HBM2E-16GB
SK Hynix HBM2E 16GB High Bandwidth Memory for AI accelerators and high-performance computing
Product Overview
Description
The HBM2E 16GB is SK Hynix's second-generation High Bandwidth Memory solution delivering up to 1TB/s bandwidth. This 16GB memory stack is designed for AI accelerators, GPUs, and high-performance computing applications requiring massive memory bandwidth.
Using 8-layer 3D stacking technology with TSV (Through-Silicon Via) interconnects, the HBM2E provides 1024-bit wide interface operating at 3.2GT/s per pin. The memory architecture enables AI training accelerators to access large neural network weights without memory bandwidth bottlenecks.
The HBM2E is widely adopted in AI training accelerators from major AI chip companies. The proven technology and broad ecosystem support make it ideal for AI accelerator designs. With 1TB/s bandwidth and 16GB capacity, it provides the memory resources needed for modern AI workloads.
Product Series
HBM
Primary Application
AI training accelerators
Key Features
- 16GB HBM2E memory stack
- Up to 1TB/s bandwidth
- 8-layer 3D stacking with TSV
- 1024-bit wide interface
- 1.2V low power operation
- Widely adopted AI memory
Specifications
| Memory Type | HBM2E |
|---|---|
| Capacity | 16GB |
| Stack Layers | 8 layers |
| Interface Width | 1024-bit |
| Data Rate | 3.2GT/s per pin |
| Bandwidth | Up to 1TB/s |
| Voltage | 1.2V |
| Package | 2.5D SiP |
Applications
AI training accelerators
Electronic system design
GPU graphics memory
Electronic system design
High-performance computing
Electronic system design
Networking processors
Communication and interface
Machine learning systems
Electronic system design
Data center accelerators
Electronic system design
FAE Expert Insights
"The HBM2E 16GB has become the de facto standard for AI training accelerators. In my experience supporting AI chip companies, this memory provides the bandwidth required for training large neural networks. The 1TB/s bandwidth eliminates memory bottlenecks that would otherwise limit AI accelerator utilization. The 16GB capacity accommodates modern AI models with billions of parameters. SK Hynix has proven manufacturing capability ensuring supply for high-volume AI deployments. I recommend HBM2E for AI accelerator designs targeting current-generation AI training applications."
Industry-standard HBM2E providing 1TB/s bandwidth for AI training workloads
— Kevin Liu, BeiLuo
Frequently Asked Questions
What is required to integrate HBM2E into an AI accelerator design?
HBM2E integration requires careful system design: 1) AI accelerator ASIC - must include HBM memory controller and PHY, 2) 2.5D packaging - HBM requires interposer or substrate interposer technology, 3) Signal integrity - high-speed HBM interfaces require careful PCB and interposer design, 4) Thermal management - HBM stacks generate significant heat requiring thermal solution, 5) Co-design - memory and accelerator are tightly coupled requiring joint optimization. The HBM2E interface follows JEDEC standards, but successful integration requires close cooperation between AI chip designer, packaging partner, and memory supplier. SK Hynix provides system co-design support for AI accelerator customers.
HBM integration requires system co-design. Contact SK Hynix early in design for co-design support.
What AI accelerators use HBM2E memory?
HBM2E is used in many AI accelerators: 1) NVIDIA A100 and H100 GPUs - use HBM2E/HBM3 for AI training, 2) AMD CDNA GPUs - employ HBM2E for AI workloads, 3) Google TPU v4 - uses custom HBM2E-like memory, 4) Various AI startup accelerators - includingGraphcore, Cerebras, and SambaNova systems. The AI industry has standardized on HBM for training workloads due to the massive bandwidth requirements. HBM2E provides the right balance of bandwidth, capacity, and manufacturing maturity for current AI accelerator designs.
HBM2E is the standard for AI training accelerators. Consider HBM3 for next-generation designs.
What is the power consumption of HBM2E?
HBM2E power consumption characteristics: 1) Active power - approximately 1.2W per GB under load, 2) Idle power - significantly lower when not actively accessed, 3) Total power - 16GB HBM2E consumes approximately 15-20W in active AI workloads, 4) Power efficiency - much better than GDDR for bandwidth provided, 5) Thermal design - requires heat spreader and adequate cooling. The 1.2V operating voltage provides good power efficiency for the extreme bandwidth. System designers must account for HBM power in overall thermal budget.
Plan for 15-20W power consumption in thermal design. HBM2E provides excellent power efficiency per GB/s.
What is the typical development timeline for HBM integration?
HBM integration typically requires 12-18 months: 1) Architecture phase (3 months) - define memory requirements and system architecture, 2) Co-design phase (6 months) - work with SK Hynix on interface design and interposer planning, 3) Implementation phase (6 months) - ASIC design, packaging design, and layout, 4) Validation phase (3 months) - silicon bring-up, characterization, and system validation. Start engagement with SK Hynix at architecture phase. Packaging partner selection is critical and should happen early.
Plan 12-18 month timeline for HBM integration. Engage SK Hynix early in architecture phase.
How does HBM2E compare to GDDR6 for AI applications?
HBM2E vs GDDR6 comparison for AI: 1) Bandwidth - HBM2E provides 1TB/s vs GDDR6's 64-128GB/s per chip, 2) Power efficiency - HBM2E is 3-4x more power efficient per GB/s, 3) Form factor - HBM2E compact 2.5D vs GDDR6 discrete packages, 4) Capacity - HBM2E 16GB per stack vs GDDR6 16-32GB total with multiple chips, 5) Cost - HBM2E higher cost but justified by performance. For AI training, HBM2E is the clear winner due to bandwidth requirements. GDDR6 may be suitable for inference with lower bandwidth needs.
Use HBM2E for AI training requiring maximum bandwidth. GDDR6 may be suitable for inference applications.