Edge-Native Multimodal RAG: Hardware-Aware Quantization and Architectural Optimization for Consumer GPUs
Summary
- Researchers present an edge-native multimodal RAG framework optimized for consumer-grade Turing hardware like the NVIDIA Tesla T4.
- While GPTQ offers higher throughput at 51.6 tok/s, NF4 quantization maintains robust long-context performance at 81 ms/tok.
- Coupling NF4 with decoupled Florence-2 vision integration adds only 430 MB of VRAM overhead.
- Structured prompting reduces latency by 47% and restores quantization-induced mathematical accuracy loss from 35.0% to 92.0%.
Why it matters
This research provides practical deployment pathways for multimodal RAG pipelines on 12-16 GB consumer GPUs by resolving critical memory, bandwidth, and quantization trade-offs.