibility with leading 2.5D packaging ecosystems positions it as a foundational memory component for accelerators targeting the most demanding frontier AI workloads, including generative AI, graph neural networks, and high-fidelity physics simulation.

Ideal for

• Large language model (LLM) training on GPU clusters where model weight tensors and gradient buffers demand sustained multi-terabyte-per-second memory bandwidth to prevent compute stalls
• High-throughput AI inference serving for transformer-based models where low-latency token generation requires rapid KV-cache access across tens of gigabytes of on-package memory
• High-performance computing (HPC) workloads such as molecular dynamics simulations and climate modeling that are memory-bandwidth-bound and benefit directly from sub-microsecond latency HBM access
• Graph neural network (GNN) training on large sparse graphs where irregular memory access patterns demand the high bandwidth and low latency that HBM3E provides over conventional GDDR or LPDDR alternatives
• AI-accelerated genomics and drug discovery pipelines requiring simultaneous processing of large biological datasets with deterministic memory performance and hardware ECC protection
• Next-generation AI ASIC integration in hyperscale data center pods where per-accelerator memory capacity must reach 36GB or beyond per stack to accommodate growing model parameter counts without CPU offloading

Technical specifications

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