How HBM3 and HBM4 Work: Differences, Performance, Costs, and Future Outlook

High Bandwidth Memory (HBM) is a specialized, 3D-stacked DRAM architecture designed to overcome the "memory wall"—the bottleneck where data movement speeds fail to keep pace with the exponential growth in processor compute power. By stacking memory dies vertically and connecting them with Through-Silicon Vias (TSVs) and ultra-wide interfaces, HBM allows for massive data throughput in a compact, power-efficient footprint.

How HBM3 and HBM4 Work

HBM3 uses a 1024-bit interface and 16 channels, delivering bandwidth around 819 GB/s per stack. It serves as the backbone for AI training and high-performance computing (HPC). HBM4, on the other hand, doubles the interface width to 2048 bits and increases the number of channels to 32. This architectural change allows for significantly higher bandwidth and capacity without requiring aggressive increases in clock speeds. Additionally, HBM4 uses a new base-logic die architecture that improves power efficiency and thermal management.

Key Differences Between HBM3, HBM3E, and HBM4

Performance and Speed

HBM4 offers a significant bandwidth boost—from about 1.2 TB/s in HBM3E to over 2 TB/s, with peak rates up to 3.3 TB/s. This increase is achieved through the wider interface and more channels, without needing drastic clock speed increases. HBM4 is not backward compatible with HBM3/HBM3E controllers, requiring new memory controllers and PHY designs.

Future Projection

HBM3E is currently the industry standard, powering flagship AI accelerators like NVIDIA's H200 and Blackwell. Mass production of HBM4 began in early 2026, and it will be foundational for NVIDIA's next-generation Rubin architecture. HBM4E is expected in 2026–2027, and the long-term roadmap targets system bandwidths of 64 TB/s by the end of the decade to support increasingly complex agentic AI models.

Costs

HBM is significantly more expensive than standard DRAM (e.g., DDR5) due to the complexity of 3D stacking and advanced packaging. HBM3 costs around $200 per 24GB stack, HBM3E costs about $300 per 36GB stack, and HBM4 is projected to cost around $500 per 48GB stack. Memory can account for 30–40% of the total manufacturing cost of an AI accelerator, making it a primary driver of GPU price increases.

Main Players

The HBM market is highly concentrated: SK Hynix (market leader with 50–62% share, strong partnership with NVIDIA), Samsung Electronics (leveraging large-scale semiconductor fabrication), and Micron Technology (aggressively expanding capacity). Together they control over 95% of global output.

Conclusion

HBM4 represents a major architectural leap, significantly boosting the performance of AI systems. The higher costs and lack of backward compatibility are challenges, but the bandwidth and capacity gains are critical for next-generation AI models. The three main players continue to drive innovation.