This article details a practical MLOps architecture for serving multiple Large Language Model (LLM) workloads efficiently from a single graphics processing unit (GPU). It outlines a system that leverages vLLM, demand-driven LoRA adapters, ClickHouse, Azure ML, and Azure Blob Storage to create a production-ready ML platform without the need to double hardware resources. AI
IMPACT Optimizes GPU utilization for LLM serving, potentially reducing infrastructure costs for AI deployments.
RANK_REASON Describes a technical implementation for optimizing existing hardware for LLM workloads, rather than a new model release or core research.
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