arXiv:2605.26415v1 Announce Type: cross Abstract: Deploying Vision-Language Models on resource-constrained hardware typically requires INT8 quantization, but in joint-embedding architectures such as CLIP this introduces a failure mode distinct from quantized CNN classifiers: activation noise accumulated across transformer blocks perturbs the direction of the multimodal embedding, eroding the cosine alignment on which zero-shot retrieval depends. We characterize this as Quantization-Induced Representation Collapse (QIRC) and quantify it on INT8 CLIP ViT-B/32, where the layer-wise noise-to-signa
The increasing push to deploy large AI models on edge and resource-constrained devices makes efficient quantization a critical and immediate problem.
This research addresses a fundamental challenge in deploying powerful vision-language models like CLIP on ubiquitous, lower-power hardware, crucial for broader AI adoption.
New methodologies for mitigating quantization collapse in joint-embedding models could enable more widespread and performant on-device AI applications.
- · Edge AI hardware manufacturers
- · Developers of on-device AI applications
- · Users of AI-powered mobile and IoT devices
- · Cloud AI service providers (potentially, as more processing moves to edge)
- · Companies relying solely on high-compute AI solutions
Improved efficiency and performance of AI models on resource-constrained devices.
Accelerated development and adoption of AI applications in areas where cloud connectivity or high power consumption are limiting factors.
Increased decentralization of AI inference, potentially impacting data privacy and sovereignty paradigms as more processing occurs locally.
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Read at arXiv cs.AI