Storage Bottleneck in Hardware Design AI's Storage Demands: Can Hardware Designers Keep Up?

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Artificial intelligence is increasingly becoming part of everyday life—whether in wearables, autonomous vehicles, or cloud models. This puts unprecedented pressure on hardware development. In this context, storage is increasingly becoming the critical bottleneck for AI systems at all levels.

AI workloads rely on fast, high-density storage to supply data-hungry models. Whether a generative model is being trained in the data center or object recognition is being executed on an edge device, storage bandwidth and capacity are now the key factors limiting performance, energy efficiency, and thermal stability.

To meet these requirements, hardware teams rely on various memory architectures. These include High-Bandwidth Memory (HBM) for wide I/O channels and high throughput during AI training. GDDR6/GDDR7 is used for graphics-intensive or inference-heavy tasks. LPDDR5/LPDDR5X is suitable for power-constrained edge AI applications, and 3D-stacked DRAM offers more capacity in a smaller space. Emerging nonvolatile technologies like MRAM and ReRAM show promising approaches for persistent AI state storage and faster boot times for edge devices in the future but are still largely under development for mainstream AI applications. Each of these technologies, however, comes with specific limitations regarding power consumption, compatibility, thermal design, availability, and performance. This necessitates design trade-offs at the system level.

Hardware Design for AI is Defined By Storage

In traditional hardware development processes, memory selection only occurred after decisions about the CPU or GPU had been made. However, in the era of AI, this order has reversed. Today, hardware developers find that the choice of memory architecture determines the entire hardware stack. It affects the board layout, power supply design, and the form factor of the final product.

The selection of high-speed GDDR6 can enable faster AI inference but requires dedicated power rails and more complex PCB layouts, which introduce additional thermal and EMC challenges. Using LPDDR5 in battery-powered mobile devices reduces power consumption but limits the available bandwidth, potentially constraining model size or inference rates. High Bandwidth Memory (HBM), on the other hand, offers enormous throughput potential but demands advanced packaging and cooling technologies, such as vapor chambers or liquid cooling systems, both of which drive up costs.

These are not theoretical concerns. As memory requirements for AI evolve rapidly, developers must define the memory configuration and interfaces earlier—often before software models and firmware are stabilized. This increases the risk associated with early design decisions: a poorly calculated memory choice may necessitate a PCB redesign or limit future upgrade paths.

Volatility in the Storage Supply Chain

With the rapid proliferation of AI models, the demand for high-performance storage technologies is also increasing. However, DRAM and NAND, in particular, have always been cyclical and highly price-sensitive markets. The current surge in demand driven by AI is further amplifying this volatility.

HBM, GDDR6, and LPDDR5 are now considered strategic key technologies. Their production is concentrated in South Korea, which dominates the DRAM market. Taiwan, on the other hand, leads in advanced packaging and foundry services. Japan supplies materials and specialty memory.

This geographical concentration entails certain risks, including geopolitical instability, such as tensions in the Taiwan Strait. Additionally, export controls and trade restrictions pose challenges. Manufacturing bottlenecks are also an issue, as EUV lithography and DRAM-specific equipment are only available in a limited number of factories. Material shortages, such as for fluorinated process gases or specialized photoresists, can further disrupt the supply chain and delay deliveries.

For developers working on AI-driven new launches, this means longer lead times and a higher risk of component obsolescence—such as when memory roadmaps shift or a specific component becomes scarce.

It's Not Just About Storage Capacity—Access Matter Too

In AI hardware, more memory is only helpful if it is the right type of memory. It must be installed in the right location and connected in the right way.

The transition from universal computing tasks to AI-centric workloads opens up new design approaches for product development teams. Tightly coupled memory reduces latency but requires deeper integration with processors or SoCs. Loosely coupled memory offers more flexibility but can lead to bottlenecks depending on the architecture. Memory access patterns—such as tensor reuse, stride accesses, or sparsity—must be optimized according to the model structure and compute pipeline. Partitioning decisions—such as storing weights in HBM, activations in LPDDR, and intermediate data in NVM—also significantly impact performance, thermal behavior, and battery life.

Compatibility is a technical minefield. Developers must ensure that the memory is not only electrically compatible with AI chips, FPGAs, or SoCs but also logically aligned with parameters such as bandwidth, latency, and parallelism. An incorrect configuration can significantly limit computational performance, increase energy consumption, or result in costly hardware accelerators being underutilized.

Proactive Memory Planning As A Clear Competitive Advantage

Companies that successfully bring AI hardware to market today not only optimize performance but also embed resilience into their memory strategy from the very beginning.

This means that risks related to procurement and lifecycle status must be considered during memory selection. Memory access and throughput should be simulated early in the design phase. Hardware, software, and supply chain teams need to collaborate to identify obstacles in a timely manner. Additionally, investing in design platforms that support real-time collaboration and component intelligence is worthwhile.

When design and procurement teams work in isolation, memory decisions are often delayed or made without coordination. The result is costly errors and missed market opportunities. However, when teams collaborate from the start, they can identify alternatives, reduce availability risks, and develop systems that balance power consumption, computational performance, and supply chain security.

As developers increasingly deploy AI across all areas—from edge sensors to data center infrastructure—memory becomes a critical lever in the competitive landscape, where strategies for performance, scalability, and component availability intersect. Those who view memory not as a secondary factor but as a key design constraint will successfully bring to market products that meet the demands of evolving AI workloads. This provides a clear advantage in the race for the next product generation. (sg)

*Justin Sears is responsible for product marketing of B2B SaaS platforms at Altium. As "Head of Product Marketing for SaaS," he leads the team that positions cloud solutions for electronics development in the market.

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