AI Memory Demand Surge: HBM Memory and GPU Memory Bandwidth Transformation

Last Modified

2026-05-18

Update Frequency

Not

Format

PDF

This report first explains why HBM plays a key role in the evolution of LLM and AIGC applications. It then examines the HBM upgrade roadmap and explores how heterogeneous memory integration strategies can expand capacity and increase bandwidth to meet future AIGC demand.

Key Highlights

Explains the pivotal role of HBM in LLM and AIGC workloads.
Reviews the HBM upgrade roadmap and future development.
Examines heterogeneous memory integration to boost capacity and bandwidth.
Links these strategies to emerging AIGC performance requirements.

Infinite HBM Demand Fueled by Rapid LLM Evolution and Explosive Growth of AIGC Applications
- Table 1: Token Processing Capacity Across GPT Model Generations
- Figure 1: Text Generation Workflow and HBM Usage
- Figure 2: Image Generation Workflow and HBM Usage
- Figure 3: Video Generation Workflow and HBM Usage
- Table 2: Estimated HBM Requirements for Three Types of AI Generation Workloads
HBM-Centric Heterogeneous Memory Integration to Focus on Capacity and Bandwidth Upgrades
- Table 3: Theoretical Performance and Specification Parameters of Past HBM
- Figure 4: Schematics of Integration between HBM4 and LPDDR
- Figure 5: Integration between HBM and NMC Die
- Figure 6: Schematics of Integration between HBM5 and SOCAMM
- Figure 7: Schematics of HBM6 with Dual-Tower Architecture
- Figure 8: Schematics of Integration between HBM7 and HBF
TRI’s View