RAGMark: A Comprehensive Framework for Benchmarking Retrieval-Augmented Generation Systems
We present RAGMark, a modular benchmarking framework for advanced Retrieval-Augmented Generation (RAG) systems targeting small-scale multi-GPU environments. RAGMark evaluates diverse RAG components, including retrievers, vector databases, prompt-processing methods, and generator models, while collecting detailed per-stage metrics such as latency, GPU utilization, memory consumption, power usage, time to first token (TTFT), throughput, and answer quality. The framework is highly extensible, separating RAG stages, timing, and resource monitoring into modular components, and is designed to efficiently sweep large configuration spaces while minimizing repeated model and database initializatio...
| Authors | Zlatan Feric, Amir Taherin, Bin Ren et al. |
|---|---|
| Primary category | cs.PF |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
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