The correct answer is B. The customer is not asking for the lowest-cost AI inference server; the scenario explicitly states that RAG performance is the primary decision factor, while also requiring limited rack space and air cooling. HPE ProLiant Compute DL384 Gen12 with NVIDIA GH200 NVL2 is the strongest fit because it is designed for high-performance enterprise AI and provides coherent CPU/GPU memory architecture with very high memory bandwidth, which is especially valuable for RAG and larger language-model workflows. HPE product information describes the DL384 Gen12 as a 2U air-cooled 19-inch rack server with NVIDIA GH200 NVL2, and HPE states that the platform provides outstanding coherent memory and memory bandwidth to run larger models faster and more cost-effectively. Option A is powerful, but the DL380a Gen12 is a larger GPU platform and is less optimized for the “limited rack space” RAG-specific requirement than the DL384. Option C is compact but lower-performance, making it weaker when performance is the primary factor. Option D is excessive for an early-stage on-prem POC. Reference/topics: AI workload sizing, RAG architecture, DL384 Gen12, GH200 NVL2, air-cooled AI infrastructure.