H5AG38EXNDX026N: Technical Guide to SK Hynix 24GB HBM2E Memory Stack

Introduction

The H5AG38EXNDX026N is a 24GB High Bandwidth Memory 2E (HBM2E) stack manufactured by SK Hynix, featuring 12-high (12H) vertically-stacked DRAM dies delivering 1.15 TB/s bandwidth through 1024-bit interface, designed for flagship AI accelerators, high-end GPUs, and HPC systems requiring maximum memory capacity per stack with extreme bandwidth.

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Technical Overview

Core Specifications

Parameter	Specification
Capacity	24GB per stack
Stack Height	12-High (12H)
Interface Width	1024-bit
Data Rate	9.2 Gbps per pin
Bandwidth	1.15 TB/s per stack
Voltage	1.1V
Channels	16 channels × 64-bit
Process Node	1α/1β nm
Operating Temp	0°C to +95°C

Key Features

Maximum Capacity (12H):

• 24GB per stack (50% more than 16GB HBM2E)
• 12 DRAM dies stacked vertically
• Enables ultra-high-capacity GPU configurations
• Critical for large AI model training

Extreme Bandwidth:

• 1.15 TB/s per stack (1,150 GB/s)
• 9.2 Gbps per pin signaling
• Aggregate bandwidth: 4.6-9.2 TB/s (4-8 stacks)
• 40% higher than standard HBM2E (819 GB/s)

Advanced 3D Stacking:

• TSV (Through-Silicon Via) technology
• 12-layer vertical integration
• Optimized thermal design
• Enhanced reliability features

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Complete Specifications

Memory Organization

Parameter	Value
Total Capacity	24GB (192 Gb)
Stack Configuration	12-High (12 dies)
Capacity per Die	2GB (16 Gb)
Channels	16 independent
Interface Width	1024-bit (16 × 64-bit)
Banks	16 banks per channel

Performance Specifications

Parameter	Typical	Unit
Bandwidth per Stack	1,150	GB/s
Data Rate	9.2	Gbps
Access Latency	100-120	ns
Power per Stack	14-17	W
Power Efficiency	~75	GB/s/W

Thermal & Power

Parameter	Value
Operating Voltage	1.1V
Typical Power	14-17W per stack
Peak Power	~20W per stack
Thermal Design	Enhanced for 12H stacking
Recommended Cooling	Active (heatsink + airflow)

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Applications

Flagship AI Accelerators

Current Deployment:

• NVIDIA H200: 6× 24GB HBM2E = 141GB total
• AMD MI300X: 8× 24GB HBM2E = 192GB total
• Intel Gaudi 3: Multiple 24GB stacks

Use Cases:

• Large Language Model (LLM) training (GPT-4, PaLM 2)
• Computer vision model training (Stable Diffusion 3)
• Multimodal AI (text + image + video)
• Reinforcement learning (AlphaGo-scale)

High-End Professional GPUs

Graphics Workloads:

• 8K/16K content creation
• Real-time ray tracing (complex scenes)
• VFX rendering (film production)
• Scientific visualization

Configuration:

• 4× H5AG38EXNDX026N = 96GB GPU
• 6× H5AG38EXNDX026N = 144GB GPU
• Sufficient for ultra-high-resolution workflows

HPC & Supercomputing

Scientific Computing:

• Computational Fluid Dynamics (CFD)
• Molecular dynamics (billion-atom simulations)
• Climate modeling
• Quantum chemistry calculations

Benefits:

• Large datasets in-memory
• 1.15 TB/s sustains computation
• Reduced memory bottlenecks

AI Inference at Scale

Cloud AI Serving:

• GPT-4 scale model inference
• DALL-E/Stable Diffusion serving
• Real-time video analytics
• Multi-tenant inference platforms

Capacity Advantage:

• 24GB per stack fits larger models
• Batch processing efficiency
• Reduced model parallelism complexity

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Implementation Considerations

Multi-Stack Configurations

4-Stack System (96GB):

• Total bandwidth: 4.6 TB/s
• Total power: 56-68W (memory only)
• Application: High-end professional GPU

6-Stack System (144GB):

• Total bandwidth: 6.9 TB/s
• Total power: 84-102W (memory only)
• Application: NVIDIA H200 configuration

8-Stack System (192GB):

• Total bandwidth: 9.2 TB/s
• Total power: 112-136W (memory only)
• Application: AMD MI300X configuration

Thermal Management

Challenges:

• 12H stacking generates significant heat
• 14-17W per stack requires active cooling
• 8-stack system: 112-136W memory thermal load

Solutions:

• Vapor chamber heat spreaders
• Direct liquid cooling (water blocks)
• High-performance thermal interface materials
• Optimized airflow design

Platform Integration

Silicon Interposer:

• Routes 1024 signals per stack
• Enables multiple HBM stacks adjacent to GPU die
• CoWoS (Chip-on-Wafer-on-Substrate) packaging
• Advanced 2.5D integration

Power Delivery:

• Clean, low-noise 1.1V supply
• High current capacity (12-15A per stack)
• 8 stacks: 96-120A total current requirement
• Low-impedance power distribution

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Comparison

vs 16GB HBM2E (8H)

Feature	24GB (H5AG38EXNDX026N)	16GB (8H)
Capacity	24GB	16GB
Stack Height	12H	8H
Bandwidth	1.15 TB/s	1.15 TB/s
Power	14-17W	12-15W
Thermal Challenge	Higher	Moderate
Best For	Maximum capacity	Balanced

Trade-off: Same bandwidth, 50% more capacity, slightly higher power.

vs 36GB HBM2E (18H)

Feature	24GB (H5AG38EXNDX026N)	36GB (18H)
Capacity	24GB	36GB
Stack Height	12H	18H
Availability	Volume production	Limited
Cost	Moderate	Premium
Thermal	Manageable	Challenging

Note: 36GB stacks rare, primarily custom designs.

System-Level Impact

6-Stack GPU (NVIDIA H200 equivalent):

• 24GB stacks: 144GB total
• 16GB stacks: 96GB total
• Benefit: 50% more capacity, fits larger AI models

Conclusion

The H5AG38EXNDX026N delivers maximum HBM2E capacity with 24GB per stack through 12-high die stacking, maintaining flagship 1.15 TB/s bandwidth while enabling ultra-high-capacity configurations (144GB-192GB) for GPT-4 scale AI training, professional 8K/16K graphics, and memory-intensive HPC applications. As the current capacity leader in production HBM2E, it powers flagship accelerators like NVIDIA H200 and AMD MI300X.

Key Advantages:

✅ 24GB Capacity: 50% more than 16GB HBM2E
✅ 1.15 TB/s Bandwidth: Extreme memory throughput
✅ 12H Stacking: Advanced 3D integration
✅ Flagship Deployment: H200, MI300X proven
✅ AI Optimized: GPT-4+ scale model training
✅ SK Hynix Quality: Industry-leading HBM manufacturer

Designing next-gen AI systems? Visit AiChipLink.com for HBM sourcing and architecture consultation.

 

 

 

 

 

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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