Model Releases
Qdrant and Minima Deliver 2.92x More Agentic RAG Tasks per GPU-Hour
Reducing Retrieval and Calls When a retrieval-augmented generation (RAG) agent runs, it often has to plan a search, check the evidence it gets back, and try again when that evidence falls short. Those
Reducing Retrieval and Calls When a retrieval-augmented generation (RAG) agent runs, it often has to plan a search, check the evidence it gets back, and try again when that evidence falls short. Those inefficiencies compound. Every extra retrieval and every extra model call adds latency, context, and inference cost.
Related
- LayerRAG-Bench: A Cross-Layer Reliability Benchmark for Agentic Retrieval-Augmented Generation
- Qdrant Beats Elastic’s DiskBBQ at 2x Throughput, Half the Latency, and 1/3 the Compute
- HiPRAG: Hierarchical Process Rewards for Efficient Agentic Retrieval Augmented Generation
Source: Qdrant | 2026-08-13