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This technique can increase [[fault tolerance]] by enabling quick substitutions for the most complicated, most failure-prone parts of a system. However, it can make the load balancer itself a [[single point of failure]].
=== Data ingestion for AI
Increasingly, load balancing techniques are being used to manage high-volume data ingestion pipelines that feed [[artificial intelligence]] [[AI training|training]] and [[inference]] systems—sometimes referred to as “[[AI Factory|AI factories]].” These AI-driven environments require continuous processing of vast amounts of structured and unstructured data, placing heavy demands on networking, storage, and computational resources.<ref>{{Cite web |title=Optimize Traffic Management for AI Factory Data Ingest |url=https://www.f5.com/company/blog/ai-factory-traffic-management-data-ingest |access-date=2025-01-30 |website=F5, Inc. |language=en-US}}</ref> To maintain the necessary high throughput and low latency, organizations commonly deploy load balancing tools capable of advanced TCP optimizations, connection pooling, and adaptive scheduling. Such features help distribute incoming data requests evenly across servers or nodes, prevent congestion, and ensure that compute resources remain efficiently utilized.<ref>{{Cite web |title=Optimize, Scale, and Secure AI Interactions |url=https://www.f5.com/solutions/use-cases/optimize-scale-and-secure-ai |access-date=2025-01-30 |website=F5, Inc. |language=en-US}}</ref>
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