arXiv:2601.22709v5 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) achieve strong multimodal performance but are costly to deploy, and post-training quantization often causes significant accuracy loss. Despite its potential, quantization-aware training for VLMs remains underexplored. We propose GRACE, a framework unifying knowledge distillation and QAT under the Information Bottleneck principle: quantization constrains information capacity while distillation guides what to preserve within this budget. Treating the teacher as a proxy for task-relevant information, we introd
The increasing deployment of Vision-Language Models (VLMs) highlights the urgent need for efficient, hardware-friendly architectures, addressing a core limitation as AI scales.
This development addresses a critical bottleneck in VLM deployment, enabling broader adoption and reducing computational costs, making advanced AI more accessible and sustainable.
The proposed GRACE framework significantly increases the efficiency of VLMs without substantial accuracy loss, allowing for more performant models on constrained hardware.
- · AI hardware manufacturers (edge devices)
- · VLM developers
- · Cloud computing providers (reduced inference cost)
- · Industries deploying VLMs (e.g., robotics, autonomous vehicles)
- · Companies reliant on inefficient, high-compute VLM architectures
- · Hardware providers not adapting to efficient AI demands
More efficient and compact VLM deployments become feasible across various applications.
The reduced computational overhead could accelerate the integration of VLMs into edge devices and real-time systems, democratizing advanced AI capabilities.
Increased accessibility and lower operational costs of VLMs might foster new application paradigms and business models that were previously cost-prohibitive.
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Read at arXiv cs.AI