Why AI infrastructure teams should rethink the role of capacity in the age of inference

As AI workloads evolve from training to inference and agentic AI, the conversation is shifting from compute performance to infrastructure efficiency.

One of the biggest challenges is managing the explosive growth of key-value (KV) cache. The more context an AI application can retain, the greater the likelihood of a cache hit and the lower the need for expensive GPU recomputation.

Conventional wisdom suggests that only the fastest storage belongs anywhere near AI workloads. But as KV cache requirements grow, capacity and economics are becoming just as important as raw performance.

Here are five reasons HDDs may have an important role to play in the future of AI infrastructure.

1. AI Inference Has a Capacity Challenge

KV cache allows large language models (LLMs) to reuse previously computed context instead of processing it again.

As context windows grow and agentic AI workflows become more complex, KV cache requirements are expanding rapidly. Infrastructure teams are increasingly faced with a simple challenge:

How do you retain more context without dramatically increasing costs?

The answer cannot rely solely on expensive memory tiers. At scale, organizations need a way to extend cache capacity economically while maintaining the performance needed to support inference workloads.

2. Every Cache Miss Comes at a Cost

The value of KV cache is straightforward: avoid asking GPUs to repeat work they have already completed.

When a cache miss occurs:

• Inference latency increases.
• GPU resources are consumed by recomputation.
• Infrastructure costs rise.
• Valuable GPU capacity is diverted from new work.

For AI operators, the question is no longer simply, “What is the fastest storage?”

The more important question is:

“What storage is fast enough to avoid GPU recomputation?”

If cached data can be retrieved more efficiently than it can be regenerated, the cache is delivering value.

3. HDDs Deliver Cost-Effective Capacity at Scale

Every storage tier serves a purpose.

• HBM delivers maximum performance but limited capacity.
• DRAM expands available cache but remains expensive.
• SSDs provide high-speed persistent storage.
• HDDs provide massive capacity with compelling economics.

This makes HDDs particularly attractive as AI environments scale.

Rather than attempting to keep every token in expensive memory tiers, organizations can leverage HDDs as a bulk-capacity layer that extends cache retention and increases the likelihood of cache hits.

The result is a larger effective cache footprint without a proportional increase in infrastructure spending.

4. The Goal Isn’t Replacing SSDs. It’s Complementing Them.

The most effective AI storage architectures are not built around a single technology.

Instead, they leverage multiple tiers, allowing each layer to do what it does best.

Recent KV cache benchmarking demonstrated that when HDD capacity is paired with a modest DRAM or SSD staging layer, the resulting architecture can deliver performance comparable to SSD-centric approaches while dramatically increasing available cache capacity.

In that model:

• DRAM provides immediate access to active data.
• SSDs serve as high-performance staging tiers.
• HDDs provide scalable, cost-effective bulk capacity.

Together, these tiers help balance performance, capacity, and economics.

5. AI’s Next Bottleneck May Be Economics

The AI industry has spent years focused on model size, GPU counts, and training performance.

Inference is changing the equation.

As organizations deploy AI at scale, infrastructure efficiency becomes increasingly important. Maximizing cache hit rates can be just as valuable as increasing compute performance because every cache hit helps avoid costly recomputation.

And maximizing cache hit rates requires capacity.

That is where HDDs offer a compelling advantage.

The future of AI infrastructure is not simply about making every layer faster. It is about enabling expensive GPU resources to spend more time creating value and less time repeating work that has already been done.

Conclusion

HDDs are often overlooked in AI discussions because they are not the fastest storage medium available.

But KV cache introduces a different optimization challenge.

The objective is not necessarily achieving the lowest possible latency. The objective is maximizing cache residency, improving cache hit rates, and reducing costly GPU recomputation.

Emerging benchmark data suggests that when deployed as part of a tiered storage architecture, HDDs can provide the scale, economics, and performance necessary to support next-generation AI inference environments.

For AI infrastructure architects, the question may no longer be:

“Are HDDs fast enough for AI?”

Instead, it may be:

“Can we afford to build large-scale KV cache without them?”