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- Adopt purpose-built architectures rather than retrofitting legacy systems to unlock the transformative potential of AI.
- Elevate memory and storage to core system roles to enable low latency continuous data access for real-time inference.
- Design a rapid data pipeline to ingest, clean, transform, store, move, and deliver data for continuous, distributed AI services.
- Optimize the entire network including memory, storage, and networking while balancing performance, efficiency, cost, and scalability.
“We tend to think of AI as a single workload, and it’s not. It’s thousands, it’s millions, it’s billions of different workloads,” says Jim McGregor, founder and principal analyst, Tirias Research. AI inference changes the optimization problem from one of raw compute to coordinated infrastructure—memory, storage, and networking.
For business leaders, the priority is clear: AI infrastructure decisions must balance cost, flexibility, and future readiness. The winners will be organizations that improve performance per watt, reduce environmental footprint, and remove memory and storage bottlenecks before they limit growth.
AI inference requires a new architectural approach
Systems for AI need to be rearchitected because shoehorning modern AI systems into legacy infrastructure limits AI’s transformative potential. Purpose-built architectures are essential to realize the true value of AI, from accelerating scientific discovery to creating truly autonomous digital agents.
Traditional enterprise IT has been able to rely on relatively stable infrastructure assumptions, but inference and agentic AI introduce new demands around latency, data movement, scalability, and utilization that make architecture choices far more consequential.
“Data centers must now support continuous, distributed, and increasingly real-time AI services—none of which are a single workload,” says McGregor. “They all require different requirements from a system-level perspective.”
To support real-time AI, enterprises can no longer view memory and storage merely as supporting hardware, but at the heart of the system. Organizations need to architect a data pipeline that can rapidly ingest, clean, transform, store, move, and deliver data. Inference workloads place sustained pressure on infrastructure in ways that look very different from earlier training-centric deployments, demanding continuous data retrieval and caching that traditional applications never required.
Accordingly, performance by itself is no longer the sole benchmark that matters. Enterprises increasingly must balance performance with efficiency, cost, and scalability, especially as they try to support different AI services without overbuilding infrastructure for peak conditions.
“You have to optimize the entire network, and that includes memory and storage, around the types of workloads you plan on running,” says McGregor. “You have to really have a detailed understanding of what those workloads are going to be.”
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