H5AG36EXNDX017N: Technical Guide to SK Hynix 16GB HBM2E Memory Stack

Introduction

The H5AG36EXNDX017N is a 16GB High Bandwidth Memory 2E (HBM2E) stack manufactured by SK Hynix, featuring vertically-stacked DRAM dies using Through-Silicon Via (TSV) technology with 1024-bit wide interface delivering up to 460 GB/s bandwidth per stack for AI accelerators, high-end GPUs, and HPC applications requiring extreme memory performance.

This advanced memory solution provides double the capacity of standard 8GB HBM2E stacks through 12-high (12H) die stacking, enabling high-performance computing systems to achieve both massive bandwidth and large memory capacity in single-chip packages optimized for data-intensive AI training, scientific computing, and graphics workloads.

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Technical Overview

Core Specifications

Parameter	Specification
Capacity	16GB per stack
Stack Height	12-High (12H)
Interface Width	1024-bit
Data Rate	3.6 Gbps per pin
Bandwidth	460 GB/s per stack
Voltage	1.2V core
Memory Type	HBM2E
Package	Interposer-based

Key Features

Double Capacity:

• 16GB vs 8GB standard HBM2E
• 12 DRAM dies stacked (12H)
• Enables larger memory footprints
• Critical for AI model training

Ultra-High Bandwidth:

• 460 GB/s per stack
• 1024-bit parallel interface
• 3.6 Gbps per pin signaling
• Aggregate 1.84-3.68 TB/s (4-8 stacks)

Advanced Technology:

• TSV (Through-Silicon Via) 3D stacking
• Low power consumption
• Integrated ECC support
• Excellent thermal characteristics

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Complete Specifications

Memory Organization

Parameter	Value
Total Capacity	16GB (128 Gb)
Stack Configuration	12-High (12 dies)
Capacity per Die	1.33GB (10.67 Gb)
Channels	8 independent channels
Banks per Channel	16
Prefetch	2n (DDR)

Performance Specifications

Parameter	Typical	Unit
Bandwidth per Stack	460	GB/s
Data Rate	3.6	Gbps
Access Latency	100-120	ns
Power per Stack	12-15	W
Efficiency	~31	GB/s/W

Comparison: 16GB vs 8GB HBM2E

Feature	H5AG36EXNDX017N (16GB)	Standard 8GB HBM2E
Capacity	16GB	8GB
Stack Height	12-High (12H)	8-High (8H)
Bandwidth	460 GB/s	460 GB/s
Power	~15W	~12W
Use Case	Large AI models	Standard GPU/AI

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Applications

AI Training - Large Language Models

Massive Parameter Models:

• GPT-3/4 scale models (175B+ parameters)
• LLaMA, PaLM, Claude training
• Stable Diffusion, DALL-E training

Memory Requirements:

Model: GPT-3 (175B parameters)
Weights: 175B × 2 bytes (FP16) = 350GB
Gradients: 350GB
Optimizer states: 700GB (Adam)
Total per node: >1TB

Solution: 8× H5AG36EXNDX017N stacks
Capacity: 8 × 16GB = 128GB per GPU
Multi-GPU: 8 GPUs = 1TB total

HPC - Scientific Computing

Large-Scale Simulations:

• Computational Fluid Dynamics (CFD)
• Molecular Dynamics
• Weather modeling
• Quantum chemistry

Advantages:

• 16GB capacity holds larger datasets in-memory
• 460 GB/s bandwidth sustains computation
• Reduces memory-bound bottlenecks

High-End Graphics

Professional GPUs:

• 8K video editing and rendering
• Real-time ray tracing
• Large 3D scene rendering
• CAD/CAM workstations

Capacity Benefits:

• 16GB per stack × 4 stacks = 64GB
• Holds massive texture datasets
• Enables complex scene graphs

AI Inference - Edge Servers

High-Throughput Inference:

• GPT-4 serving (ChatGPT-scale)
• Computer vision at scale
• Recommendation systems
• Real-time video analytics

Configuration:

• 16GB holds full model in memory
• Fast bandwidth enables batch inference
• Low latency for user queries

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Implementation Considerations

System Integration

Multi-Stack Configurations:

Configuration	Total Memory	Total Bandwidth	Typical Application
4× H5AG36EXNDX017N	64GB	1.84 TB/s	High-end GPU
6× H5AG36EXNDX017N	96GB	2.76 TB/s	Professional workstation
8× H5AG36EXNDX017N	128GB	3.68 TB/s	AI accelerator flagship

Thermal Management

Heat Dissipation:

• 12-15W per stack typical
• 8 stacks = 96-120W total memory power
• Requires active cooling (heatsink + fan/liquid)
• Thermal design critical for 12H stacking

Cooling Solutions:

• Vapor chamber heat spreaders
• Direct liquid cooling (water blocks)
• High-efficiency thermal interface materials (TIM)

Package Integration

Silicon Interposer:

• Routes 1024 signals per stack
• Enables multiple HBM stacks adjacent to GPU die
• CoWoS (Chip-on-Wafer-on-Substrate) packaging
• TSMC/Samsung advanced packaging required

Power Delivery

Voltage Regulation:

• Clean 1.2V supply required
• Current capacity: 10-15A per stack
• 8 stacks: 80-120A total current
• Low-noise VRM essential

Conclusion

The H5AG36EXNDX017N delivers 16GB HBM2E capacity with 460 GB/s bandwidth through advanced 12-high die stacking, enabling flagship AI accelerators and professional GPUs to support cutting-edge workloads requiring both extreme bandwidth and large memory capacity. Ideal for GPT-scale AI training, HPC simulations, and professional graphics demanding maximum performance.

Key Advantages:

✅ 16GB Capacity: Double standard HBM2E for larger models
✅ 460 GB/s Bandwidth: Extreme memory throughput
✅ 12-High Stacking: Advanced 3D integration technology
✅ AI Optimized: Enables GPT-3/4 scale training
✅ HPC Ready: Large in-memory scientific datasets
✅ SK Hynix Quality: Industry-leading HBM manufacturer

Designing AI/HPC systems? Visit AiChipLink.com for technical resources and memory architecture consultation.

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Written by Jack Elliott from AIChipLink.

 

AIChipLink, one of the fastest-growing global independent electronic   components distributors in the world, offers millions of products from thousands of manufacturers, and many of our in-stock parts is available to ship same day.

 

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