Why Big AI Ambitions Demand Powerful Data Infrastructure

By LAWRENCE FRANKLYN

The accelerating evolution of artificial intelligence (AI) is driving bigger strategic thinking and revenue ambitions everywhere.

But putting robust AI plans into action, moving from experimentation to production to mass deployment, takes more than devising an AI-powered growth strategy.

Making the most of AI’s rapidly advancing capabilities depends on infrastructure that can power up and scale with them to meet the accelerating demand on graphics processing units (GPUs), data centers, and storage. For an enterprise pursuing a transformational AI strategy, the greatest challenge is understanding how legacy infrastructure might be getting in the way of its ambitions.

“Legacy infrastructure was built for human interactions,” says Xin Guo, co-CEO of Solidigm. “New AI infrastructure needs to be built for machine interactions.”

To fully tap the vast supply of data that powers AI, many organizations now need to implement high-performance, high-capacity data storage platforms that incorporate cooling and edge efficiency.

Compute Power

In recent months, questions of storage have taken on greater urgency as organizations seek to maximize their AI capabilities by gaining more power from their GPUs and fitting greater volumes of data into smaller spaces. Architecture that seemed sufficient to support AI when an enterprise first embarked on its AI planning may suddenly seem obsolete.

The ongoing explosion in data volume—driving such innovations as generative AI and agentic AI, which run on AI inference pipelines fueled by retrieval augmented generation (RAG) and key value (KV) caches—shows no signs of abating.

One basic prompt could touch off an agentic AI loop that taps a far larger database to read and summarize an ever-greater tranche of material to instantaneously generate dozens of additional queries and prompts so it can interpret what a user might need from a natural-language input.

RAG applications run on massive vector databases and large language models produce rapidly accumulating KV caches that work as context memory but can cause infrastructure bottlenecks, increasing latency and slowing performance. That puts immense pressure on AI infrastructure far beyond what it called for even a year ago and could be adding more cost than value.

An organization running large energy-inefficient hard disk drives (HDDs) may struggle to fully scale and increase its revenue with AI, which demands GPUs powerful enough to rapidly process and deliver complex data while consuming less energy and physical space than ever.

An AI-powered organization needs considerable rack power, facility power, floor space, and cooling capacity to support a data center, but building AI on legacy HDD architecture can keep an enterprise from seeing AI’s full potential by incurring high costs for power, cooling, footprint, and operational overhead that might otherwise fuel revenue.

To realize the greatest returns on their AI investments, enterprises may now need to upgrade to high-performance, high-capacity storage designed for AI data access patterns; high-density storage platforms maximizing capacity per rack unit and watt; and infrastructure featuring liquid and immersion cooling readiness and edge-optimized footprints.

Data Density

Beyond faster compute and GPUs, the next phase of AI depends on data density to determine how far AI strategy can scale.

Organizations that factor data density into their AI planning can move, store, and access massive amounts of data efficiently and economically on solid-state drives (SSDs) so they can easily scale to meet demand without needing to rebuild their architecture.

Beyond meeting the storage capacity and performance capability demands to rapidly scale for AI, using energy-efficient, liquid-cooled SSDs can help an enterprise apply more energy to compute power instead of to cooling expenses. By contrast, applying standard HDDs may bear hidden long-term expenses, as they depend on costly air-cooling mechanisms that demand far more physical space, a serious consideration in planning a scalable data center to power AI infrastructure.

Succeeding with AI Transformation

An enterprise’s AI transformation depends on a solid foundation to support the heightening demands of data processing and storage. But an enterprise that relies on its existing AI infrastructure risks limiting its AI ambitions to only what that infrastructure can support.

Today’s AI-powered business strategy depends on removing storage bottlenecks to accelerate and sustain data delivery, increasing data capacity with SSDs to take up less physical space and consume less power than standard drives, and taking a foundation-first approach that scales AI capabilities to real-world parameters.

With AI evolving so quickly, organizations relying on outdated AI foundations could lose ground to their competitors. Those with foundations that scale and adapt can meet and exceed their ambitions for transformation and growth.

Lawrence Franklyn is senor vice president and head of information technology at Solidigm.