With data growth from artificial intelligence (AI) expanding at an exponential rate, the demand for high-bandwidth memory (HBM) has surged. HBM is essential for AI accelerators and GPUs, but it remains a premium and complex technology. The challenge is that GPU innovation is moving faster than industry standards, making customized solutions increasingly critical.
According to Dell’Oro Group, the global server and storage component market grew 62% year-over-year in Q1 2025, fueled largely by AI expansion. This growth has driven soaring demand for HBM, GPUs, accelerators, and network interface cards (NiCs).
GPU Performance Driving HBM Demand #
AI servers have rapidly expanded from 20% of the server market to nearly 60% in just a few years. This shift has been powered by GPUs with ever-increasing capacity, which in turn has pushed HBM adoption forward.
HBM supply, however, is tight—vendors are typically booked more than a year in advance. SK Hynix currently leads the HBM market with around 64% revenue share, followed by Samsung and Micron.
Micron plans to begin production of HBM4 in 2026, featuring a 2048-bit interface, followed by HBM4E in later years. The company also introduced an option for customized base die designs to better align with specialized compute requirements. In Q3 FY2025, Micron’s HBM revenue grew nearly 50% sequentially, pushing its annualized run rate to $6 billion.
While alternatives like GDDR, low-latency DRAM, or flash SSDs can support certain workloads, they lack the bandwidth and integration benefits of HBM. For top-tier AI performance, HBM remains irreplaceable.
Supply and Standards Bottlenecks #
One of the biggest challenges for HBM is the mismatch between GPU release cycles and memory standardization. GPU makers now launch new architectures annually, while JEDEC memory standards typically take years to update.
Unlike DDR or other memory technologies that transition every four to five years, HBM generations evolve every 2–2.5 years. This accelerated pace is reshaping the industry, forcing vendors to innovate faster than ever.
HBM wafer production is also rising sharply, outpacing traditional DRAM advances like DDR5. Testing requirements have become more demanding, with higher data bandwidth, larger device capacities, and more complex thermal management needed to handle power density.
Custom Solutions Reshaping HBM #
Another growing trend is the shift toward custom HBM implementations. Hyperscalers and SoC vendors increasingly require specialized memory features tailored to their AI accelerators and ASICs.
This has led to:
- More controller and logic functionality integrated directly into the HBM base die
- A shift of base die manufacturing to foundries like TSMC using 3nm and 5nm processes
- Greater reliance on advanced packaging technologies, such as 2.5D and 3D integration
These changes complicate testing, but they also enable more flexible architectures and tighter optimization for AI workloads.
Scaling Capacity for Growing AI Models #
AI model sizes are expanding at a staggering pace, with top models growing from millions to billions of parameters, and forecasts pointing to trillion-parameter models in the near future.
To meet this demand, next-generation HBM architectures (HBM4, HBM4E, and eventually HBM5) are focusing on:
- Higher bandwidth per stack
- Greater memory density to support massive model parameters
- Lower power consumption to meet data center efficiency goals
Custom architectures from vendors like Marvell have demonstrated the potential for:
- 33% greater memory capacity
- 25% more compute area
- Up to 70% lower memory interface power consumption
These advances are critical as AI workloads push the limits of compute, memory, and power budgets.
Conclusion #
HBM is evolving faster than traditional standards can keep up. While JEDEC specifications remain important, the industry is increasingly driven by GPU makers and hyperscalers that demand rapid innovation and customized solutions.
The future of HBM will be defined by its ability to balance:
- Unprecedented memory bandwidth
- Explosive capacity growth
- Energy efficiency for AI workloads
As AI continues to scale, custom HBM designs will become the norm, and innovation will remain well ahead of standardization.