Common challenges serving LLMs on Kubernetes

Taneem Ibrahim and Yuan Tang describe some of the common challenges serving LLMs ranging from 2B to 405B parameters on Kubernetes. Sharing of computational resources across multiple LoRA adapters, shortening model loading time, and providing a more efficient way to fetch models from an OCI image registry are a sample of challenges that are being addressed in upstream open source Kubernetes, Kserve and vLLM working groups.

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