Qdrant and Minima Deliver 2.92x More Agentic RAG Tasks per GPU-Hour

Qdrant and Minima report that combining hybrid, reranked retrieval with compressed LLM inference increased agentic RAG performance to 3,158 successful tasks per GPU-hour, or 2.92 times the baseline dense-retrieval BF16 setup, while reducing median task latency from 21.3 to 7.7 seconds and maintaining similar grounded-answer and citation quality. Across 1,800 benchmark tasks from SciFact, FiQA, HotpotQA, and a payload-filtering dataset, Qdrant’s dense-plus-BM25 retrieval, reciprocal-rank fusion, late-interaction reranking, and payload filters made first-pass evidence sufficient in 87% of tasks, reduced retrieved context by 56%, and recorded no tenant-policy violations in 50,000 adversarial filtering queries. Minima ran Qwen3.6-27B on one NVIDIA RTX PRO 6000 Blackwell GPU using NVFP4 weights, reduced-precision KV-cache tiers, and native kernels, shrinking model-weight memory from 54.0 GB to 16.9 GB and nearly doubling standalone inference throughput. The reported GPU-only cost per 1,000 successful tasks declined from $1.39 to $0.48, although the comparison excluded Qdrant hosting and embedding-service costs.

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