Case Study: LG AI Research achieves faster, scalable K-EXAONE deployment with FriendliAI

LG AI Research triples traffic and cuts deployment time by weeks with FriendliAI

LG AI Research, a leading AI research organization, faced significant challenges when moving their large-scale K-EXAONE foundation model into production. They struggled with high GPU costs, memory bottlenecks from long-context workloads, and difficulty scaling inference reliably. To overcome this, they chose the FriendliAI production inference platform to handle these demands without diverting internal resources from model innovation.

By deploying K-EXAONE on FriendliAI's Dedicated Endpoints and Model APIs, LG AI Research achieved a successful production deployment. The solution provided efficient resource utilization, elastic scaling, and dramatically faster deployment. The results included a threefold increase in traffic, a reduction in deployment time by several weeks, and predictable costs as usage grew. FriendliAI's platform enabled the research team to focus on development while reliably scaling their model.

LG AI Research