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AIDemo offlineInferenceLoad BalancingGPUStreaming

F5 AI Delivery

High-performance traffic management for inference workloads — GPU-aware load balancing and streaming.

Overview

Inference traffic behaves nothing like standard web traffic: requests are long-lived, responses stream token by token, and backend cost is dominated by GPU utilisation. This demonstration shows how F5 delivers inference workloads with awareness of those characteristics, including queue-depth-based load balancing and correct handling of streamed responses. You will compare naive round-robin against GPU-aware steering and show the resulting difference in time-to-first-token under load. A strong technical demo for infrastructure teams building an internal AI platform.

Architecture

Laptop

Mobile

F5 Application Delivery & Security Platform

WAF · API Security · Bot Defense · Load Balancing

Data Centre

Public Cloud

Kubernetes

Generic illustration — a specific architecture diagram has not been uploaded for this demo yet.

How to access it

Online Demo

Access— this demo is currently offline

Credentials

Username
se-demo
Password
••••••••••••••••••

Lab URL

Access

Lab Guide URL

Access
Environment
F5 UDF — blueprint "AI Delivery + 3x vLLM nodes"
Setup time
~20 minutes

Maintainer

Fouad Chmainy— chat on Microsoft Teams

Maintainer notes

Use the supplied load-generation script to create realistic queue pressure. The difference between steering modes is not visible at low concurrency.

How to learn it

Highspot

Positioning, battlecards and customer-facing collateral.

SharePoint

Demo scripts, lab guides and configuration files.

Video walkthroughs

Recorded demonstrations on @MatthieuDierickF5. No dedicated recording is linked for this demo yet.

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