In this third article of the AI infrastructure series, you will learn about AI infrastructure compute, storage, observability, performance, optimization (deep dive), and security. This is the final part in my three-part AI infrastructure series. It’s recommended to read the previous two articles published on DZone:
- AI Infrastructure for Agents and LLMs: Options, Tools, and Optimization
- AI Infrastructure Guide: Tools, Frameworks, and Architecture Flows
Compute Layer Architecture
The Compute Layer provides the raw processing power needed for AI workloads, with specialized considerations for GPU management, resource allocation, and workload scheduling. This layer must handle the unique characteristics of AI workloads: high memory requirements, long-running processes, and dynamic resource needs.
This article has been indexed from DZone Security Zone
Read the original article: