HBM3-24GB
SK Hynix HBM3 24GB next-generation High Bandwidth Memory with 1.5TB/s bandwidth for advanced AI
Product Overview
Description
The HBM3 24GB is SK Hynix's third-generation High Bandwidth Memory delivering up to 1.5TB/s bandwidth. This 24GB memory stack is the cutting edge of AI memory technology, providing 50% more bandwidth and 50% more capacity than HBM2E.
Featuring 12-layer 3D stacking with advanced TSV technology, the HBM3 achieves data rates of 6.4GT/s per pin, doubling the speed of HBM2E. The 24GB capacity provides ample memory for large language models and advanced AI networks with billions of parameters.
HBM3 is essential for next-generation AI training systems including large language model training, autonomous driving AI, and advanced scientific simulations. The memory's massive bandwidth enables AI accelerators to achieve higher utilization by eliminating memory bottlenecks.
Product Series
HBM
Primary Application
Large language model training
Key Features
- 24GB HBM3 memory stack
- Up to 1.5TB/s bandwidth
- 12-layer 3D stacking technology
- 6.4GT/s per pin data rate
- 1.1V improved power efficiency
- Next-gen AI memory
Specifications
| Memory Type | HBM3 |
|---|---|
| Capacity | 24GB |
| Stack Layers | 12 layers |
| Interface Width | 1024-bit |
| Data Rate | 6.4GT/s per pin |
| Bandwidth | Up to 1.5TB/s |
| Voltage | 1.1V |
| Package | 2.5D SiP |
Applications
Large language model training
Electronic system design
Advanced AI accelerators
Electronic system design
Exascale HPC systems
Electronic system design
Autonomous driving AI
Electronic system design
Scientific simulation
Electronic system design
Next-gen GPU graphics
Electronic system design
FAE Expert Insights
"HBM3 is major leap in AI memory technology. The 1.5TB/s bandwidth is essential for training the latest large language models with hundreds of billions of parameters. In my experience supporting AI system designers, HBM3 enables new AI architectures that were previously memory-bound. The 24GB capacity accommodates the massive weight matrices in modern neural networks. The improved power efficiency at 1.1V helps manage thermal constraints in high-density AI systems. HBM3 is the memory technology that will enable the next wave of AI breakthroughs."
1.5TB/s bandwidth enabling next-generation large language model training
— Dr. Amanda Chen, BeiLuo
Frequently Asked Questions
What are the key differences between HBM3 and HBM2E packaging?
HBM3 and HBM2E have similar packaging approaches but different requirements: 1) Thermal constraints - HBM3's higher power density requires more robust thermal solutions, 2) Signal integrity - higher data rates in HBM3 demand tighter signal integrity design, 3) Interposer design - HBM3 may require larger interposers with more routing layers, 4) Test challenges - HBM3's higher speed requires advanced test equipment, 5) Co-design - both require close system co-design, HBM3 more so. Despite challenges, HBM3 uses similar 2.5D packaging principles. The ecosystem for HBM3 is maturing rapidly with TSMC and Samsung providing advanced packaging options.
HBM3 requires advanced packaging capabilities. Engage packaging partners early in design.
What AI applications specifically benefit from HBM3?
HBM3 benefits applications requiring extreme memory bandwidth and capacity: 1) Large language model training - GPT-4, PaLM, and similar models with 100B+ parameters, 2) Autonomous driving - processing sensor data from cameras, LiDAR, radar in real-time, 3) Scientific simulation - molecular dynamics, climate modeling, materials science, 4) Recommendation systems - training with massive user-item interaction data, 5) Protein folding - AlphaFold-style computations requiring large memory footprint. HBM3 enables AI systems that were previously impossible due to memory limitations. The 1.5TB/s bandwidth and 24GB capacity unlock new possibilities in AI research and deployment.
HBM3 is essential for large language models and advanced AI research. Contact our team for HBM3 system design support.
What is the power consumption improvement of HBM3 over HBM2E?
HBM3 provides significant power efficiency improvements: 1) Voltage reduction - HBM3 operates at 1.1V vs HBM2E's 1.2V, 2) Power per GB/s - approximately 20% more efficient than HBM2E, 3) Total power - 24GB HBM3 consumes similar power to 16GB HBM2E despite higher capacity, 4) Thermal benefits - lower power reduces cooling requirements, 5) System impact - enables higher compute density in AI accelerators. The improved power efficiency is crucial for managing thermal constraints in high-performance AI systems.
HBM3 provides better power efficiency. Plan thermal design for similar power as HBM2E despite higher capacity.
What is the availability timeline for HBM3?
HBM3 availability timeline: 1) Current status - HBM3 is in production and shipping to major AI chip companies, 2) Supply ramp - production volume increasing throughout 2024-2025, 3) Lead times - currently longer than HBM2E due to high demand, 4) Roadmap - SK Hynix continues to expand HBM3 capacity, 5) Next-gen - HBM3E and HBM4 in development. For new AI accelerator designs, HBM3 is the recommended choice for production starting in 2024. Contact SK Hynix for specific availability and allocation.
HBM3 is available for production designs. Plan for potential lead times due to high demand.
How does HBM3 support large language model training?
HBM3 enables large language model training through: 1) Massive bandwidth - 1.5TB/s keeps GPU/TPU compute units fed with data, 2) Large capacity - 24GB accommodates billion-parameter model weights, 3) Model parallelism - multiple HBM3 stacks enable trillion-parameter models, 4) Training efficiency - high bandwidth reduces training time significantly, 5) Scalability - HBM3 supports scaling to hundreds of accelerators. GPT-4 class models with 100B+ parameters require HBM3's combination of bandwidth and capacity. Without HBM3, these models would be memory-bound.
HBM3 is essential for training 100B+ parameter models. Contact SK Hynix for LLM system design support.