Use Cases for NVMe-oF for Deep Learning Workloads and HCI Pooling | SNIA

Abstract

The efficiency, performance and choice in NVMe-oF is enabling some very unique and interesting use cases – from AI/ML to Hyperconverged Infrastructures. Artificial Intelligence workloads process massive amounts of data from structured and from unstructured sources. Today most deep learning architectures rely on local NVMe to serve up tagged and untagged datasets into map-reduce systems and neural networks for correlation. NVMe-oF for Deep Learning infrastructures enables a shared data model to ML/DL pipelines without sacrificing overall performance and training times. NVMe-oF is also enabling HCI deployment to scale without adding more compute, enabling end customers to reduce dark flash and reduce cost. The talk explores these and several innovative technologies driving the next storage connectivity revolution.

Learning Objectives

Storage architectures for Deep Learning Workloads,Extending the reach of HCI platforms using NVMe-oF,Ethernet Bunch of Flash architectures