HBM2E-8GB
SK Hynix HBM2E 8GB memory stack with 460GB/s bandwidth for AI and HPC applications
Product Overview
Description
The HBM2E-8GB is an 8GB High Bandwidth Memory stack featuring SK Hynix's advanced HBM2E technology. With 460GB/s bandwidth, it provides high-performance memory for AI accelerators, HPC systems, and graphics processors.
This HBM2E stack uses 4-layer DRAM die stacking with through-silicon vias (TSV) to achieve high density and bandwidth in a compact footprint. The 8GB capacity is suitable for mid-range AI accelerators and HPC applications.
With 2.0GT/s data rate per pin and 1024-bit interface, the HBM2E-8GB delivers excellent memory bandwidth efficiency. It is ideal for applications requiring high bandwidth but not requiring the maximum capacity of 16GB stacks.
Product Series
HBM
Primary Application
Mid-range AI accelerators
Key Features
- 8GB capacity with 4-layer stacking
- 460GB/s memory bandwidth
- 1024-bit wide interface
- 2.0GT/s data rate per pin
- 1.2V low voltage operation
- TSV technology for high density
- Compact footprint for space-constrained designs
Specifications
| Memory Type | HBM2E |
|---|---|
| Capacity | 8GB |
| Stack Layers | 4-Hi |
| Interface Width | 1024-bit |
| Data Rate | 2.0GT/s per pin |
| Bandwidth | 460GB/s |
| Voltage | 1.2V |
| Operating Temperature | 0C to +95C (Tcase) |
Applications
Mid-range AI accelerators
Electronic system design
HPC systems
Electronic system design
Graphics processors
Electronic system design
Network processors
Communication and interface
FPGA-based acceleration
Electronic system design
Edge AI devices
Electronic system design
FAE Expert Insights
"The HBM2E-8GB is an excellent choice for AI accelerators that don't need the full 16GB capacity. The 460GB/s bandwidth is still exceptional and handles most AI inference workloads with ease. I've used this in edge AI devices where the smaller footprint and lower power compared to 16GB stacks are advantageous. The cost savings compared to 16GB HBM2E can be significant for high-volume production. For training smaller models or inference at the edge, the 8GB capacity is often sufficient. The performance per watt is excellent."
Cost-effective HBM2E solution for mid-range AI and HPC
— HBM Applications FAE, BeiLuo
Frequently Asked Questions
When should I choose 8GB vs 16GB HBM2E?
Choose HBM2E-8GB when: 1) Cost sensitivity - lower cost per stack, 2) Power constraints - lower power consumption, 3) Smaller models - AI models fitting in 8GB, 4) Edge deployment - space and power limited, 5) Inference workloads - typically need less memory than training. Choose HBM2E-16GB when: 1) Large models - training LLMs and large CNNs, 2) Maximum bandwidth - 1TB/s vs 460GB/s, 3) Future-proofing - headroom for model growth.