About this Document

This document describes the design requirements, architecture, implementation approach, and validation methodology for an AI inference frontend fabric built with HPE Juniper Networks QFX switches, HPE Juniper Apstra Data Center Director, and AMD Instinct™ MI300X GPU systems. This JVD also introduces the newest QFX5140-24CD8O switch as a key frontend leaf node for production of AI inference deployments.

All validation tests were conducted in Juniper’s AI Innovation Lab in Sunnyvale, CA, USA, where Juniper collaborates closely with customers and technology partners to develop AI solutions and test deployments for a range of AI applications, infrastructure architectures, and models.

Modern AI inference environments require predictable latency, scalable throughput, and efficient resource utilization to support high query volumes and maintain a consistent user experience. As inference deployments transition from experimentation to production, the frontend network becomes increasingly important in enabling reliable communication between inference clients, load balancing services, and GPU-accelerated compute infrastructure. The AI Inference Network Design with HPE Juniper Networks QFX switches, and AMD Instinct™ MI300X GPUs demonstrates how a standards-based Ethernet frontend fabric can efficiently support AI inference workloads while maintaining predictable performance characteristics.

The solution provides a benchmark-focused reference architecture for AI inference environments and demonstrates how modern Ethernet-based frontend networks can support production inference deployments. Through inference performance benchmarking and frontend network characterization, the solution offers practical guidance on infrastructure design, software frameworks, operational considerations, and validated deployment approaches for AI inference workloads.