Edge-Native Multimodal RAG: Hardware-Aware Quantization and Architectural Optimization for Consumer GPUs

A multimodal retrieval-augmented generation framework built on NormalFloat 4-bit (NF4) quantization and a decoupled Florence-2 vision integration runs in an 8.37 GB footprint on consumer Turing GPUs such as the NVIDIA Tesla T4, according to an arXiv paper (2611.14924v1). The authors report NF4 sustains long-context performance at 81 ms/token, while GPTQ reaches 51.6 tok/s but shows context instability beyond 2048 tokens on Turing hardware, and that structured prompting cuts latency 47% without changing computational intensity of 1.88 GFLOPs/token. Structured few-shot prompting restored mathematical accuracy from 35.0% to 92.0% across 200 trials, compensating for quantization-induced reasoning loss, with visual reasoning adding only 430 MB of VRAM overhead.

arXiv:2611.14924v1 Announce Type: new Abstract: Deployment of retrieval-augmented generation RAG for Edge-AI is constrained by memory and bandwidth limitations. A standard 7B parameter model consumes 14 GB Video Random Access Memory VRAM in half-precision FP16 , precluding co-location of vector indices and Key-Value KV caches. This paper presents an engineering integration and empirical evaluation of a multimodal RAG framework optimized for consumer-grade Turing hardware e.g., NVIDIA Tesla T4 . Our system-level analysis identifies a critical trade-off: while generative pre-trained transformer quantization GPTQ offers superior throughput 51.6 tok/s , it exhibits significant context instability at 2048+ tokens on Turing architectures. Conversely, NormalFloat 4-bit NF4 maintains robust long-context performance with viable latency 81 ms/tok . By coupling NF4 with a decoupled Florence-2 vision integration, we achieve visual reasoning with only 430 MB VRAM overhead. Furthermore, empirical observations of prompt-induced search space pruning show that structured prompting reduces latency by 47% without altering computational intensity 1.88 GFLOPs/token . The integrated architecture maintains an 8.37 GB footprint, establishing a viable RAG pipeline for 12-16 GB GPUs. Finally, structured few-shot prompting effectively compensates for quantization-induced reasoning loss, restoring mathematical accuracy from 35.0% to 92.0% across 200 trials.