Running multiple specialized AI models in production typically means one GPU per model, leaving most hardware idle and wasting money. This post explains why standard serving tools like vLLM and TEI fail at GPU sharing, then introduces the open-source Superlinked Inference Engine (SIE) as a solution. SIE runs a unified cluster that loads models on demand, evicts idle ones, uses a shared request queue, and batches by compute cost rather than request count. A hands-on flood insurance claim pipeline demonstrates five different models (Docling, GLiNER, BGE-reranker, Grounding DINO, Qwen) running through three unified API primitives (extract, score, generate) on shared GPU infrastructure, with code examples for each stage.
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