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NVA-1144: NetApp HCI AI Inferencing at the Edge Data Center with H615c and NVIDIA T4

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Arvind Ramakrishnan, NetApp

This document describes how NetApp HCI can be designed to host artificial intelligence (AI) inferencing workloads at edge data center locations. The design is based on NVIDIA T4 GPU-powered NetApp HCI compute nodes, an NVIDIA Triton Inference Server, and a Kubernetes infrastructure built using NVIDIA DeepOps. The design also establishes the data pipeline between the core and edge data centers and illustrates implementation to complete the data lifecycle path.

Modern applications that are driven by AI and machine learning (ML) have pushed the limits of the internet. End users and devices demand access to applications, data, and services at any place and any time, with minimal latency. To meet these demands, data centers are moving closer to their users to boost performance, reduce back-and-forth data transfer, and provide cost-effective ways to meet user requirements.

In the context of AI, the core data center is a platform that provides centralized services, such as machine learning and analytics, and the edge data centers are where the real-time production data is subject to inferencing. These edge data centers are usually connected to a core data center. They provide end-user services and serve as a staging layer for data generated by IoT devices that need additional processing and that is too time sensitive to be transmitted back to a centralized core.
This document describes a reference architecture for AI inferencing that uses NetApp HCI as the base platform.

Customer Value

NetApp HCI offers differentiation in the hyperconverged market for this inferencing solution, including the following advantages:

  • A disaggregated architecture allows independent scaling of compute and storage and lowers the virtualization licensing costs and performance tax on independent NetApp HCI storage nodes.

  • NetApp Element storage provides quality of service (QoS) for each storage volume, which provides guaranteed storage performance for workloads on NetApp HCI. Therefore, adjacent workloads do not negatively affect inferencing performance.

  • A data fabric powered by NetApp allows data to be replicated from core to edge to cloud data centers, which moves data closer to where application needs it.

  • With a data fabric powered by NetApp and NetApp FlexCache software, AI deep learning models trained on NetApp ONTAP AI can be accessed from NetApp HCI without having to export the model.

  • NetApp HCI can host inference servers on the same infrastructure concurrently with multiple workloads, either virtual-machine (VM) or container-based, without performance degradation.

  • NetApp HCI is certified as NVIDIA GPU Cloud (NGC) ready for NVIDIA AI containerized applications.

  • NGC-ready means that the stack is validated by NVIDIA, is purpose built for AI, and enterprise support is available through NGC Support Services.

  • With its extensive AI portfolio, NetApp can support the entire spectrum of AI use cases from edge to core to cloud, including ONTAP AI for training and inferencing, Cloud Volumes Service and Azure NetApp Files for training in the cloud, and inferencing on the edge with NetApp HCI.