Editing Load balancing (computing) (section)

=== Data Ingestion for AI Model Training ===
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>

When deployed in large-scale or high-performance AI environments, load balancers also mitigate bandwidth constraints and accommodate varying data governance requirements—particularly when sensitive training data cannot be sent to third-party cloud services. By routing data locally (on-premises) or across private clouds, load balancers allow AI workflows to avoid public-cloud bandwidth limits, reduce transit costs, and maintain compliance with regulatory standards. As AI models expand in size (often measured by billions or even trillions of parameters), load balancing for data ingestion has grown in importance for maintaining the reliability, scalability, and cost efficiency of AI factories.