Architecting memory and storage in the AI era
Architecting memory and storage in the AI era
摘要: Sponsored
Artificial intelligence
Architecting memory and storage in the AI era
With AI inference now driving enterprise workloads, organizations m
正文
Sponsored
Artificial intelligence
Architecting memory and storage in the AI era
With AI inference now driving enterprise workloads, organizations must rethink infrastructure for speed, efficiency, scalability, and performance per watt to unlock AI’s real-world potential.
MIT Technology Review Insights
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September 4, 2026
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The era of AI inference has arrived. Imagine a healthcare system analyzing millions of data points in real time to accelerate life-saving medical research, or an intelligent assistant instantly resolving thousands of complex customer needs at once. These real-world breakthroughs rely on advanced infrastructure acting as the engine of continuous intelligence, powering real-time services while also supporting an increasingly intelligent edge of IoT and consumer devices. However, in this inference-driven landscape, every delay, bottleneck, or wasted watt directly affects human outcomes and operating costs.
This shift changes what infrastructure must deliver. Performance, latency, memory bandwidth, storage throughput, and networking cannot be optimized in silos. Inference workloads are continuous, geographically distributed, and highly sensitive to response time, requiring systems designed for scale, resilience, and efficiency from the start.
“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 a