“We tend to think of AI as a single workload, but that’s not the case. It’s thousands, millions, billions of different workloads,” said Jim McGregor, founder and principal analyst at Tirias Research. AI inference transforms optimization from a focus on raw computing power into a coordinated infrastructure challenge involving memory, storage, networking, and data movement.
For business leaders, the priorities are clear: AI infrastructure decisions must balance cost, flexibility, scalability, and long-term performance. Organizations that improve performance per watt, reduce their environmental impact, and eliminate memory and storage bottlenecks will be better positioned to scale AI deployments and support future workloads.
AI inference demands new infrastructure architectures
Deploying modern AI systems on legacy infrastructure can limit the technology’s transformative potential. From accelerating scientific discovery to enabling autonomous digital agents, purpose-built AI architectures are essential for unlocking the full value of inference and other advanced workloads.
Traditional enterprise IT has often relied on relatively stable infrastructure requirements. AI inference and agentic AI introduce far more demanding needs, including lower latency, faster data movement, greater scalability, and higher utilization. As a result, infrastructure architecture plays a critical role in AI performance, reliability, and cost efficiency.
“Data centers now need to support continuous, distributed, and increasingly real-time AI services, none of which are a single workload,” McGregor said. “From a system-level perspective, they all require different requirements.”
To support real-time AI applications, companies should treat memory and storage as core components of the computing system rather than secondary supporting hardware. Effective AI data pipelines must rapidly ingest, clean, transform, store, move, and deliver data. Unlike traditional applications, inference workloads place sustained pressure on infrastructure through continuous data retrieval, caching, and processing.
Performance is therefore no longer the only benchmark that matters. Enterprises must balance speed with energy efficiency, operating cost, scalability, and flexibility as they support multiple AI services without overbuilding infrastructure for occasional peak demand.
“You need to optimize your entire network, including memory and storage, depending on the type of workload you plan to run,” McGregor said. “We need to understand in detail what those workloads will be.”
Source: www.technologyreview.com