MGVQ: Synergizing Multi-dimensional Sensitivity-Aware and Gradient-Hessian Fusion for Vector Quantization
arXiv:2605.24019v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) achieve outstanding performance, yet their huge model size severely hinders deployment on edge devices with limited resources. As an efficient model compression technique, vector quantization (VQ) excels in ultra-low-bit representation, which maps model weights to discrete codewords in a compact codebook to cut memory consumption and transmission overhead while preserving model capability. Direct VQ application to VLMs still has two core limitations. First, cross-modality weight distribution differences brought by
The proliferation of complex Vision-Language Models creates an urgent need for efficient deployment, making model compression techniques like vector quantization highly relevant right now.
This research addresses a critical bottleneck for wider VLM adoption, enabling their deployment on resource-constrained edge devices and expanding their applications beyond large data centers.
The ability to significantly compress VLMs without severe performance degradation changes the landscape for edge AI, potentially democratizing access to advanced AI capabilities.
- · Edge device manufacturers
- · AI developers targeting mobile and IoT
- · On-device AI applications
- · Machine learning researchers
- · Cloud-dependent AI service providers (in some use cases)
More powerful AI models can run directly on consumer devices, reducing latency and increasing privacy.
Accelerated development of localized AI applications across various industries due to reduced compute demands.
Increased competition for device-side AI model optimization, potentially leading to new hardware-software co-design innovations.
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Read at arXiv cs.LG