Architecting memory and storage in the AI era
Memory and storage are becoming the real bottleneck in AI inference — not compute.
Why it matters
As AI inference workloads scale to real-time, multi-million-datapoint scenarios, the infrastructure layer (memory hierarchy, storage latency, I/O bandwidth) is reshaping how data centers are built. This is a buildout and architecture story, not a model story.
The key facts
8 to knowHealthcare systems analyzing millions of data points in real time for medical research
Real-time customer service resolution at scale
Memory and storage architecture as critical infrastructure constraint for AI inference
Published in MIT Technology Review (September 2026)
Article focuses on real-time AI inference infrastructure requirements
Examples: healthcare systems analyzing millions of data points in real time; intelligent assistants handling thousands of concurrent requests
Core subject: memory and storage architecture as the bottleneck in continuous intelligence systems
Date: September 2026 (recent publication)
Go to the source
MIT Technology Reviewtechnologyreview.com
Publisher excerpt: 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…