Rethinking Small VLM Quantization: From Component-Wise Analysis to Hardware-Aware Edge Deployment
arXiv:2607.08029v1 Announce Type: new Abstract: The emergence of vision language models with fewer than 3 billion parameters has accelerated the implementation of on-device multimodal intelligence. However, a detailed understanding of component-wise quantization remains a bottleneck for optimal deployment. This paper presents a systematic evaluation framework for empirically validating five hypotheses across six quantization configurations on the Jetson Orin NX and AGX. By separating the vision encoder, projector, and large language model backbone yields the following results: (1) Quantization
The proliferation of smaller, on-device vision language models (VLMs) necessitates optimizing their efficiency for real-world applications, making quantization a critical and timely area of research.
Achieving optimal quantization for small VLMs is crucial for deploying advanced AI capabilities on edge devices, unlocking new applications and reducing reliance on cloud infrastructure.
The improved understanding and systematic evaluation framework for VLM quantization will accelerate the development and deployment of efficient multimodal AI at the edge, moving beyond generic quantization approaches.
- · Edge AI device manufacturers
- · On-device AI application developers
- · Specialized VLM developers
- · Hardware-aware AI optimization firms
More sophisticated and performant multimodal AI models will operate effectively on resource-constrained edge hardware.
Increased adoption of rich AI-powered features in consumer electronics, automotive, and industrial IoT sectors due to lower latency and improved privacy.
Potential for new business models and services that are exclusively enabled by fully on-device, high-performance multimodal AI, reducing data transmission costs and bandwidth needs.
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Read at arXiv cs.LG