Definition: The hardware and system architecture designed to support the unique demands of artificial intelligence workloads.
Explanation
AI infrastructure refers to the combination of computing resources, memory technologies, storage solutions, and networking components optimized to handle the large-scale, latency-sensitive, and data-intensive nature of AI workloads. Unlike traditional computing systems, AI infrastructure must manage vast datasets that are frequently accessed and processed in parallel, requiring a memory hierarchy that goes beyond conventional DRAM capabilities. This includes integrating high-bandwidth memory, flash storage, and other specialized memory types to balance speed, capacity, cost, and power consumption effectively.
Example
An AI infrastructure setup might include GPUs with high-bandwidth memory for fast data processing, large pools of DRAM for active data, and NAND flash storage for less frequently accessed data, all interconnected to maintain low latency and high throughput for training deep learning models.
Who This Is For
This term is relevant for system architects, AI researchers, data center engineers, and IT professionals involved in designing, deploying, or managing hardware and software environments tailored for AI applications.
Related Terms
DRAM, NAND flash, high-bandwidth memory, AI workloads, memory hierarchy
Also Known As
artificial intelligence infrastructure, AI compute infrastructure