Learn about storage architecture optimization for AI workloads in this technical presentation from the SNIA Compute, Memory, and Storage Summit. Explore high-performance throughput requirements for GPU cluster servers and large-capacity mass storage solutions essential for AI model training data management. Discover Supermicro's AI storage solutions featuring flash-based storage servers and high-capacity disk storage servers, integrated with partner technologies from WEKA, OSNexus, Quantum ActiveScale, and Scality. Examine a real-world case study of a 25PB AI-optimized storage implementation at a leading technology manufacturing company, focusing on machine vision applications and their broader applicability to various use cases. Gain insights from Product Director Paul McLeod's expertise on designing and deploying storage architectures specifically tailored for demanding AI workloads.
Storage Architecture Optimized for AI Workloads - High Performance Solutions for GPU Clusters
Overview
Syllabus
Storage Architecture Optimized for AI Workloads
Taught by
SNIAVideo
Related Courses
-
Intel® Solutions Pro – Principles of AI Everywhere
-
Benchmarking Storage with AI Workloads - Performance Analysis and Optimization
-
Compute, Memory, and Storage: Optimized Configurations for a New Era of Workloads
-
AI at the Intersection of Storage and Networking
-
Top HPC Best Practices for Deploying and Managing Demanding Workloads
-
Storage Requirements for AI Infrastructure - From Data Preparation to Inference
